diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
deleted file mode 100644
index 48517a0..0000000
--- a/.github/workflows/test.yml
+++ /dev/null
@@ -1,24 +0,0 @@
-name: tests
-
-on:
-  push:
-  pull_request:
-
-jobs:
-  model-free:
-    runs-on: ubuntu-latest
-    defaults:
-      run:
-        working-directory: recognization
-    steps:
-      - uses: actions/checkout@v4
-      - uses: actions/setup-python@v5
-        with:
-          python-version: "3.12"
-      - name: Install pinned dependencies
-        run: python -m pip install -r requirements.lock
-      - name: Model-free suite
-        env:
-          YOLO_OFFLINE: "1"
-          YOLO_AUTOINSTALL: "false"
-        run: python -m unittest discover -s code -p 'test_*.py' -v
diff --git a/recognization/.gitignore b/recognization/.gitignore
index 20e714e..789e4d1 100644
--- a/recognization/.gitignore
+++ b/recognization/.gitignore
@@ -19,23 +19,4 @@ ENV/
 
 # Temporary weights
 runs/detect/train/weights/last.pt
-weights/dinov2_vits14_lvd142m.safetensors
 *.log
-stock-*/identity_head.pt
-.gallery_cache_*.npz
-.demo-token
-config/local.env
-
-# Fetched images. Every set keeps its manifest.json (source URL, licence,
-# artist) and its results in git; the pixels are re-fetchable from those URLs
-# and are 59 MB for the Wikimedia shelves alone.
-data/external-shelves/*/*.jpg
-data/external-shelves/*/*.jpeg
-data/external-shelves/*/*.JPG
-data/external-shelves/*/*.png
-data/packshots/research/*/images/
-data/packshots/research/*/pages/
-data/packshots/research/*/*.jpg
-
-# Rebuildable sweep cache (code/sweep_accept_lines.py --cache).
-*.pkl
diff --git a/recognization/DEMO.md b/recognization/DEMO.md
deleted file mode 100644
index f018f86..0000000
--- a/recognization/DEMO.md
+++ /dev/null
@@ -1,81 +0,0 @@
-# Running the demo
-
-Two surfaces: the **web client** (open it in a browser) and the **iOS/Android app**
-(scan screen). Both are clients of the same recognition server.
-
-## 1. Start the server
-
-```bash
-cd recognization
-YOLO_OFFLINE=1 .venv/bin/python code/app.py --port 8080 --classify-workers 8
-```
-
-Ready when `curl -s http://127.0.0.1:8080/v1/health` answers (about 25 s: the model
-and the catalog load, then the OCR engine reports itself).
-
-Flags that matter for a demo:
-
-| Flag | Why |
-| --- | --- |
-| `--classify-workers 8` | More parallel classification; the demo's bottleneck. |
-| `--host 0.0.0.0` | Only if a phone or iPad on the LAN must reach it (set `DORAYAKI_API_TOKEN` too). |
-| `--no-tags` | Disables price-tag OCR entirely if it is fighting for CPU. |
-| `DORAYAKI_OCR=none` | Same, via environment. |
-
-## 2. The web client
-
-Open <http://127.0.0.1:8080>.
-
-- **Live** — pick a camera (or “Video file…”), press Start. Boxes appear as
-  products are recognised, with name and price; teal boxes mark the shelf-edge
-  price tags; amber means “check this”; red marks empty shelf space.
-- **Photo** — a still shelf image: boxes paint immediately, identities fill in,
-  the algorithm selector re-classifies, and tapping a product opens the card with
-  the catalog photo, stock, and the tag reading compared against the catalog.
-
-## 3. The app (iOS simulator, no camera needed)
-
-The app can drive a **synthetic store walk**: the server renders a virtual camera
-sweeping a real shelf photo — pan a row left to right, step up, pan back — so the
-scan screen behaves like an employee walking the shelf.
-
-Start the server on the port the app expects (8090 is the Debug default; 8097 is
-what the verification scripts used — pass the override if you change it):
-
-```bash
-YOLO_OFFLINE=1 .venv/bin/python code/app.py --port 8090 --host 127.0.0.1 --classify-workers 8
-```
-
-Then run the app in the simulator and open **Scan**. Press the scan control: the
-stage shows the walk moving, green boxes with names appear, teal tag boxes carry
-prices, and tapping a product opens the card.
-
-On a real device, replace the walk with the camera: the app uses the device camera
-and the same server (a LAN IP needs HTTPS or an ATS exception — see
-`docs/API.md`).
-
-## 4. The demo configuration (measured)
-
-Everything below was measured on this machine, 2026-09-24, one client, 1280×720
-frames, the synthetic walk:
-
-| Setting | Result |
-| --- | --- |
-| live method `orb` | **7.2 results/s, 178 labelled tracks, 21 distinct SKUs, first label 0.7 s** |
-| live method `hybrid` | 1.4–9.9/s depending on load, 4–13 tracks, first label 3–5 s |
-| walk `pan=0.02` | a row sweeps in ~8 s — reads like a person |
-| walk `pan=0.06` | ~3 s per row — a time-lapse |
-| OCR cadence (live) | 1 rail band every 30 frames; more than that drops the frame rate |
-
-So: **ORB for live labelling, Hybrid for “Show matching”** (tap-to-inspect), and
-the slower walk pace. The method menu in both clients switches between them live.
-
-## 5. What to say, honestly
-
-Working today: live detection and tracking, product identity with names and
-prices, price-tag reading and the tag-vs-catalog comparison, empty-shelf gaps,
-tap-to-inspect with the catalog photo and stock, and the walk.
-
-Not built yet: configurable shelf rules (facing counts, brand blocks), the
-fix → rescan → verify loop, and a head-office dashboard. Do not claim those on
-camera.
diff --git a/recognization/README.md b/recognization/README.md
index 2a2be37..c17c809 100644
--- a/recognization/README.md
+++ b/recognization/README.md
@@ -1,208 +1,99 @@
 # 🍡 Dorayaki - Smart Supermarket Shelf AI & Inventory Recognition
 
-Hệ thống nhận diện sản phẩm trên kệ siêu thị và quản lý tồn kho theo kiến trúc **client - server**: mô hình thị giác máy tính (YOLO + Local Feature Matching: SURF/SIFT, ORB, BRISK, Hybrid theo paper *Electronics 2023, 12, 3640*) chạy trong một **recognition server**, còn các client (web, thiết bị chụp ảnh) chỉ gửi khung hình và nhận kết quả.
-
-Server phục vụ **2 chế độ**:
-
-1. **Live (nhận diện thời gian thực)** - camera hoặc video gửi liên tục qua WebSocket, sản phẩm được phát hiện trên từng khung hình, theo dõi (tracking) xuyên khung hình, phân loại một lần cho mỗi sản phẩm và gắn nhãn ngay trên màn hình.
-2. **Photo (ảnh tĩnh)** - chụp/upload/chọn ảnh kệ, nhận diện toàn bộ kệ trong một lần, bấm vào từng sản phẩm để xem chi tiết, tồn kho và điểm matching.
+Hệ thống nhận diện sản phẩm trên kệ siêu thị và quản lý tồn kho thời gian thực dựa trên kiến trúc kết hợp **YOLO** (Object Detection) và **Local Feature Matching** (SURF / SIFT, ORB, BRISK, Hybrid) theo paper quốc tế (*Electronics 2023, 12, 3640*).
 
 ---
 
 ## 📋 Yêu cầu hệ thống (System Requirements)
-
-- **Hệ điều hành:** Windows 10/11, macOS hoặc Linux.
-- **Python:** 3.10 - 3.12 (khuyên dùng 3.11/3.12).
-- **Phần cứng:** CPU 4 lõi trở lên, RAM 8GB. GPU không bắt buộc: trên máy M5 đo được CPU nhanh hơn MPS (28ms so với 112ms mỗi khung hình).
+- **Hệ điều hành:** Windows 10/11, macOS hoặc Linux (Ubuntu/Debian).
+- **Python:** Python 3.9, 3.10 hoặc 3.11 (khuyên dùng Python 3.10).
+- **Phần cứng:** CPU tối thiểu 4 lõi, RAM 8GB (có GPU NVIDIA là lợi thế nhưng CPU chạy hoàn toàn mượt mà).
 
 ---
 
-## 🚀 Cài đặt và chạy
+## 🚀 Hướng dẫn cài đặt và chạy Step-by-Step
 
-### Bước 1: Chuẩn bị thư mục dự án
+### Bước 1: Sao chép toàn bộ thư mục dự án sang máy mới
+Đảm bảo bạn copy toàn bộ thư mục dự án, đặc biệt là các thư mục cốt lõi:
+- code/: Toàn bộ mã nguồn backend, frontend, thuật toán nhận diện.
+- 
+uns/: Chứa file trọng số mô hình YOLO đã train (
+uns/detect/train/weights/best.pt).
+- stock/: Chứa file catalog stock.csv và ảnh mẫu sản phẩm chuẩn sku_001 -> sku_016.
+- dd/ & image.jpg: Các ảnh kệ hàng mẫu để test và demo.
+- 
+equirements.txt: Danh sách thư viện cần thiết.
 
-Cần có đầy đủ:
+### Bước 2: Tạo môi trường ảo Python (Khuyên dùng)
+Mở Terminal / PowerShell / Command Prompt tại thư mục dự án:
 
-- `code/` - mã nguồn server, client web, client mẫu.
-- `runs/` - trọng số YOLO đã train (`runs/detect/train/weights/best.pt`).
-- `weights/dinov2_vits14_lvd142m.safetensors` - DINOv2 backbone for the identity gallery. Not in Git. See "DINOv2 weights" below.
-- `stock/` - catalog `stock.csv` và ảnh mẫu sản phẩm `sku_001` -> `sku_022`.
-- `add/` và `image.jpg` - ảnh kệ mẫu để test/demo.
-- `requirements.txt`, `requirements-dev.txt`.
-
-### Bước 2: Tạo môi trường ảo
-
-```bash
-conda create -n dorayaki python=3.11 -y
+#### Cách 1: Sử dụng Conda (Khuyên dùng nếu đã cài Anaconda/Miniconda)
+`ash
+conda create -n dorayaki python=3.10 -y
 conda activate dorayaki
-```
+`
 
-hoặc venv:
+#### Cách 2: Sử dụng venv chuẩn của Python
+- **Trên Windows:**
+`ash
+python -m venv venv
+venv\Scripts\activate
+`
+- **Trên macOS / Linux:**
+`ash
+python3 -m venv venv
+source venv/bin/activate
+`
 
-```bash
-python3 -m venv .venv
-source .venv/bin/activate        # Windows: .venv\Scripts\activate
-```
+### Bước 3: Cài đặt các thư viện phụ thuộc
+Chạy lệnh sau để cài đặt toàn bộ package:
+`ash
+pip install -r requirements.txt
+`
 
-### Bước 3: Cài đặt thư viện
+*(Ghi chú nếu máy có GPU NVIDIA và muốn tăng tốc độ YOLO, có thể cài PyTorch CUDA theo hướng dẫn tại pytorch.org).*
 
-```bash
-pip install -r requirements-dev.txt
-```
+### Bước 4: Khởi chạy ứng dụng Dorayaki Web App
 
-### DINOv2 weights
-
-The identity gallery embeds crops with DINOv2 ViT-S/14 (LVD-142M), Apache-2.0. It loads only a local file and does not contact Hugging Face, including when `YOLO_OFFLINE` is unset. Without the file, gallery identity fails and the server log says the weights are missing.
-
-The pinned file is `model.safetensors` from `timm/vit_small_patch14_dinov2.lvd142m`, sha256 `04d27f3400d059fc0cfd7d17dd1909a75bf3ea8fb3eeb48b97cb99e57ee20081` (~84 MB). Copy a cache you already have; do not fetch one from an unknown URL:
-
-```bash
-mkdir -p weights
-cp "$HOME/.cache/huggingface/hub/models--timm--vit_small_patch14_dinov2.lvd142m/snapshots/"*/model.safetensors \
-  weights/dinov2_vits14_lvd142m.safetensors
-```
-
-`DORAYAKI_DINOV2_WEIGHTS` overrides that path. A file with any other sha256 is refused.
-
-With the gallery identity backend, enrolled views are embedded into an ignored
-`stock-dir/.gallery_cache_<image_size>.npz` feature cache. The cache is reused
-only when its key matches the preprocessing version, image size, pinned DINOv2
-SHA-256, catalog CSV, enrolled SKU/view order and image bytes; its matrix shape,
-numeric finite values and row identities are also checked. A stale or malformed
-cache is rebuilt. A cache hit still checks the local model file's SHA-256, and
-gallery startup refuses readiness if the pinned weights or usable gallery are
-missing. OCR continues on the separate tag worker (`tag_executor`).
-
-(`requirements-dev.txt` = `requirements.txt` + `httpx` cho test.)
-
-### Bước 4: Khởi chạy recognition server
-
-```bash
+#### Cách 1: Chạy bằng lệnh Python
+`ash
 python code/app.py --port 8080
-```
+`
+*(Nếu dùng macOS/Linux có thể dùng python3 code/app.py --port 8080)*
 
-Script có sẵn: `run_app.bat` (Windows) hoặc `./run_app.sh` (Linux/macOS).
+#### Cách 2: Chạy nhanh bằng script có sẵn
+- **Trên Windows:** Click đúp vào file 
+un_app.bat
+- **Trên Linux/macOS:** Chạy ./run_app.sh
 
-Các tham số thường dùng:
-
-| Tham số | Mặc định | Ý nghĩa |
-| --- | --- | --- |
-| `--host` | `127.0.0.1` | Đổi thành `0.0.0.0` để mở cho máy khác trong LAN (nên đặt `DORAYAKI_API_TOKEN`). |
-| `--device` | `cpu` | `cpu`, `mps`, `cuda`, `cuda:0`. |
-| `--imgsz` | `640` | Kích thước đầu vào của detector. |
-| `--live-method` | `hybrid` | Thuật toán mặc định cho chế độ live. |
-| `--max-sessions` | `4` | Số client live đồng thời. |
-| `--ssl-certfile` / `--ssl-keyfile` | - | Bật HTTPS (cần cho camera trên điện thoại). |
-
-### Bước 5: Mở ứng dụng
-
-Mở trình duyệt: **http://127.0.0.1:8080** (hoặc `http://127.0.0.1:8080`)
-
-- Tab **Live**: chọn camera (hoặc "Video file…") rồi bấm **Start**. Sản phẩm hiện khung xanh kèm tên và giá khi đã nhận diện chắc chắn; khung vàng "要確認" là trường hợp chưa chắc chắn.
-- Tab **Photo**: chọn ảnh mẫu, chụp ảnh hoặc upload ảnh kệ. Nhấn vào sản phẩm để xem chi tiết và bấm **Show matching** để xem các điểm matching.
-
-Camera trên điện thoại chỉ hoạt động khi trang mở bằng **https** hoặc **localhost** (yêu cầu bảo mật của trình duyệt).
+### Bước 5: Mở ứng dụng trên trình duyệt
+Mở trình duyệt (Chrome, Edge, Safari, Firefox) và truy cập:
+👉 **http://localhost:8080** (hoặc **http://127.0.0.1:8080**)
 
 ---
 
-## 🔌 API (dùng cho client khác)
+## 🛠️ Các lệnh kiểm tra hệ thống (Test Scripts)
 
-Server có REST + WebSocket đầy đủ, tài liệu chi tiết trong [`docs/API.md`](docs/API.md). Swagger UI: **http://127.0.0.1:8080/docs**.
-
-Live WebSocket protocol `1` also supports an optional full-resolution crop lane:
-clients advertise `capabilities`, then answer per-track `crop_request` messages
-from their buffered capture. Clients without the capability are unchanged; wire
-examples, byte limits, expiry and errors are in [`docs/API.md`](docs/API.md#websocket-v1stream).
-
-```bash
-# Nhận diện một ảnh kệ
-curl -X POST --data-binary @image.jpg -H 'Content-Type: image/jpeg' \
-  'http://127.0.0.1:8080/v1/recognize?classify=true'
-
-# Client live mẫu (camera, video, hoặc pan qua ảnh tĩnh)
-python code/stream_client.py --source image.jpg --pan 12 --seconds 20
-python code/stream_client.py --source 0                 # webcam
-```
-
-Bất kỳ client nào gửi được ảnh JPEG đều dùng được: điện thoại, iPad, camera gắn xe đẩy, camera đeo cổ, robot. Giao thức không phụ thuộc trình duyệt.
-
-For the native app demo, `./run-demo-server.sh` uses `stock-lopia-clean` by
-default: `stock-lopia` is shelf crops cut from the `image6` evaluation photo, so
-scoring against it is memorization. Pass another stock directory explicitly to
-change stores; the script writes ignored local app endpoints and a local token.
-
-### Identity backends
-
-`--identity` picks what names a product. The default is `local`:
-
-- `local` — RootSIFT over a per-SKU reference pyramid, similarity-constrained
-  RANSAC, a hue check, then a text lane (OCR of the crop against each catalog
-  row's name plus its packshot OCR) that vetoes a match whose package text names
-  a different row and answers on its own when geometry finds nothing. Measured
-  on the corrected `image6` labels at live resolution: 59% of in-catalog facings
-  named at 95% precision, 0 of 48 unknowns accepted. Rows with no enrolled image
-  are still answerable, because the text lane builds their signature from the
-  catalog name.
-- `matcher` — Van's original SURF/ORB/BRISK composite, kept as the E-1 baseline
-  lane for comparison. Always answers, so it accepts near-unknown siblings.
-- `gallery` — the DINOv2 embedding gallery. Fenced off: it is not the default
-  and it refuses to start without the pinned weights. Its recall collapses as
-  the catalog grows (7/42 recall@10 at 493 SKUs), so it is not the recognition
-  path.
-
-The text lane needs OCR. On macOS that is Vision, which is already wired in
-`code/ocr.py`; `DORAYAKI_OCR=none` disables it and the local lane then stands
-alone (43% coverage at 79% precision on the same labels).
+1. **Test toàn diện hệ thống (Test Suite):**
+`ash
+python code/test_suite.py
+`
+2. **Test nhận diện sản phẩm trên ảnh image.jpg:**
+`ash
+python code/test_image.py
+`
+3. **Test nhận diện các chỗ trống trên kệ (Empty Shelf Slots):**
+`ash
+python code/test_empty.py
+`
+*(Kết quả trực quan sẽ được xuất ra file detected_empty_slots.jpg với viền đỏ bao quanh các vị trí hết hàng).*
 
 ---
 
-
-## 🛠️ Kiểm tra hệ thống (Tests)
-
-From `recognization/`, after `pip install -r requirements.lock` (or `pip install -r requirements-dev.txt`):
-
-```bash
-YOLO_OFFLINE=1 YOLO_AUTOINSTALL=false python -m unittest discover -s code -p 'test_*.py' -v
-```
-
-That discovers every `code/test_*.py` module. `test_real_model` is included and skipped unless `DORAYAKI_REAL_MODEL=1` (it loads the YOLO weights). CI runs the same command from `.github/workflows/test.yml`.
-
-```bash
-YOLO_OFFLINE=1 python code/bench_engine.py   # speed measurement, not part of the suite
-```
-
----
-
-## 🌟 Tính năng chính
-
-1. **Nhận diện thời gian thực (Live)**: phát hiện sản phẩm trên từng khung hình, theo dõi xuyên khung hình, phân loại một lần cho mỗi sản phẩm, gắn nhãn tên/giá ngay trên khung hình; hiển thị số khung/giây và độ trễ.
-2. **Phát hiện chỗ trống trên kệ (Out-of-Stock)**: khoanh đỏ các vị trí hết hàng kèm ước lượng số lượng thiếu; có bộ lọc ổn định để khung không nhấp nháy.
-3. **Nhận diện ảnh tĩnh (Photo)**: nhận diện toàn bộ kệ trong một lần thay vì bấm từng sản phẩm.
-4. **Phân loại đa thuật toán**: Hybrid (Paper), SURF/SIFT, ORB, BRISK - đổi được ngay trong lúc chạy.
-5. **Trạng thái trung thực**: chỉ hiện tên sản phẩm khi kết quả được chấp nhận; trường hợp mơ hồ hiện "要確認" kèm các ứng viên, không đoán bừa.
-6. **Tương tác chi tiết**: bấm vào sản phẩm để xem tên, giá, ngày nhập, hạn sử dụng, tồn kho và điểm matching.
-7. **Đa client**: nhiều client kết nối đồng thời, mỗi client có session và tracking riêng.
-8. **Đa ngôn ngữ**: Tiếng Anh và Tiếng Nhật.
-9. **Gợi ý chất lượng ảnh**: cảnh báo lóa (glare), rung (blur), chụp quá xa.
-
----
-
-## 📁 Cấu trúc mã nguồn
-
-```
-code/
-  app.py             # server: REST /v1 + WebSocket /v1/stream + phục vụ web client
-  engine.py          # RecognitionEngine: detector + matcher + catalog, thread-safe
-  live.py            # LiveSession: tracking, phân loại nền, gắn nhãn, chất lượng ảnh
-  tracking.py        # SessionTracker: ByteTrack với ID riêng cho từng session
-  detector.py        # YOLO detector
-  feature_matcher.py # SURF/SIFT, ORB, BRISK, Hybrid
-  identity.py        # accepted / ambiguous / unknown / rejected
-  empty_shelf_detector.py, inventory.py, gallery.py, tags.py, tag_cards.py
-  stream_client.py   # client mẫu (camera / video / pan ảnh tĩnh)
-  bench_engine.py    # đo tốc độ
-  test_*.py          # unittest modules (discover -s code -p 'test_*.py')
-  web/               # web client (index.html, styles.css, app.js)
-docs/API.md          # tài liệu API đầy đủ
-docs/design-price-tags-and-compliance.md   # thiết kế OCR giá, đối chiếu, compliance, mobile
-```
+## 🌟 Các tính năng chính của ứng dụng
+1. **Phát hiện sản phẩm (YOLO):** Tự động detect hơn 170+ vật phẩm trên kệ trong 1 lượt scan.
+2. **Phát hiện chỗ trống trên kệ (Out-of-Stock Detection):** Tự động phát hiện các vị trí hết hàng và khoanh đỏ, ước lượng số lượng sản phẩm đang thiếu.
+3. **Phân loại đa thuật toán:** Cho phép chọn giữa **Hybrid (Paper)**, **SURF / SIFT**, **ORB**, **BRISK**.
+4. **Tương tác trực quan:** Click vào bất kỳ sản phẩm nào trên ảnh kệ để xem tên, giá, ngày nhập, hạn sử dụng, số lượng tồn kho và xem các điểm matching inliers.
+5. **Đa ngôn ngữ:** Hỗ trợ chuyển đổi nhanh Tiếng Anh và Tiếng Nhật.
+6. **Tải ảnh linh hoạt:** Chọn ảnh từ danh mục mẫu, chụp ảnh trực tiếp từ webcam/camera điện thoại, hoặc upload ảnh từ máy tính.
diff --git a/recognization/code/app.py b/recognization/code/app.py
index 481b254..b01c90d 100644
--- a/recognization/code/app.py
+++ b/recognization/code/app.py
@@ -1,41 +1,24 @@
 """
-Dorayaki recognition server (どら焼き).
-
-The computer-vision model runs here, as a service. Capture clients talk to it
-over a versioned REST API (stills, catalog, inspect) and a WebSocket stream
-(live frames in, tracked products and labels out). Any number of clients can
-connect at once; no client shares state with another.
-
-Run:
-    python code/app.py --port 8080
-    python code/app.py --host 0.0.0.0 --port 8080     # LAN, set DORAYAKI_API_TOKEN
-    python code/app.py --ssl-certfile cert.pem --ssl-keyfile key.pem
-
-The web client is served from / and is a pure client of the API; it can also be
-pointed at another server with ?server=https://host:port.
+Interactive Web Application: Dorayaki (どら焼き)
+Supermarket Shelf Product Recognition & Inventory System
+Features:
+- Light, fresh pastel green visual design
+- Brand Logo: Dorayaki
+- Live camera photo capture + File upload + Select from D:\\recognization\\add
+- Clean bounding box detection (NO class labels, NO confidence text)
+- Dual language support (English / Japanese)
+- Fine-grained classification: SURF, ORB, BRISK, Hybrid (Paper method)
 """
 
-from __future__ import annotations
-
-import argparse
-import asyncio
-import base64
-import binascii
-from contextlib import asynccontextmanager
-from dataclasses import dataclass
-import json
-import logging
 import os
-import re
 import sys
-import threading
-import time
-import uuid
-from typing import Any, Dict, List, Optional, Tuple
-
-# Keep the demo off the network before anything imports ultralytics.
-os.environ.setdefault("YOLO_OFFLINE", "1")
-os.environ.setdefault("YOLO_AUTOINSTALL", "false")
+import io
+import cv2
+import json
+import base64
+import argparse
+import numpy as np
+from typing import Optional, List, Dict
 
 current_dir = os.path.dirname(os.path.abspath(__file__))
 project_root = os.path.dirname(current_dir)
@@ -45,1184 +28,1218 @@ if os.path.exists(os.path.join(project_root, "stock")):
 if current_dir not in sys.path:
     sys.path.insert(0, current_dir)
 
-from fastapi import (  # noqa: E402
-    Depends,
-    FastAPI,
-    Header,
-    HTTPException,
-    Request,
-    WebSocket,
-    WebSocketDisconnect,
-)
-from fastapi.exceptions import RequestValidationError  # noqa: E402
-from fastapi.middleware.cors import CORSMiddleware  # noqa: E402
-from fastapi.responses import FileResponse, JSONResponse, Response  # noqa: E402
-from fastapi.staticfiles import StaticFiles  # noqa: E402
-from pydantic import BaseModel, Field  # noqa: E402
-
-from engine import (  # noqa: E402
-    CLASSIFY_METHODS,
-    COORDINATE_CONVENTION,
-    EngineSettings,
-    ImageExpired,
-    RecognitionEngine,
-    STATE_DETECTED,
-    UndecodableImage,
-    UnknownMethod,
-    build_default_engine,
-    identity_payload,
-    settings_from_env,
-)
-from record import FrameRecorder  # noqa: E402
-from live import MAX_CROP_BYTES, LiveSession, LiveSettings  # noqa: E402
-from transport import (  # noqa: E402
-    BodyLimit,
-    ServiceAdvertiser,
-    advertise_host,
-    bearer_matches,
-    declared_length,
-    is_loopback_host,
-    read_bounded,
-    require_token_for_bind,
-    token_matches,
-)
-
-LOGGER = logging.getLogger("dorayaki")
-
-MAX_UPLOAD_BYTES = 20 * 1024 * 1024
-MAX_FRAME_BYTES = 4 * 1024 * 1024
-DEFAULT_MAX_SESSIONS = 4
-WS_IDLE_TIMEOUT_S = 120.0
-CATALOG_VERSION_FALLBACK = "unknown"
-MAX_CROP_PIXELS = 4_000_000
-
-# The replayed-sample pan: a portrait window at the phone's frame size, JPEG at
-# the quality the device camera sends. `PAN_STEP` is pixels per frame **in the
-# sent frame**, so it is divided by the downscale before slicing: the window is
-# the sample's full height scaled to `PAN_LONG_EDGE`, and a step measured in
-# source pixels would shrink with that scale and silently change the pace.
-PAN_LONG_EDGE = 1280
-PAN_STEP = 5.6
-PAN_JPEG_QUALITY = 70
+from pipeline import ShelfRecognitionPipeline
 
 
-def _jpeg_dimensions(data: bytes) -> Optional[Tuple[int, int]]:
-    """Read JPEG SOF dimensions without allocating a decoded image."""
-    if not data.startswith(b"\xff\xd8"):
-        return None
-    offset = 2
-    while offset + 4 <= len(data):
-        if data[offset] != 0xff:
-            return None
-        while offset < len(data) and data[offset] == 0xff:
-            offset += 1
-        if offset >= len(data):
-            return None
-        marker = data[offset]
-        offset += 1
-        if marker in (0xd8, 0x01) or 0xd0 <= marker <= 0xd7:
-            continue
-        if marker in (0xd9, 0xda) or offset + 2 > len(data):
-            return None
-        length = int.from_bytes(data[offset:offset + 2], "big")
-        if length < 2 or offset + length > len(data):
-            return None
-        if marker in (0xc0, 0xc1, 0xc2, 0xc3, 0xc5, 0xc6, 0xc7, 0xc9, 0xca, 0xcb, 0xcd, 0xce, 0xcf):
-            if length < 7:
-                return None
-            return (int.from_bytes(data[offset + 5:offset + 7], "big"),
-                    int.from_bytes(data[offset + 3:offset + 5], "big"))
-        offset += length
-    return None
+def encode_image_base64(img_bgr: np.ndarray, quality: int = 85) -> str:
+    """Encodes an OpenCV image to a base64 JPEG data URL."""
+    if img_bgr is None or img_bgr.size == 0:
+        return ""
+    success, buffer = cv2.imencode(".jpg", img_bgr, [cv2.IMWRITE_JPEG_QUALITY, quality])
+    if not success:
+        return ""
+    return f"data:image/jpeg;base64,{base64.b64encode(buffer).decode('utf-8')}"
 
 
-# ---------------------------------------------------------------------------
-# settings
-# ---------------------------------------------------------------------------
-
-
-@dataclass
-class ServerSettings:
-    host: str = "127.0.0.1"
-    port: int = 8080
-    token: Optional[str] = None
-    # Empty means same-origin only. "*" reflects every caller and is not a default.
-    cors_origins: str = ""
-    max_sessions: int = DEFAULT_MAX_SESSIONS
-    live_method: str = "hybrid"
-    live_conf: float = 0.1
-    live_tags: bool = True
-    # A live session can be recorded so it becomes data to label (record.py).
-    record_frames: Optional[str] = None
-    record_every: int = 3
-    log_level: str = "info"
-    ssl_certfile: Optional[str] = None
-    ssl_keyfile: Optional[str] = None
-
-
-def server_settings_from_env() -> ServerSettings:
-    base = ServerSettings()
-    return ServerSettings(
-        host=os.environ.get("DORAYAKI_HOST", base.host),
-        port=int(os.environ.get("DORAYAKI_PORT", base.port)),
-        token=os.environ.get("DORAYAKI_API_TOKEN") or None,
-        cors_origins=os.environ.get("DORAYAKI_CORS_ORIGINS", base.cors_origins),
-        max_sessions=int(os.environ.get("DORAYAKI_MAX_SESSIONS", base.max_sessions)),
-        live_method=os.environ.get("DORAYAKI_LIVE_METHOD", base.live_method),
-        live_conf=float(os.environ.get("DORAYAKI_LIVE_CONF", base.live_conf)),
-        live_tags=os.environ.get("DORAYAKI_LIVE_TAGS", "1").lower() not in ("0", "false", "no"),
-        log_level=os.environ.get("DORAYAKI_LOG_LEVEL", base.log_level),
-        ssl_certfile=os.environ.get("DORAYAKI_SSL_CERTFILE") or None,
-        ssl_keyfile=os.environ.get("DORAYAKI_SSL_KEYFILE") or None,
-    )
-
-
-def load_local_token(path: str = "config/local.env") -> None:
-    """Fill DORAYAKI_API_TOKEN from an ignored env file. Never prints the value.
-
-    ponytail: one KEY=VALUE file, no quoting. Upgrade path is a real env loader
-    once a second secret needs the same file.
-    """
-    if os.environ.get("DORAYAKI_API_TOKEN"):
-        return
+def decode_image_base64(data_url: str) -> Optional[np.ndarray]:
+    """Decodes a base64 data URL into an OpenCV BGR image."""
     try:
-        with open(path, "r", encoding="utf-8") as handle:
-            lines = handle.readlines()
-    except OSError:
-        return
-    for line in lines:
-        key, sep, value = line.strip().partition("=")
-        if key == "DORAYAKI_API_TOKEN" and sep and value:
-            os.environ["DORAYAKI_API_TOKEN"] = value
-            return
-
-
-# ---------------------------------------------------------------------------
-# request models
-# ---------------------------------------------------------------------------
-
-
-class StockUpdateBody(BaseModel):
-    delta: int = Field(..., ge=-100, le=100, description="units added (+) or dispensed (-)")
-
-
-class ClassifyBody(BaseModel):
-    method: Optional[str] = None
-    detection_ids: Optional[List[int]] = None
-
-
-class InspectBody(BaseModel):
-    box: List[int] = Field(..., min_length=4, max_length=4)
-    method: Optional[str] = None
-
-
-# ---------------------------------------------------------------------------
-# helpers
-# ---------------------------------------------------------------------------
-
-
-def _error(code: str, message: str, status_code: int) -> HTTPException:
-    return HTTPException(status_code=status_code, detail={"error": {"code": code, "message": message}})
-
-
-def _require_method(method: Optional[str]) -> str:
-    chosen = (method or "hybrid").lower()
-    if chosen not in CLASSIFY_METHODS:
-        raise _error("BAD_REQUEST", f"unknown method '{method}'", 400)
-    return chosen
+        if "," in data_url:
+            data_url = data_url.split(",")[1]
+        img_bytes = base64.b64decode(data_url)
+        np_arr = np.frombuffer(img_bytes, np.uint8)
+        return cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
+    except Exception as e:
+        print(f"[Error] Failed to decode base64 image: {e}")
+        return None
 
 
 def get_available_shelf_images(add_dir: str = "add") -> List[Dict[str, str]]:
-    """Same demo set the old /api/shelf_samples returned, now id-keyed."""
-    items: List[Dict[str, str]] = []
+    """Lists images from D:\\recognization\\add and root image.jpg."""
+    items = []
     if os.path.exists("image.jpg"):
-        items.append({"id": "main_shelf", "path": "image.jpg", "label": "image.jpg (Main Shelf)"})
+        items.append({"path": "image.jpg", "label": "image.jpg (Main Shelf)"})
 
     if os.path.exists(add_dir):
         valid_exts = (".webp", ".jpg", ".jpeg", ".png")
         files = sorted(
             [f for f in os.listdir(add_dir) if f.lower().endswith(valid_exts)],
-            key=lambda x: (len(x), x),
+            key=lambda x: (len(x), x)
         )
-        for name in files:
-            items.append(
-                {
-                    "id": name.rsplit(".", 1)[0],
-                    "path": os.path.join(add_dir, name).replace("\\", "/"),
-                    "label": f"{add_dir}/{name}",
-                }
-            )
+        for f in files:
+            p = os.path.join(add_dir, f).replace("\\", "/")
+            items.append({"path": p, "label": f"{add_dir}/{f}"})
 
+    # Also include runs samples if present
     mosaic = "runs/detect/train/train_batch0.jpg"
     if os.path.exists(mosaic):
-        items.append({"id": "train_mosaic", "path": mosaic, "label": "train_batch0.jpg (Mosaic)"})
+        items.append({"path": mosaic, "label": "train_batch0.jpg (Mosaic)"})
+
     return items
 
 
+def create_fastapi_app(pipeline: ShelfRecognitionPipeline):
+    from fastapi import FastAPI, Request
+    from fastapi.responses import HTMLResponse, JSONResponse
+    from fastapi.middleware.cors import CORSMiddleware
 
-
-# ---------------------------------------------------------------------------
-# app factory
-# ---------------------------------------------------------------------------
-
-
-def create_app(
-    engine: RecognitionEngine,
-    settings: Optional[ServerSettings] = None,
-    *,
-    web_dir: Optional[str] = None,
-) -> FastAPI:
-    settings = settings or server_settings_from_env()
-    @asynccontextmanager
-    async def lifespan(_app: FastAPI):
-        yield
-        for session in list(sessions.values()):
-            if session is not None:
-                session.close()
-        engine.shutdown()
-
-    app = FastAPI(
-        title="Dorayaki Recognition Server",
-        version="1.0",
-        description="Shelf product detection, tracking and classification for capture clients.",
-        lifespan=lifespan,
+    app = FastAPI(title="Dorayaki - Supermarket Shelf Intelligence")
+    app.add_middleware(
+        CORSMiddleware,
+        allow_origins=["*"],
+        allow_credentials=True,
+        allow_methods=["*"],
+        allow_headers=["*"],
     )
-    # Same-origin is the default. A configured list is echoed back; "*" is not.
-    cors = [o.strip() for o in settings.cors_origins.split(",") if o.strip() and o.strip() != "*"]
-    if cors:
-        app.add_middleware(
-            CORSMiddleware,
-            allow_origins=cors,
-            allow_credentials=False,
-            allow_methods=["GET", "POST"],
-            allow_headers=["Authorization", "Content-Type"],
-        )
 
-    # A live session can be recorded so a real scan becomes data to label later.
-    recorder = FrameRecorder(
-        settings.record_frames,
-        every=settings.record_every,
-        identity_backend=engine.settings.identity_backend,
-    ) if settings.record_frames else None
-    samples = {item["id"]: item for item in get_available_shelf_images("add")}
-    # path -> (mtime_ns, size, width, height). Dropped when the file changes.
-    sample_dimensions: Dict[str, Tuple[int, int, int, int]] = {}
-    sample_dimensions_lock = threading.Lock()
-    # path -> ((mtime_ns, size), decoded image). The pan re-cuts this per frame.
-    sample_images: Dict[str, Any] = {}
-    sample_image_lock = threading.Lock()
-    sessions: Dict[str, LiveSession] = {}
-    sessions_lock = asyncio.Lock()
-    app.state.engine = engine
-    app.state.sessions = sessions
-
-    async def authorize(authorization: Optional[str] = Header(default=None)) -> None:
-        if not settings.token:
-            return
-        if not bearer_matches(authorization, settings.token):
-            raise _error("UNAUTHORIZED", "missing or invalid bearer token", 401)
-
-    # ---------------------------------------------------------------- health
-    @app.get("/v1/health")
-    async def health() -> Dict[str, Any]:
-        loop = asyncio.get_running_loop()
-
-        def _health() -> Dict[str, Any]:
-            gallery = (
-                engine.identity_gallery()
-                if getattr(engine.settings, "identity_backend", "") == "gallery"
-                else None
-            )
-            return {
-                "status": "ok",
-                "model": {
-                    "version": engine.model_version(),
-                    "device": getattr(engine.settings, "device", None) or "auto",
-                    "imgsz": getattr(engine.settings, "imgsz", 640),
-                    "warm": True,
-                },
-                "catalog": {"version": engine.catalog_version(), "sku_count": engine.sku_count()},
-                "identity": {
-                    "backend": getattr(engine.settings, "identity_backend", "matcher"),
-                    "gallery_views": gallery.view_count() if gallery is not None else None,
-                },
-                "ocr": {
-                    # Without this an operator could not tell a broken OCR from a shelf
-                    # with no readable tags.
-                    "engine": getattr(engine.ocr_engine, "name", "none"),
-                    "available": bool(getattr(engine.ocr_engine, "available", lambda: False)()),
-                },
-                "sessions_active": sum(session is not None for session in sessions.values()),
-                "uptime_s": round(engine.uptime_s(), 1),
-            }
-
-        return await loop.run_in_executor(None, _health)
-
-    # ---------------------------------------------------------------- catalog
-    @app.get("/v1/catalog", dependencies=[Depends(authorize)])
-    async def catalog() -> Dict[str, Any]:
-        loop = asyncio.get_running_loop()
-
-        def _catalog() -> Dict[str, Any]:
-            return {"catalog_version": engine.catalog_version(), "products": engine.catalog()}
-
-        return await loop.run_in_executor(None, _catalog)
-
-    @app.get("/v1/catalog/{sku_id}/image", dependencies=[Depends(authorize)])
-    async def catalog_image(sku_id: str) -> Response:
-        loop = asyncio.get_running_loop()
-        path = await loop.run_in_executor(None, engine.catalog_image_path, sku_id)
-        if not path:
-            raise _error("NOT_FOUND", f"no catalog image for '{sku_id}'", 404)
-        return FileResponse(path)
-
-    @app.post("/v1/catalog/{sku_id}/stock", dependencies=[Depends(authorize)])
-    async def update_stock(sku_id: str, body: StockUpdateBody) -> Dict[str, Any]:
-        if body.delta == 0:
-            raise _error("VALIDATION_ERROR", "delta must not be zero", 422)
-        loop = asyncio.get_running_loop()
-        product = await loop.run_in_executor(None, engine.update_stock, sku_id, body.delta)
-        if product is None:
-            raise _error("NOT_FOUND", f"unknown sku '{sku_id}'", 404)
-        return {"status": "success", "product": product}
-
-    def _sample_dimensions(path: str) -> Optional[Tuple[int, int]]:
-        try:
-            stat = os.stat(path)
-        except OSError:
-            with sample_dimensions_lock:
-                sample_dimensions.pop(path, None)
-            return None
-        key = (stat.st_mtime_ns, stat.st_size)
-        with sample_dimensions_lock:
-            cached = sample_dimensions.get(path)
-            if cached is not None and cached[:2] == key:
-                return cached[2], cached[3]
-        import cv2
-
-        image = cv2.imread(path)
-        if image is None:
-            with sample_dimensions_lock:
-                sample_dimensions.pop(path, None)
-            return None
-        width, height = int(image.shape[1]), int(image.shape[0])
-        with sample_dimensions_lock:
-            sample_dimensions[path] = (key[0], key[1], width, height)
-        return width, height
-
-    def _panned_jpeg(path: str, index: int) -> Optional[bytes]:
-        """One portrait window of `path`, slid `index` steps along the shelf.
-
-        The window is the shape the phone sends - 9:16, long edge
-        `PAN_LONG_EDGE` - but it spans the sample's **full height** and pans
-        horizontally. A window that is only `PAN_LONG_EDGE` tall shows one
-        shelf row: on `image6` (1500x2000, rows at y 200/560/870/1150/1440) a
-        720 px band centred at 640-1360 reaches 52 of 66 enrolled facings and
-        gives every other SKU 0% coverage, which reads as a matcher failure
-        when it is only the crop. Full height reaches all of them, and the
-        downscale to `PAN_LONG_EDGE` is the same 0.64 the evaluator's live
-        resolution uses.
-
-        It steps `PAN_STEP` pixels per frame and reverses at each edge, so the
-        walk is continuous rather than a jump back to the start.
-
-        The decoded image is cached: re-reading a 1500x2000 JPEG per frame cost
-        more than the encode, which made the walk slower than `PAN_STEP` says.
-        """
-        import cv2
-
-        image = _decoded_sample(path)
-        if image is None:
-            return None
-        height, width = image.shape[:2]
-        window_h = height
-        window_w = min(int(window_h * 9 / 16), width)
-        max_x = max(0, width - window_w)
-        # `PAN_STEP` is in sent-frame pixels; the slice is in source pixels.
-        scale = min(1.0, PAN_LONG_EDGE / float(window_h))
-        step = PAN_STEP / scale if scale > 0 else PAN_STEP
-        travel = index * step
-        if max_x == 0:
-            x = 0
-        else:
-            # Triangle wave: pan out to the edge, then back, forever.
-            period = 2 * max_x
-            phase = travel % period
-            x = int(phase if phase <= max_x else period - phase)
-        window = image[:, x:x + window_w]
-        if scale < 1.0:
-            window = cv2.resize(
-                window,
-                (int(round(window.shape[1] * scale)), int(round(window.shape[0] * scale))),
-                interpolation=cv2.INTER_AREA,
-            )
-        ok, encoded = cv2.imencode(".jpg", window, [int(cv2.IMWRITE_JPEG_QUALITY), PAN_JPEG_QUALITY])
-        return encoded.tobytes() if ok else None
-
-    def _decoded_sample(path: str) -> Any:
-        """The sample decoded once, re-read only when the file changes."""
-        try:
-            stat = os.stat(path)
-        except OSError:
-            with sample_image_lock:
-                sample_images.pop(path, None)
-            return None
-        key = (stat.st_mtime_ns, stat.st_size)
-        with sample_image_lock:
-            cached = sample_images.get(path)
-            if cached is not None and cached[0] == key:
-                return cached[1]
-        import cv2
-
-        image = cv2.imread(path)
-        if image is None:
-            return None
-        with sample_image_lock:
-            sample_images[path] = (key, image)
-        return image
-
-    def _sample_listing() -> List[Dict[str, Any]]:
-        listing = []
-        for item in samples.values():
-            entry = {"id": item["id"], "label": item["label"], "url": f"/v1/samples/{item['id']}"}
-            dimensions = _sample_dimensions(item["path"])
-            if dimensions is not None:
-                entry["width"], entry["height"] = dimensions
-            listing.append(entry)
-        return listing
-
-    @app.get("/v1/samples", dependencies=[Depends(authorize)])
-    async def list_samples() -> Dict[str, Any]:
-        loop = asyncio.get_running_loop()
-        return {"samples": await loop.run_in_executor(None, _sample_listing)}
-
-    @app.get("/v1/samples/{sample_id}", dependencies=[Depends(authorize)])
-    async def get_sample(sample_id: str, pan: Optional[int] = None) -> Response:
-        item = samples.get(sample_id)
-        if not item or not await asyncio.get_running_loop().run_in_executor(None, os.path.exists, item["path"]):
-            raise _error("NOT_FOUND", f"no sample '{sample_id}'", 404)
-        if pan is None:
-            return FileResponse(item["path"])
-        # A still replayed as frames is a frozen shelf: every frame carries the
-        # same boxes, so the live path never exercises a track that moves. `pan`
-        # is the frame index, and the window slides one step per frame and
-        # reverses at the edges - the same walk `stream_client.py` drives, so a
-        # simulator sees what a real slow pan produces.
-        loop = asyncio.get_running_loop()
-        jpeg = await loop.run_in_executor(None, _panned_jpeg, item["path"], max(0, pan))
-        if jpeg is None:
-            raise _error("NOT_FOUND", f"cannot read sample '{sample_id}'", 404)
-        return Response(content=jpeg, media_type="image/jpeg")
-
-    # ---------------------------------------------------------------- recognize
-    @app.post("/v1/recognize", dependencies=[Depends(authorize)])
-    async def recognize(
-        request: Request,
-        classify: bool = True,
-        method: Optional[str] = None,
-        conf: Optional[float] = None,
-        empty_slots: bool = True,
-        tags: bool = False,
-    ) -> Dict[str, Any]:
-        if method is not None:
-            _require_method(method)
-        content_type = (request.headers.get("content-type") or "").split(";")[0].strip().lower()
-        announced = declared_length(request.headers.get("content-length"))
-        if announced is not None and announced > MAX_UPLOAD_BYTES:
-            raise _error("PAYLOAD_TOO_LARGE", f"image larger than {MAX_UPLOAD_BYTES} bytes", 413)
-        if content_type == "multipart/form-data":
-            form = await request.form(max_part_size=MAX_UPLOAD_BYTES)
-            upload = form.get("file")
-            if upload is None or not hasattr(upload, "read"):
-                raise _error("BAD_REQUEST", "multipart body needs a 'file' field", 400)
-            payload = await read_bounded(upload, MAX_UPLOAD_BYTES)
-        else:
-            if content_type and not content_type.startswith("image/") and content_type != "application/octet-stream":
-                raise _error("UNSUPPORTED_MEDIA_TYPE", f"unsupported content type '{content_type}'", 415)
-            payload = await read_bounded(request.stream(), MAX_UPLOAD_BYTES)
-        if not payload:
-            raise _error("BAD_REQUEST", "empty request body", 400)
-        if len(payload) > MAX_UPLOAD_BYTES:
-            raise _error("PAYLOAD_TOO_LARGE", f"image larger than {MAX_UPLOAD_BYTES} bytes", 413)
-        loop = asyncio.get_running_loop()
-        try:
-            image = await loop.run_in_executor(None, engine.decode_image, payload)
-        except UndecodableImage as exc:
-            raise _error("UNDECODABLE_IMAGE", str(exc), 422)
-        try:
-            result = await loop.run_in_executor(
-                None,
-                lambda: engine.recognize(
-                    image,
-                    classify=classify,
-                    method=method,
-                    conf=conf,
-                    with_empty_slots=empty_slots,
-                ),
-            )
-        except UnknownMethod as exc:
-            raise _error("BAD_REQUEST", str(exc), 400)
-
-        if tags:
-            # One submit for the whole still-tag operation, on the same
-            # single-worker executor the live sessions read tags on. The default
-            # pool would let a photo's OCR run beside a live session's Vision
-            # work: pyobjc holds the GIL, so the two would interleave and both
-            # would slow down. `_attach_still_tags` calls `run_pass` itself
-            # (reconcile.attach_tags_to_boxes), never submits, so nothing here
-            # queues behind the worker it is running on.
-            result = await loop.run_in_executor(
-                engine.tag_executor, _attach_still_tags, result, image
-            )
-        return result
-
-    def _is_price_card(crop: Any) -> bool:
-        """Is this detection a shelf price card rather than a product?
-
-        Measured on a supermarket photo whose shelf edge carries big price cards:
-        the detector boxes them as products, because a card is roughly the shape of a
-        pack. Their text gives them away - a price card's characters are 30-44 %
-        digits and it prints the tax-excluded price (本体), while a product's are 3-19 %
-        digits. Reporting a price card as a product would put a box labelled with a
-        product name on a price tag, which is worse than not detecting it.
-
-        ponytail: every surviving detection pays one Vision read. There is no cheap
-        pre-filter to add, because a card is only distinguishable by its text - its
-        box is pack-shaped, so the aspect/position bounds in tags.py (measured for
-        real tag regions, 1.8-3.8:1) would exclude the boxes cards actually arrive
-        in. Re-measure boxed-card aspect from real photos before narrowing this; a
-        card that slips through is a product name printed on a price tag.
-        """
-        ocr_engine = getattr(engine, "ocr_engine", None)
-        if ocr_engine is None or not getattr(ocr_engine, "available", lambda: False)():
-            return False
-        try:
-            import cv2
-
-            big = cv2.resize(crop, None, fx=2.0, fy=2.0, interpolation=cv2.INTER_CUBIC)
-            lines = ocr_engine.read(big).lines
-        except Exception:
-            return False
-        text = " ".join(line.text for line in lines)
-        compact = re.sub(r"\s", "", text)
-        if not compact:
-            return False
-        digits = sum(1 for char in compact if char.isdigit() or char in "¥￥./") / len(compact)
-        return ("本体" in text and digits >= 0.20) or digits >= 0.30
-
-    def _drop_price_cards(result: Dict[str, Any], image: Any) -> int:
-        """Remove detections that are price cards, and say how many were removed."""
-        kept, dropped = [], 0
-        for detection in result["detections"]:
-            crop = engine.crop(image, detection["box"])
-            if crop is not None and crop.size and _is_price_card(crop):
-                dropped += 1
-                continue
-            kept.append(detection)
-        if dropped:
-            result["detections"] = kept
-            result["summary"] = engine.summarize(kept, result.get("empty_slots") or [])
-        result["price_cards_dropped"] = dropped
-        return dropped
-
-    def _attach_still_tags(result: Dict[str, Any], image: Any) -> Dict[str, Any]:
-        """Run one synchronous tag pass over a still and merge its evidence."""
-        ocr_engine = getattr(engine, "ocr_engine", None)
-        if ocr_engine is None or not getattr(ocr_engine, "available", lambda: False)():
-            result["price_tags"] = {"readable": 0, "unreadable": 0, "absent": 0}
-            result["compliance"] = {"violations": 0, "by_rule": {}, "rows": [], "hints": []}
-            result["ocr_engine"] = "none"
-            return result
-
-        from reconcile import attach_tags_to_boxes
-        from tags import TagService, TagSettings
-
-        # Before anything else: a price card is not a product.
-        _drop_price_cards(result, image)
-
-        boxes = [
-            {"id": detection["id"], "box": detection["box"]} for detection in result["detections"]
-        ]
-        identities = {
-            int(detection["id"]): detection["identity"] for detection in result["detections"]
+    HTML_PAGE = """<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Dorayaki - Smart Supermarket Shelf AI</title>
+    <link href="https://fonts.googleapis.com/css2?family=Plus+Jakarta+Sans:wght@400;500;600;700;800&family=Noto+Sans+JP:wght@400;500;700&display=swap" rel="stylesheet">
+    <style>
+        :root {
+            --bg-body: #f4fbf6;
+            --bg-card: #ffffff;
+            --bg-glass: rgba(255, 255, 255, 0.9);
+            --primary-green: #22c55e;
+            --primary-dark: #15803d;
+            --primary-light: #dcfce7;
+            --primary-subtle: #f0fdf4;
+            --accent-green: #86efac;
+            --border-color: #d1fae5;
+            --border-hover: #86efac;
+            --text-main: #0f172a;
+            --text-muted: #64748b;
+            --shadow-soft: 0 4px 20px -2px rgba(34, 197, 94, 0.08), 0 2px 6px -1px rgba(0, 0, 0, 0.04);
+            --shadow-hover: 0 10px 25px -3px rgba(34, 197, 94, 0.16);
         }
-        catalog_by_sku = {product["sku_id"]: product for product in engine.catalog()}
-        service = TagService(ocr_engine, settings=TagSettings())
-        started = time.perf_counter()
-        bundle = attach_tags_to_boxes(service, image, boxes, catalog_by_sku, identities)
-        elapsed_ms = (time.perf_counter() - started) * 1000.0
 
-        # Tag-first identity: the store's own label, read from the tag printed
-        # under the facings it names. It fills identities the packaging pass could
-        # not resolve and never overwrites one it did.
-        tag_identities = bundle.get("tag_identities") or {}
-        for detection in result["detections"]:
-            derived = tag_identities.get(int(detection["id"]))
-            if not derived:
-                continue
-            # The tag's claim is always reported, so a client can compare the two
-            # identity channels; it only *fills* the identity when the packaging
-            # pass could not name the product.
-            detection["tag_identity"] = dict(derived)
-            if (detection.get("identity") or {}).get("state") == "recognized" \
-                    and (detection["identity"].get("sku_id") == derived["sku_id"]
-                         or detection["identity"].get("source") == "tag_name"):
-                continue
-            product = catalog_by_sku.get(derived["sku_id"]) or {}
-            detection["identity"] = {
-                "state": "recognized",
-                "sku_id": derived["sku_id"],
-                "identity_level": "tag_derived",
-                "confidence": derived["score"],
-                "score": derived["score"],
-                "source": "tag_name",
-                "candidates": [],
-                "attempts": 1,
-                "method": "tag_name",
-            }
-            detection["product"] = {
-                "sku_id": product.get("sku_id"),
-                "product_name": product.get("product_name"),
-                "price": product.get("price"),
-            }
-
-        for detection in result["detections"]:
-            box_id = int(detection["id"])
-            tag = bundle["by_box"].get(box_id)
-            if tag is not None:
-                detection["price_tag"] = tag
-            reconciliation = bundle["reconciliations"].get(box_id)
-            if reconciliation is not None:
-                detection["reconciliation"] = reconciliation
-
-        result["price_tags"] = bundle["counts"]
-        result["compliance"] = bundle["compliance"]
-        result["ocr_engine"] = ocr_engine.name
-        result["tag_timing_ms"] = round(elapsed_ms, 1)
-        engine.image_store.set_tag_bundle(result["image_id"], bundle)
-        return result
-
-    @app.post("/v1/images/{image_id}/classify", dependencies=[Depends(authorize)])
-    async def classify_image(image_id: str, body: ClassifyBody) -> Dict[str, Any]:
-        method = _require_method(body.method)
-        try:
-            stored = engine.image_store.get(image_id)
-        except ImageExpired:
-            raise _error("IMAGE_EXPIRED", f"image '{image_id}' is no longer available", 404)
-        detections = stored.detections
-        if body.detection_ids is not None:
-            wanted = set(int(i) for i in body.detection_ids)
-            detections = [d for d in detections if int(d["id"]) in wanted]
-        started = time.perf_counter()
-        loop = asyncio.get_running_loop()
-        rows = await loop.run_in_executor(
-            None, lambda: engine.classify_boxes(stored.image, detections, method=method)
-        )
-        rows_by_id = {row["detection_id"]: row for row in rows}
-        for detection in stored.detections:
-            row = rows_by_id.get(int(detection["id"]))
-            if row is not None:
-                detection["identity"] = row["identity"]
-        engine.image_store.update_detections(image_id, stored.detections)
-
-        # Tags were read before the identities existed; reconcile them now.
-        compliance = None
-        if stored.tag_bundle:
-            from reconcile import reconcile_bundle
-
-            catalog_by_sku = {product["sku_id"]: product for product in engine.catalog()}
-            refreshed = reconcile_bundle(stored.tag_bundle, stored.detections, catalog_by_sku)
-            for detection in stored.detections:
-                decision = refreshed["reconciliations"].get(int(detection["id"]))
-                if decision is not None:
-                    detection["reconciliation"] = decision
-            compliance = refreshed["compliance"]
-        identities = []
-        for detection in detections:
-            row = {
-                "detection_id": int(detection["id"]),
-                "box": list(detection["box"]),
-                "identity": detection["identity"],
-            }
-            # Re-reconciled after classification; without it a client would keep
-            # the `tag_only` verdict it got when identities did not exist yet.
-            if detection.get("reconciliation") is not None:
-                row["reconciliation"] = detection["reconciliation"]
-            if detection.get("price_tag") is not None:
-                row["price_tag"] = detection["price_tag"]
-            identities.append(row)
-        summary = engine.summarize(stored.detections, [])
-        payload = {
-            "image_id": image_id,
-            "method": method,
-            "identities": identities,
-            "summary": summary,
-            "timing_ms": {"classify": round((time.perf_counter() - started) * 1000.0, 2)},
+        * {
+            box-sizing: border-box;
+            margin: 0;
+            padding: 0;
+            font-family: 'Plus Jakarta Sans', 'Noto Sans JP', sans-serif;
         }
-        if compliance is not None:
-            payload["compliance"] = compliance
-        return payload
 
-    @app.post("/v1/images/{image_id}/inspect", dependencies=[Depends(authorize)])
-    async def inspect_image(image_id: str, body: InspectBody) -> Dict[str, Any]:
-        method = _require_method(body.method)
-        try:
-            stored = engine.image_store.get(image_id)
-        except ImageExpired:
-            raise _error("IMAGE_EXPIRED", f"image '{image_id}' is no longer available", 404)
-        loop = asyncio.get_running_loop()
-        return await loop.run_in_executor(
-            None, lambda: engine.inspect(stored.image, body.box, method=method)
-        )
+        body {
+            background-color: var(--bg-body);
+            color: var(--text-main);
+            min-height: 100vh;
+            display: flex;
+            flex-direction: column;
+        }
 
-    # ---------------------------------------------------------------- live stream
-    @app.websocket("/v1/stream")
-    async def stream(websocket: WebSocket) -> None:
-        # Query tokens are rejected. A wrong header is rejected before hello.
-        # A missing header is the browser case: hello goes out, then the first
-        # client text must be {"type":"auth","token":"..."} or the socket closes.
-        if websocket.query_params.get("token"):
-            await websocket.close(code=1008)
-            return
-        header = websocket.headers.get("authorization")
-        if settings.token and header and not bearer_matches(header, settings.token):
-            await websocket.close(code=1008)
-            return
-        loop = asyncio.get_running_loop()
-        async with sessions_lock:
-            if len(sessions) >= settings.max_sessions:
-                await websocket.close(code=1013)
-                return
-            session_id = str(uuid.uuid4())
-            sessions[session_id] = None  # reserve before any disk work
-        session = None
-        try:
-            model_version = await loop.run_in_executor(None, engine.model_version)
-            session = LiveSession(
-                session_id,
-                engine,
-                settings=LiveSettings(
-                    method=settings.live_method,
-                    conf=settings.live_conf,
-                    max_inflight_per_session=engine.settings.live_inflight,
-                    max_attempts=engine.settings.live_attempts,
-                    tags=settings.live_tags,
-                ),
-            )
-            await loop.run_in_executor(None, session.refresh_catalog)
-            catalog_version = session._catalog_version
-            if settings.live_tags:
-                await loop.run_in_executor(None, session.enable_tags)
-            async with sessions_lock:
-                sessions[session_id] = session
-        except BaseException:
-            if session is not None:
-                session.close()
-            async with sessions_lock:
-                sessions.pop(session_id, None)
-            raise
-        try:
-            await websocket.accept()
-            send_lock = asyncio.Lock()
+        header {
+            background: var(--bg-card);
+            border-bottom: 1px solid var(--border-color);
+            padding: 14px 32px;
+            display: flex;
+            justify-content: space-between;
+            align-items: center;
+            position: sticky;
+            top: 0;
+            z-index: 100;
+            box-shadow: 0 2px 10px rgba(0, 0, 0, 0.02);
+        }
 
-            async def send(payload: Dict[str, Any]) -> None:
-                async with send_lock:
-                    await websocket.send_text(json.dumps(payload))
-            await send(
-                {
-                    "type": "hello",
-                    "protocol": 1,
-                    "session_id": session_id,
-                    "catalog_version": catalog_version,
-                    "model": {
-                        "version": model_version,
-                        "device": getattr(engine.settings, "device", None) or "auto",
-                        "imgsz": getattr(engine.settings, "imgsz", 640),
-                    },
-                    "config": session.config(),
-                    "limits": {
-                        "max_frame_bytes": MAX_FRAME_BYTES,
-                        "max_crop_bytes": MAX_CROP_BYTES,
-                    },
+        .brand {
+            display: flex;
+            align-items: center;
+            gap: 14px;
+        }
+
+        .brand-logo-svg {
+            width: 44px;
+            height: 44px;
+            filter: drop-shadow(0 2px 6px rgba(34, 197, 94, 0.25));
+        }
+
+        .brand-title {
+            font-size: 20px;
+            font-weight: 800;
+            color: #14532d;
+            letter-spacing: -0.5px;
+            display: flex;
+            align-items: center;
+            gap: 6px;
+        }
+
+        .brand-jp {
+            font-size: 13px;
+            font-weight: 600;
+            color: #16a34a;
+            background: var(--primary-light);
+            padding: 2px 8px;
+            border-radius: 6px;
+        }
+
+        .brand-sub {
+            font-size: 12px;
+            color: var(--text-muted);
+            font-weight: 500;
+        }
+
+        .header-actions {
+            display: flex;
+            align-items: center;
+            gap: 12px;
+        }
+
+        .lang-switch-btn {
+            background: var(--primary-subtle);
+            border: 1px solid var(--border-color);
+            color: var(--primary-dark);
+            padding: 6px 14px;
+            border-radius: 999px;
+            font-size: 12px;
+            font-weight: 700;
+            cursor: pointer;
+            display: flex;
+            align-items: center;
+            gap: 6px;
+            transition: all 0.2s;
+        }
+
+        .lang-switch-btn:hover {
+            background: var(--primary-light);
+            border-color: var(--primary-green);
+        }
+
+        .agent-pills {
+            display: flex;
+            gap: 6px;
+        }
+
+        .pill {
+            font-size: 11px;
+            font-weight: 600;
+            padding: 4px 10px;
+            border-radius: 999px;
+            border: 1px solid var(--border-color);
+            background: var(--primary-subtle);
+            color: #166534;
+            display: flex;
+            align-items: center;
+            gap: 5px;
+        }
+
+        .dot {
+            width: 6px;
+            height: 6px;
+            border-radius: 50%;
+            background-color: var(--primary-green);
+            box-shadow: 0 0 6px var(--primary-green);
+        }
+
+        main {
+            flex: 1;
+            padding: 20px 32px;
+            display: grid;
+            grid-template-columns: 1fr 430px;
+            gap: 24px;
+        }
+
+        .shelf-panel {
+            background: var(--bg-card);
+            border: 1px solid var(--border-color);
+            border-radius: 18px;
+            padding: 20px;
+            display: flex;
+            flex-direction: column;
+            gap: 16px;
+            box-shadow: var(--shadow-soft);
+        }
+
+        .toolbar {
+            display: flex;
+            justify-content: space-between;
+            align-items: center;
+            flex-wrap: wrap;
+            gap: 12px;
+            padding-bottom: 12px;
+            border-bottom: 1px solid var(--border-color);
+        }
+
+        .toolbar-group {
+            display: flex;
+            align-items: center;
+            gap: 8px;
+            flex-wrap: wrap;
+        }
+
+        .btn-group {
+            display: flex;
+            background: var(--primary-subtle);
+            border: 1px solid var(--border-color);
+            border-radius: 10px;
+            padding: 3px;
+            gap: 2px;
+        }
+
+        .method-btn {
+            background: transparent;
+            border: none;
+            color: var(--text-muted);
+            padding: 6px 12px;
+            border-radius: 8px;
+            font-size: 12px;
+            font-weight: 600;
+            cursor: pointer;
+            transition: all 0.2s;
+        }
+
+        .method-btn.active {
+            background: var(--primary-green);
+            color: white;
+            box-shadow: 0 2px 8px rgba(34, 197, 94, 0.3);
+        }
+
+        .tool-btn {
+            background: var(--bg-card);
+            border: 1px solid var(--border-color);
+            color: #15803d;
+            padding: 7px 14px;
+            border-radius: 10px;
+            font-size: 12px;
+            font-weight: 700;
+            cursor: pointer;
+            display: flex;
+            align-items: center;
+            gap: 6px;
+            transition: all 0.2s;
+            box-shadow: 0 1px 3px rgba(0, 0, 0, 0.04);
+        }
+
+        .tool-btn:hover {
+            background: var(--primary-subtle);
+            border-color: var(--primary-green);
+            transform: translateY(-1px);
+        }
+
+        select.shelf-select {
+            background: var(--bg-card);
+            color: var(--text-main);
+            border: 1px solid var(--border-color);
+            padding: 7px 12px;
+            border-radius: 10px;
+            font-size: 12px;
+            font-weight: 600;
+            outline: none;
+            cursor: pointer;
+            box-shadow: 0 1px 3px rgba(0, 0, 0, 0.04);
+        }
+
+        select.shelf-select:hover {
+            border-color: var(--primary-green);
+        }
+
+        .shelf-canvas-wrapper {
+            flex: 1;
+            position: relative;
+            background: #0f172a;
+            border-radius: 14px;
+            overflow: auto;
+            border: 1px solid var(--border-color);
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            min-height: 560px;
+            cursor: crosshair;
+        }
+
+        #shelfCanvas {
+            max-width: 100%;
+            display: block;
+        }
+
+        .instruction-banner {
+            position: absolute;
+            bottom: 16px;
+            left: 50%;
+            transform: translateX(-50%);
+            background: rgba(255, 255, 255, 0.95);
+            backdrop-filter: blur(8px);
+            border: 1px solid var(--primary-green);
+            color: #14532d;
+            padding: 8px 20px;
+            border-radius: 999px;
+            font-size: 12px;
+            font-weight: 700;
+            box-shadow: 0 4px 16px rgba(34, 197, 94, 0.2);
+            pointer-events: none;
+            display: flex;
+            align-items: center;
+            gap: 8px;
+        }
+
+        .side-panel {
+            display: flex;
+            flex-direction: column;
+            gap: 18px;
+        }
+
+        .card {
+            background: var(--bg-card);
+            border: 1px solid var(--border-color);
+            border-radius: 18px;
+            padding: 20px;
+            display: flex;
+            flex-direction: column;
+            gap: 14px;
+            box-shadow: var(--shadow-soft);
+        }
+
+        .card-header {
+            font-size: 13px;
+            font-weight: 800;
+            color: #166534;
+            text-transform: uppercase;
+            letter-spacing: 0.6px;
+            display: flex;
+            justify-content: space-between;
+            align-items: center;
+            padding-bottom: 8px;
+            border-bottom: 1px solid var(--border-color);
+        }
+
+        .product-hero {
+            display: flex;
+            gap: 16px;
+            align-items: center;
+        }
+
+        .product-thumb {
+            width: 95px;
+            height: 120px;
+            border-radius: 12px;
+            object-fit: cover;
+            border: 2px solid var(--primary-light);
+            background: #f8fafc;
+            box-shadow: 0 2px 8px rgba(0, 0, 0, 0.06);
+        }
+
+        .product-details {
+            flex: 1;
+            display: flex;
+            flex-direction: column;
+            gap: 6px;
+        }
+
+        .product-name {
+            font-size: 17px;
+            font-weight: 800;
+            line-height: 1.3;
+            color: #0f172a;
+        }
+
+        .product-sku {
+            font-size: 11px;
+            color: #16a34a;
+            font-family: monospace;
+            background: var(--primary-light);
+            padding: 2px 6px;
+            border-radius: 4px;
+            display: inline-block;
+            width: fit-content;
+            font-weight: 700;
+        }
+
+        .product-price {
+            font-size: 26px;
+            font-weight: 800;
+            color: #15803d;
+        }
+
+        .stock-grid {
+            display: grid;
+            grid-template-columns: 1fr 1fr;
+            gap: 10px;
+            margin-top: 4px;
+        }
+
+        .stock-metric {
+            background: var(--primary-subtle);
+            border: 1px solid var(--border-color);
+            border-radius: 12px;
+            padding: 10px 14px;
+        }
+
+        .metric-title {
+            font-size: 11px;
+            color: var(--text-muted);
+            margin-bottom: 4px;
+            font-weight: 600;
+        }
+
+        .metric-val {
+            font-size: 16px;
+            font-weight: 800;
+            color: #15803d;
+        }
+
+        .vis-match-box {
+            width: 100%;
+            height: 180px;
+            background: #0f172a;
+            border-radius: 12px;
+            border: 1px solid var(--border-color);
+            overflow: hidden;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+        }
+
+        .vis-match-img {
+            width: 100%;
+            height: 100%;
+            object-fit: contain;
+        }
+
+        .action-btn {
+            width: 100%;
+            padding: 12px;
+            border: none;
+            border-radius: 12px;
+            background: linear-gradient(135deg, #22c55e, #16a34a);
+            color: white;
+            font-weight: 800;
+            font-size: 14px;
+            cursor: pointer;
+            box-shadow: 0 4px 14px rgba(34, 197, 94, 0.3);
+            transition: all 0.2s;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            gap: 8px;
+        }
+
+        .action-btn:hover {
+            transform: translateY(-1px);
+            box-shadow: 0 6px 20px rgba(34, 197, 94, 0.4);
+        }
+
+        .status-badge {
+            font-size: 11px;
+            padding: 3px 10px;
+            border-radius: 999px;
+            font-weight: 700;
+        }
+        .badge-success { background: #dcfce7; color: #15803d; }
+        .badge-warning { background: #fef3c7; color: #b45309; }
+        .badge-danger { background: #fee2e2; color: #b91c1c; }
+
+        /* Modal Styles */
+        .modal-backdrop {
+            display: none;
+            position: fixed;
+            top: 0;
+            left: 0;
+            width: 100%;
+            height: 100%;
+            background: rgba(15, 23, 42, 0.6);
+            backdrop-filter: blur(4px);
+            z-index: 1000;
+            align-items: center;
+            justify-content: center;
+        }
+
+        .modal-window {
+            background: #ffffff;
+            border-radius: 20px;
+            width: 90%;
+            max-width: 600px;
+            padding: 24px;
+            box-shadow: 0 20px 40px rgba(0, 0, 0, 0.15);
+            display: flex;
+            flex-direction: column;
+            gap: 16px;
+        }
+
+        .modal-header {
+            display: flex;
+            justify-content: space-between;
+            align-items: center;
+        }
+
+        .modal-title {
+            font-size: 18px;
+            font-weight: 800;
+            color: #14532d;
+        }
+
+        .close-btn {
+            background: none;
+            border: none;
+            font-size: 24px;
+            color: var(--text-muted);
+            cursor: pointer;
+        }
+
+        .video-box {
+            width: 100%;
+            height: 380px;
+            background: #000000;
+            border-radius: 12px;
+            overflow: hidden;
+            position: relative;
+        }
+
+        #cameraVideo {
+            width: 100%;
+            height: 100%;
+            object-fit: cover;
+        }
+    </style>
+</head>
+<body>
+    <header>
+        <div class="brand">
+            <!-- Dorayaki SVG Logo -->
+            <svg class="brand-logo-svg" viewBox="0 0 100 100" xmlns="http://www.w3.org/2000/svg">
+                <defs>
+                    <linearGradient id="pancakeTop" x1="0%" y1="0%" x2="0%" y2="100%">
+                        <stop offset="0%" stop-color="#f59e0b"/>
+                        <stop offset="100%" stop-color="#b45309"/>
+                    </linearGradient>
+                    <linearGradient id="ankoFill" x1="0%" y1="0%" x2="0%" y2="100%">
+                        <stop offset="0%" stop-color="#450a0a"/>
+                        <stop offset="100%" stop-color="#2d0606"/>
+                    </linearGradient>
+                    <linearGradient id="pancakeBottom" x1="0%" y1="0%" x2="0%" y2="100%">
+                        <stop offset="0%" stop-color="#d97706"/>
+                        <stop offset="100%" stop-color="#92400e"/>
+                    </linearGradient>
+                </defs>
+                <!-- Bottom Pancake -->
+                <ellipse cx="50" cy="62" rx="42" ry="18" fill="url(#pancakeBottom)" />
+                <!-- Anko (Sweet Red Bean) Layer -->
+                <ellipse cx="50" cy="53" rx="43" ry="14" fill="url(#ankoFill)" />
+                <!-- Top Pancake -->
+                <ellipse cx="50" cy="42" rx="44" ry="22" fill="url(#pancakeTop)" />
+                <!-- Glaze highlight -->
+                <ellipse cx="44" cy="35" rx="20" ry="8" fill="#fde68a" opacity="0.4" />
+                <!-- Cute Leaf Garnish for fresh green theme -->
+                <path d="M 52,18 C 65,12 72,25 65,30 C 58,28 54,23 52,18 Z" fill="#22c55e" />
+            </svg>
+            <div>
+                <div class="brand-title">
+                    Dorayaki <span class="brand-jp">どら焼き</span>
+                </div>
+                <div class="brand-sub" id="t-subtitle">Smart Supermarket Shelf AI & Inventory Recognition</div>
+            </div>
+        </div>
+
+        <div class="header-actions">
+            <button class="lang-switch-btn" onclick="toggleLanguage()">
+                <span id="langIcon">🌐</span> <span id="langText">日本語 / JA</span>
+            </button>
+            <div class="agent-pills">
+                <div class="pill"><span class="dot"></span> PM</div>
+                <div class="pill"><span class="dot"></span> AI</div>
+                <div class="pill"><span class="dot"></span> Dev</div>
+                <div class="pill"><span class="dot"></span> Reviewer</div>
+            </div>
+        </div>
+    </header>
+
+    <main>
+        <div class="shelf-panel">
+            <div class="toolbar">
+                <div class="toolbar-group">
+                    <span style="font-size: 12px; font-weight: 700; color: #166534;" id="t-method-label">Algorithm:</span>
+                    <div class="btn-group">
+                        <button class="method-btn active" onclick="setMethod('hybrid')">Hybrid (Paper)</button>
+                        <button class="method-btn" onclick="setMethod('surf')">SURF</button>
+                        <button class="method-btn" onclick="setMethod('orb')">ORB</button>
+                        <button class="method-btn" onclick="setMethod('brisk')">BRISK</button>
+                    </div>
+                </div>
+
+                <div class="toolbar-group">
+                    <select id="shelfSelect" class="shelf-select" onchange="loadSelectedShelf()">
+                        <!-- Populated dynamically via API -->
+                    </select>
+
+                    <!-- Empty Shelf Slots Toggle -->
+                    <button id="emptySlotsToggle" class="tool-btn" style="border-color: #fca5a5; background: #fef2f2; color: #b91c1c; font-weight: 700;" onclick="toggleEmptySlots()">
+                        <span>🔴</span> <span id="t-empty-btn">Empty Spaces</span>
+                    </button>
+
+                    <!-- Camera Snapshot Button -->
+                    <button class="tool-btn" onclick="openCameraModal()">
+                        <span>📷</span> <span id="t-camera-btn">Capture Photo</span>
+                    </button>
+
+                    <!-- File Upload Button -->
+                    <button class="tool-btn" onclick="triggerFileInput()">
+                        <span>📁</span> <span id="t-upload-btn">Upload Image</span>
+                    </button>
+                    <input type="file" id="fileInput" accept="image/*" style="display: none;" onchange="handleFileUpload(event)">
+                </div>
+            </div>
+
+            <div class="shelf-canvas-wrapper" id="canvasWrapper">
+                <canvas id="shelfCanvas"></canvas>
+                <div class="instruction-banner">
+                    <span>👆 <span id="t-instruction">Click on any product packaging to identify & view real-time inventory</span></span>
+                </div>
+            </div>
+        </div>
+
+        <div class="side-panel">
+            <div class="card" id="productCard">
+                <div class="card-header">
+                    <span id="t-card-title">Product Details</span>
+                    <span id="matchStatusBadge" class="status-badge badge-success">Ready</span>
+                </div>
+
+                <div id="cardContentPlaceholder" style="text-align: center; padding: 45px 10px; color: var(--text-muted); font-size: 13px;">
+                    <div style="font-size: 36px; margin-bottom: 10px;">🏷️</div>
+                    <span id="t-placeholder-text">No product selected.<br>Click any merchandise item on the shelf image to inspect details.</span>
+                </div>
+
+                <div id="cardContentDetails" style="display: none; flex-direction: column; gap: 14px;">
+                    <div class="product-hero">
+                        <img id="productThumb" class="product-thumb" src="" alt="Thumbnail">
+                        <div class="product-details">
+                            <span id="productSku" class="product-sku">SKU_001</span>
+                            <div id="productName" class="product-name">Product Name</div>
+                            <div id="productPrice" class="product-price">¥0</div>
+                        </div>
+                    </div>
+
+                    <div class="stock-grid">
+                        <div class="stock-metric">
+                            <div class="metric-title" id="t-metric-on">On Shelf (In Stock)</div>
+                            <div id="metricOn" class="metric-val">0</div>
+                        </div>
+                        <div class="stock-metric">
+                            <div class="metric-title" id="t-metric-out">Sold / Dispatched</div>
+                            <div id="metricOut" class="metric-val">0</div>
+                        </div>
+                        <div class="stock-metric">
+                            <div class="metric-title" id="t-metric-receipt">Receipt Date</div>
+                            <div id="metricReceipt" class="metric-val" style="font-size: 13px;">--</div>
+                        </div>
+                        <div class="stock-metric">
+                            <div class="metric-title" id="t-metric-expiry">Expiration Date</div>
+                            <div id="metricExpiry" class="metric-val" style="font-size: 13px; color: #b45309;">--</div>
+                        </div>
+                    </div>
+
+                    <button class="action-btn" onclick="purchaseOne()">
+                        <span>🛒</span> <span id="t-dispense-btn">Dispense / Purchase 1 Unit</span>
+                    </button>
+                </div>
+            </div>
+
+            <div class="card">
+                <div class="card-header">
+                    <span id="t-match-title">Feature Matching Inliers</span>
+                    <span id="scoreLabel" style="font-size: 12px; color: #16a34a; font-weight: 700;">--</span>
+                </div>
+                <div class="vis-match-box" id="visMatchBox">
+                    <span style="color: #94a3b8; font-size: 12px;" id="t-vis-placeholder">Visual keypoint matching will appear here</span>
+                </div>
+            </div>
+        </div>
+    </main>
+
+    <!-- Camera Capture Modal -->
+    <div class="modal-backdrop" id="cameraModal">
+        <div class="modal-window">
+            <div class="modal-header">
+                <div class="modal-title" id="t-modal-camera-title">📷 Take Shelf Photo</div>
+                <button class="close-btn" onclick="closeCameraModal()">&times;</button>
+            </div>
+            <div class="video-box">
+                <video id="cameraVideo" autoplay playsinline></video>
+            </div>
+            <button class="action-btn" onclick="captureSnapshot()">
+                <span>📸</span> <span id="t-take-photo-btn">Capture & Run Detection</span>
+            </button>
+        </div>
+    </div>
+
+    <script>
+        let currentLang = 'en'; // 'en' or 'ja'
+        let currentMethod = 'hybrid';
+        let detectedBoxes = [];
+        let emptySlots = [];
+        let showEmptySlots = true;
+        let selectedBoxId = null;
+        let selectedEmptyId = null;
+        let shelfImageObj = new Image();
+        let currentProductData = null;
+        let mediaStream = null;
+
+        const translations = {
+            en: {
+                subtitle: "Smart Supermarket Shelf AI & Inventory Recognition",
+                langToggle: "日本語 / JA",
+                methodLabel: "Algorithm:",
+                cameraBtn: "Capture Photo",
+                uploadBtn: "Upload Image",
+                emptyBtn: "Empty Spaces",
+                instruction: "Click on any product packaging or red empty slot to inspect details",
+                cardTitle: "Product Details",
+                emptySlotCardTitle: "Empty Shelf Space",
+                emptySlotBadge: "OUT OF STOCK",
+                placeholderText: "No product selected.<br>Click any merchandise item on the shelf image to inspect details.",
+                metricOn: "On Shelf (In Stock)",
+                metricOut: "Sold / Dispatched",
+                metricReceipt: "Receipt Date",
+                metricExpiry: "Expiration Date",
+                dispenseBtn: "Dispense / Purchase 1 Unit",
+                matchTitle: "Feature Matching Inliers",
+                visPlaceholder: "Visual keypoint matching will appear here",
+                cameraModalTitle: "📷 Take Shelf Photo",
+                takePhotoBtn: "Capture & Run Detection",
+                statusReady: "Ready",
+                statusAnalyzing: "Analyzing...",
+                statusRecognized: "Recognized",
+                statusNotFound: "Not Found"
+            },
+            ja: {
+                subtitle: "スマートスーパーマーケット棚認識 & 在庫管理AI",
+                langToggle: "English / EN",
+                methodLabel: "分類手法:",
+                cameraBtn: "カメラ撮影",
+                uploadBtn: "画像アップロード",
+                emptyBtn: "空きスペース",
+                instruction: "棚の画像上の商品または赤枠の空きスペースをクリックして詳細を表示します",
+                cardTitle: "商品情報",
+                emptySlotCardTitle: "棚の空きスペース (欠品)",
+                emptySlotBadge: "欠品 (在庫切れ)",
+                placeholderText: "商品が選択されていません。<br>棚画像上の商品をクリックしてください。",
+                metricOn: "陳列在庫数 (棚上)",
+                metricOut: "販売済 / 出庫数",
+                metricReceipt: "受入日",
+                metricExpiry: "賞味期限",
+                dispenseBtn: "1点出庫 / 購入する",
+                matchTitle: "特徴点マッチング結果",
+                visPlaceholder: "ここに特徴点の一致描画が表示されます",
+                cameraModalTitle: "📷 棚の写真を撮影",
+                takePhotoBtn: "撮影して物体検出を実行",
+                statusReady: "待機中",
+                statusAnalyzing: "解析中...",
+                statusRecognized: "認識完了",
+                statusNotFound: "未検出"
+            }
+        };
+
+        function toggleLanguage() {
+            currentLang = (currentLang === 'en') ? 'ja' : 'en';
+            applyLanguage();
+        }
+
+        function applyLanguage() {
+            const t = translations[currentLang];
+            document.getElementById('t-subtitle').innerText = t.subtitle;
+            document.getElementById('langText').innerText = t.langToggle;
+            document.getElementById('t-method-label').innerText = t.methodLabel;
+            document.getElementById('t-camera-btn').innerText = t.cameraBtn;
+            document.getElementById('t-upload-btn').innerText = t.uploadBtn;
+            document.getElementById('t-empty-btn').innerText = `${t.emptyBtn} (${emptySlots.length})`;
+            document.getElementById('t-instruction').innerText = t.instruction;
+            document.getElementById('t-card-title').innerText = t.cardTitle;
+            document.getElementById('t-placeholder-text').innerHTML = t.placeholderText;
+            document.getElementById('t-metric-on').innerText = t.metricOn;
+            document.getElementById('t-metric-out').innerText = t.metricOut;
+            document.getElementById('t-metric-receipt').innerText = t.metricReceipt;
+            document.getElementById('t-metric-expiry').innerText = t.metricExpiry;
+            document.getElementById('t-dispense-btn').innerText = t.dispenseBtn;
+            document.getElementById('t-match-title').innerText = t.matchTitle;
+            document.getElementById('t-vis-placeholder').innerText = t.visPlaceholder;
+            document.getElementById('t-modal-camera-title').innerText = t.cameraModalTitle;
+            document.getElementById('t-take-photo-btn').innerText = t.takePhotoBtn;
+        }
+
+        function toggleEmptySlots() {
+            showEmptySlots = !showEmptySlots;
+            const btn = document.getElementById('emptySlotsToggle');
+            if (showEmptySlots) {
+                btn.style.background = '#fef2f2';
+                btn.style.color = '#b91c1c';
+                btn.style.borderColor = '#fca5a5';
+            } else {
+                btn.style.background = '#ffffff';
+                btn.style.color = '#64748b';
+                btn.style.borderColor = '#e2e8f0';
+            }
+            drawCanvas();
+        }
+
+        const canvas = document.getElementById('shelfCanvas');
+        const ctx = canvas.getContext('2d');
+
+        function setMethod(method) {
+            currentMethod = method;
+            document.querySelectorAll('.method-btn').forEach(b => b.classList.remove('active'));
+            event.target.classList.add('active');
+            if (selectedBoxId !== null) {
+                const b = detectedBoxes.find(x => x.id === selectedBoxId);
+                if (b) queryClick(b.center[0], b.center[1]);
+            }
+        }
+
+        async function initSampleList() {
+            try {
+                const res = await fetch('/api/shelf_samples');
+                const data = await res.json();
+                const select = document.getElementById('shelfSelect');
+                select.innerHTML = '';
+                data.samples.forEach(item => {
+                    const opt = document.createElement('option');
+                    opt.value = item.path;
+                    opt.innerText = item.label;
+                    if (item.path === 'image.jpg') opt.selected = true;
+                    select.appendChild(opt);
+                });
+                loadSelectedShelf();
+            } catch (e) {
+                console.error(e);
+            }
+        }
+
+        async function loadSelectedShelf() {
+            const path = document.getElementById('shelfSelect').value;
+            const res = await fetch('/api/load_shelf', {
+                method: 'POST',
+                headers: {'Content-Type': 'application/json'},
+                body: JSON.stringify({ image_path: path })
+            });
+            const data = await res.json();
+            if (data.status === 'success') {
+                detectedBoxes = data.boxes;
+                emptySlots = data.empty_slots || [];
+                selectedBoxId = null;
+                selectedEmptyId = null;
+                document.getElementById('t-empty-btn').innerText = `${translations[currentLang].emptyBtn} (${emptySlots.length})`;
+                shelfImageObj.onload = () => {
+                    canvas.width = shelfImageObj.width;
+                    canvas.height = shelfImageObj.height;
+                    drawCanvas();
+                };
+                shelfImageObj.src = data.image_base64;
+            }
+        }
+
+        function triggerFileInput() {
+            document.getElementById('fileInput').click();
+        }
+
+        async function handleFileUpload(e) {
+            const file = e.target.files[0];
+            if (!file) return;
+            const reader = new FileReader();
+            reader.onload = async function(evt) {
+                const base64Data = evt.target.result;
+                await uploadAndDetect(base64Data);
+            };
+            reader.readAsDataURL(file);
+        }
+
+        async function uploadAndDetect(base64Data) {
+            document.getElementById('matchStatusBadge').innerText = translations[currentLang].statusAnalyzing;
+            document.getElementById('matchStatusBadge').className = 'status-badge badge-warning';
+
+            const res = await fetch('/api/upload_shelf', {
+                method: 'POST',
+                headers: {'Content-Type': 'application/json'},
+                body: JSON.stringify({ image_base64: base64Data })
+            });
+            const data = await res.json();
+            if (data.status === 'success') {
+                detectedBoxes = data.boxes;
+                emptySlots = data.empty_slots || [];
+                selectedBoxId = null;
+                selectedEmptyId = null;
+                document.getElementById('t-empty-btn').innerText = `${translations[currentLang].emptyBtn} (${emptySlots.length})`;
+                shelfImageObj.onload = () => {
+                    canvas.width = shelfImageObj.width;
+                    canvas.height = shelfImageObj.height;
+                    drawCanvas();
+                };
+                shelfImageObj.src = data.image_base64;
+                document.getElementById('matchStatusBadge').innerText = translations[currentLang].statusReady;
+                document.getElementById('matchStatusBadge').className = 'status-badge badge-success';
+            }
+        }
+
+        // Camera Modal Functions
+        async function openCameraModal() {
+            const modal = document.getElementById('cameraModal');
+            modal.style.display = 'flex';
+            try {
+                mediaStream = await navigator.mediaDevices.getUserMedia({ video: { width: 1280, height: 720 } });
+                document.getElementById('cameraVideo').srcObject = mediaStream;
+            } catch (err) {
+                alert("Camera access failed: " + err.message);
+                closeCameraModal();
+            }
+        }
+
+        function closeCameraModal() {
+            const modal = document.getElementById('cameraModal');
+            modal.style.display = 'none';
+            if (mediaStream) {
+                mediaStream.getTracks().forEach(track => track.stop());
+                mediaStream = null;
+            }
+        }
+
+        async function captureSnapshot() {
+            const video = document.getElementById('cameraVideo');
+            const offscreen = document.createElement('canvas');
+            offscreen.width = video.videoWidth || 1280;
+            offscreen.height = video.videoHeight || 720;
+            const offCtx = offscreen.getContext('2d');
+            offCtx.drawImage(video, 0, 0, offscreen.width, offscreen.height);
+            const dataUrl = offscreen.toDataURL('image/jpeg', 0.9);
+            closeCameraModal();
+            await uploadAndDetect(dataUrl);
+        }
+
+        function drawCanvas() {
+            ctx.clearRect(0, 0, canvas.width, canvas.height);
+            ctx.drawImage(shelfImageObj, 0, 0);
+
+            // 1. Detected Products: Clean bounding boxes without class labels or confidence text
+            detectedBoxes.forEach(b => {
+                const [x1, y1, x2, y2] = b.box;
+                const isSelected = (b.id === selectedBoxId);
+
+                ctx.lineWidth = isSelected ? 4 : 2;
+                ctx.strokeStyle = isSelected ? '#10b981' : '#22c55e';
+                ctx.fillStyle = isSelected ? 'rgba(16, 185, 129, 0.25)' : 'rgba(34, 197, 94, 0.05)';
+
+                ctx.fillRect(x1, y1, x2 - x1, y2 - y1);
+                ctx.strokeRect(x1, y1, x2 - x1, y2 - y1);
+            });
+
+            // 2. Empty Shelf Spaces: Clean RED bounding boxes
+            if (showEmptySlots && emptySlots && emptySlots.length > 0) {
+                emptySlots.forEach(slot => {
+                    const [x1, y1, x2, y2] = slot.box;
+                    const isSelected = (slot.id === selectedEmptyId);
+
+                    ctx.lineWidth = isSelected ? 4 : 3;
+                    ctx.strokeStyle = '#ef4444'; // Red bounding box
+                    ctx.fillStyle = isSelected ? 'rgba(239, 68, 68, 0.38)' : 'rgba(239, 68, 68, 0.22)';
+
+                    ctx.fillRect(x1, y1, x2 - x1, y2 - y1);
+                    ctx.strokeRect(x1, y1, x2 - x1, y2 - y1);
+
+                    // Red header tag
+                    const tagText = `EMPTY (~${slot.missing_items_est})`;
+                    ctx.font = 'bold 11px Plus Jakarta Sans, sans-serif';
+                    const textW = ctx.measureText(tagText).width;
+                    const tagH = 16;
+                    const tagY = Math.max(0, y1 - tagH - 2);
+                    ctx.fillStyle = '#ef4444';
+                    ctx.fillRect(x1, tagY, textW + 8, tagH);
+                    ctx.fillStyle = '#ffffff';
+                    ctx.fillText(tagText, x1 + 4, tagY + 12);
+                });
+            }
+        }
+
+        canvas.addEventListener('click', (e) => {
+            const rect = canvas.getBoundingClientRect();
+            const scaleX = canvas.width / rect.width;
+            const scaleY = canvas.height / rect.height;
+
+            const clickX = Math.round((e.clientX - rect.left) * scaleX);
+            const clickY = Math.round((e.clientY - rect.top) * scaleY);
+
+            queryClick(clickX, clickY);
+        });
+
+        async function queryClick(x, y) {
+            document.getElementById('matchStatusBadge').innerText = translations[currentLang].statusAnalyzing;
+            document.getElementById('matchStatusBadge').className = 'status-badge badge-warning';
+
+            const res = await fetch('/api/click', {
+                method: 'POST',
+                headers: {'Content-Type': 'application/json'},
+                body: JSON.stringify({ x: x, y: y, method: currentMethod })
+            });
+            const data = await res.json();
+
+            if (data.status === 'success') {
+                selectedBoxId = data.target_box.id;
+                selectedEmptyId = null;
+                drawCanvas();
+
+                currentProductData = data.inventory;
+                document.getElementById('cardContentPlaceholder').style.display = 'none';
+                document.getElementById('cardContentDetails').style.display = 'flex';
+
+                document.getElementById('productSku').innerText = data.inventory.sku_id;
+                document.getElementById('productName').innerText = data.inventory.product_name;
+                document.getElementById('productPrice').innerText = '¥' + data.inventory.price;
+                document.getElementById('productPrice').style.color = 'var(--primary-dark)';
+                document.getElementById('productThumb').src = data.crop_base64;
+
+                document.getElementById('metricOn').innerText = data.inventory.number_on;
+                document.getElementById('metricOut').innerText = data.inventory.number_out;
+                document.getElementById('metricReceipt').innerText = data.inventory.receipt_date;
+                document.getElementById('metricExpiry').innerText = data.inventory.expiration_date;
+
+                document.getElementById('scoreLabel').innerText = `Score: ${data.classification.score} | Inliers: ${(data.classification.confidence * 100).toFixed(1)}%`;
+                document.getElementById('matchStatusBadge').innerText = translations[currentLang].statusRecognized;
+                document.getElementById('matchStatusBadge').className = 'status-badge badge-success';
+
+                if (data.vis_base64) {
+                    document.getElementById('visMatchBox').innerHTML = `<img class="vis-match-img" src="${data.vis_base64}">`;
                 }
-            )
-        except BaseException:
-            session.close()
-            async with sessions_lock:
-                sessions.pop(session_id, None)
-            raise
+            } else if (data.status === 'empty_slot') {
+                selectedBoxId = null;
+                selectedEmptyId = data.empty_slot.id;
+                drawCanvas();
 
-        async def processing_loop() -> None:
-            while not session.closed:
-                pending = await session.take_pending_async()
-                if pending is None:
-                    continue
-                frame_id, payload = pending
-                try:
-                    result = await loop.run_in_executor(None, session.process_frame, frame_id, payload)
-                except UndecodableImage:
-                    await send({"type": "error", "code": "UNDECODABLE_IMAGE", "frame_id": frame_id,
-                                "message": "frame is not a readable image"})
-                    continue
-                except Exception as exc:  # keep the stream alive on one bad frame
-                    LOGGER.exception("live frame failed: %s", exc)
-                    await send({"type": "error", "code": "INTERNAL", "frame_id": frame_id,
-                                "message": "frame processing failed"})
-                    continue
-                if recorder is not None:
-                    # Disk writes must not sit on the event loop between results.
-                    await loop.run_in_executor(
-                        None, recorder.write, session_id, frame_id, payload, result
-                    )
-                await send(result)
+                currentProductData = null;
+                document.getElementById('cardContentPlaceholder').style.display = 'none';
+                document.getElementById('cardContentDetails').style.display = 'flex';
 
-        async def handle_inspect(message: Dict[str, Any]) -> None:
-            request_id = message.get("request_id")
-            try:
-                track_id = int(message.get("track_id"))
-            except (TypeError, ValueError):
-                await send({"type": "error", "code": "BAD_MESSAGE", "request_id": request_id,
-                            "message": "track_id required"})
-                return
-            method = message.get("method")
-            if method is not None:
-                try:
-                    method = _require_method(method)
-                except HTTPException as exc:
-                    await send({"type": "error", "code": "BAD_MESSAGE", "request_id": request_id,
-                                "message": str(exc.detail)})
-                    return
-            try:
-                result = await loop.run_in_executor(None, session.inspect, track_id, method)
-            except KeyError:
-                await send({"type": "error", "code": "TRACK_NOT_IN_VIEW", "request_id": request_id,
-                            "message": "track is not in the latest frame"})
-                return
-            except Exception as exc:
-                LOGGER.exception("inspect failed: %s", exc)
-                await send({"type": "error", "code": "INTERNAL", "request_id": request_id,
-                            "message": "inspect failed"})
-                return
-            payload = {"type": "inspect_result", "request_id": request_id, "track_id": track_id}
-            payload.update(result)
-            await send(payload)
+                document.getElementById('productSku').innerText = data.empty_slot.id.toUpperCase();
+                document.getElementById('productName').innerText = `${translations[currentLang].emptySlotCardTitle} (Row ${data.empty_slot.row_index})`;
+                document.getElementById('productPrice').innerText = 'OUT OF STOCK';
+                document.getElementById('productPrice').style.color = '#dc2626';
+                document.getElementById('productThumb').src = "data:image/svg+xml;utf8,<svg xmlns='http://www.w3.org/2000/svg' width='80' height='80' viewBox='0 0 80 80'><rect width='80' height='80' rx='12' fill='%23fee2e2'/><text x='50%' y='55%' dominant-baseline='middle' text-anchor='middle' font-size='32'>🔴</text></svg>";
 
-        async def handle_capabilities(message: Dict[str, Any]) -> None:
-            """A client that can answer with a full-resolution crop says so here.
+                document.getElementById('metricOn').innerText = '0 (Empty)';
+                document.getElementById('metricOut').innerText = `~${data.empty_slot.missing_items_est} items`;
+                document.getElementById('metricReceipt').innerText = `Width: ${data.empty_slot.width}px`;
+                document.getElementById('metricExpiry').innerText = `Height: ${data.empty_slot.height}px`;
 
-            Until this arrives the session keeps the protocol-v1 behaviour: no
-            result carries a crop request, so a v1 client is never asked. A
-            capability is believed whole or not at all: a message that does not
-            name the lane with a limit the server can honour is refused and the
-            lane stays off, rather than half-applied on the client's say-so.
-            """
-            if message.get("crop") is not True or "max_crop_bytes" not in message:
-                await send({"type": "error", "code": "BAD_MESSAGE",
-                            "message": "crop must be true and state max_crop_bytes"})
-                return
-            try:
-                capabilities = session.enable_crops(message["max_crop_bytes"])
-            except ValueError:
-                await send({"type": "error", "code": "BAD_MESSAGE",
-                            "message": "max_crop_bytes must be 1..%d" % MAX_CROP_BYTES})
-                return
-            await send({"type": "capabilities_ack", "capabilities": capabilities})
+                document.getElementById('scoreLabel').innerText = `Gap Width: ${data.empty_slot.width}px | Missing: ~${data.empty_slot.missing_items_est} units`;
+                document.getElementById('matchStatusBadge').innerText = translations[currentLang].emptySlotBadge;
+                document.getElementById('matchStatusBadge').className = 'status-badge badge-danger';
 
-        async def handle_crop(message: Dict[str, Any]) -> None:
-            """Score a crop the client returned for a track we asked about.
+                document.getElementById('visMatchBox').innerHTML = `<div style="text-align: center; padding: 24px 10px; color: #dc2626; font-size: 13px; font-weight: 600; background: #fef2f2; border-radius: 8px; border: 1px dashed #fca5a5;">⚠️ ${data.message}</div>`;
+            } else {
+                document.getElementById('matchStatusBadge').innerText = translations[currentLang].statusNotFound;
+                document.getElementById('matchStatusBadge').className = 'status-badge badge-warning';
+            }
+        }
 
-            base64 travels in a text control message, so the binary frame path
-            (4-byte id + JPEG) is untouched and the frame id space is unchanged.
-            """
-            request_id = message.get("request_id")
-            encoded = message.get("jpeg")
-            if not isinstance(encoded, str) or not encoded:
-                await send({"type": "crop_error", "request_id": request_id,
-                            "code": "CROP_BAD_MESSAGE", "message": "jpeg is required"})
-                return
-            # Base64 expands ~4/3. Refuse on the wire length before decoding it,
-            # the same way the REST frame route does.
-            if len(encoded) > (MAX_CROP_BYTES * 4) // 3 + 8:
-                await send({"type": "crop_error", "request_id": request_id,
-                            "code": "CROP_TOO_LARGE", "message": "crop over the limit"})
-                return
-            try:
-                crop_bytes = base64.b64decode(encoded, validate=True)
-            except (binascii.Error, ValueError):
-                await send({"type": "crop_error", "request_id": request_id,
-                            "code": "CROP_BAD_MESSAGE", "message": "jpeg is not valid base64"})
-                return
-            dimensions = _jpeg_dimensions(crop_bytes)
-            if dimensions is not None and dimensions[0] * dimensions[1] > MAX_CROP_PIXELS:
-                await send({"type": "crop_error", "request_id": request_id,
-                            "code": "CROP_TOO_LARGE", "message": "crop dimensions exceed the limit"})
-                return
-            reply = await loop.run_in_executor(
-                None, session.submit_crop, request_id, message.get("track_id"),
-                message.get("frame_id"), crop_bytes,
-            )
-            await send(reply)
+        async function purchaseOne() {
+            if (!currentProductData) return;
+            const res = await fetch('/api/update_stock', {
+                method: 'POST',
+                headers: {'Content-Type': 'application/json'},
+                body: JSON.stringify({ sku_id: currentProductData.sku_id, delta: -1 })
+            });
+            const data = await res.json();
+            if (data.status === 'success') {
+                document.getElementById('metricOn').innerText = data.number_on;
+                document.getElementById('metricOut').innerText = data.number_out;
+            }
+        }
 
-        processing = asyncio.create_task(processing_loop())
+        // Initialize on load
+        window.addEventListener('DOMContentLoaded', () => {
+            initSampleList();
+            applyLanguage();
+        });
+    </script>
+</body>
+</html>
+"""
 
-        authed = not settings.token or bearer_matches(
-            websocket.headers.get("authorization"), settings.token
-        )
+    @app.get("/", response_class=HTMLResponse)
+    async def index():
+        return HTMLResponse(content=HTML_PAGE)
 
-        async def handle_control(message: Dict[str, Any]) -> bool:
-            """Apply one control message. False closes the socket (auth failed)."""
-            nonlocal authed
-            kind = message.get("type")
-            if not authed:
-                presented = message.get("token") if kind == "auth" else None
-                if isinstance(presented, str) and token_matches(presented, settings.token):
-                    authed = True
-                    return True
-                await websocket.close(code=1008)
-                return False
-            if kind == "auth":
-                return True
-            if kind == "config":
-                try:
-                    config = session.configure(
-                        method=message.get("method"),
-                        conf=message.get("conf"),
-                        empty_slots=message.get("empty_slots"),
-                        tags=message.get("tags"),
-                        ocr=message.get("ocr"),
-                    )
-                except (TypeError, ValueError):
-                    await send({"type": "error", "code": "BAD_MESSAGE", "message": "bad config values"})
-                    return True
-                await send({"type": "config_ack", "config": config})
-            elif kind == "reset":
-                session.reset()
-                await send({"type": "reset_ack"})
-            elif kind == "ping":
-                await send({"type": "pong", "t": message.get("t")})
-            elif kind == "inspect":
-                await handle_inspect(message)
-            elif kind == "capabilities":
-                await handle_capabilities(message)
-            elif kind == "crop":
-                await handle_crop(message)
-            else:
-                await send({"type": "error", "code": "BAD_MESSAGE", "message": f"unknown type '{kind}'"})
-            return True
+    @app.get("/api/shelf_samples")
+    async def get_shelf_samples():
+        samples = get_available_shelf_images("add")
+        return {"status": "success", "samples": samples}
 
-        try:
-            while True:
-                try:
-                    message = await asyncio.wait_for(websocket.receive(), timeout=WS_IDLE_TIMEOUT_S)
-                except asyncio.TimeoutError:
-                    break
-                if message.get("type") == "websocket.disconnect":
-                    break
-                text = message.get("text")
-                data = message.get("bytes")
-                if data is not None:
-                    if not authed:
-                        await websocket.close(code=1008)
-                        break
-                    if len(data) < 5:
-                        await send({"type": "error", "code": "BAD_FRAME", "message": "frame shorter than 5 bytes"})
-                        continue
-                    if len(data) > MAX_FRAME_BYTES + 4:
-                        await send({"type": "error", "code": "PAYLOAD_TOO_LARGE", "message": "frame too large"})
-                        continue
-                    frame_id = int.from_bytes(data[:4], "big")
-                    session.submit_frame(frame_id, data[4:])
-                elif text is not None:
-                    try:
-                        parsed = json.loads(text)
-                        if not isinstance(parsed, dict):
-                            raise ValueError("not an object")
-                    except (json.JSONDecodeError, ValueError):
-                        await send({"type": "error", "code": "BAD_MESSAGE", "message": "invalid JSON"})
-                        continue
-                    if not await handle_control(parsed):
-                        break
-        except WebSocketDisconnect:
-            pass
-        finally:
-            session.close()
-            processing.cancel()
-            try:
-                await processing
-            except (asyncio.CancelledError, Exception):
-                pass
-            async with sessions_lock:
-                sessions.pop(session_id, None)
+    @app.post("/api/load_shelf")
+    async def load_shelf(payload: dict):
+        img_path = payload.get("image_path", "image.jpg")
+        init_res = pipeline.set_active_shelf_image(img_path)
+        if init_res.get("status") == "success":
+            return {
+                "status": "success",
+                "boxes": init_res["boxes"],
+                "empty_slots": init_res.get("empty_slots", []),
+                "image_base64": encode_image_base64(pipeline.current_image)
+            }
+        return JSONResponse(status_code=400, content={"status": "error", "message": "Failed to load image"})
 
-    # ---------------------------------------------------------------- errors
-    @app.exception_handler(RequestValidationError)
-    async def validation_exception_handler(request: Request, exc: RequestValidationError) -> JSONResponse:
-        first = (exc.errors() or [{}])[0]
-        location = ".".join(str(part) for part in first.get("loc", []) if part != "body")
-        message = first.get("msg", "invalid request")
-        detail = f"{location}: {message}" if location else message
-        return JSONResponse(
-            status_code=422, content={"error": {"code": "VALIDATION_ERROR", "message": detail}}
-        )
+    @app.post("/api/upload_shelf")
+    async def upload_shelf(payload: dict):
+        data_url = payload.get("image_base64", "")
+        img_bgr = decode_image_base64(data_url)
+        if img_bgr is None:
+            return JSONResponse(status_code=400, content={"status": "error", "message": "Failed to decode image"})
 
-    @app.exception_handler(HTTPException)
-    async def http_exception_handler(request: Request, exc: HTTPException) -> JSONResponse:
-        detail = exc.detail
-        if isinstance(detail, dict) and "error" in detail:
-            body = detail
-        else:
-            body = {"error": {"code": "HTTP_ERROR", "message": str(detail)}}
-        return JSONResponse(status_code=exc.status_code, content=body)
+        init_res = pipeline.set_active_shelf_image(img_bgr)
+        if init_res.get("status") == "success":
+            return {
+                "status": "success",
+                "boxes": init_res["boxes"],
+                "empty_slots": init_res.get("empty_slots", []),
+                "image_base64": encode_image_base64(pipeline.current_image)
+            }
+        return JSONResponse(status_code=400, content={"status": "error", "message": "Detection failed"})
 
-    @app.exception_handler(Exception)
-    async def unhandled_exception_handler(request: Request, exc: Exception) -> JSONResponse:
-        LOGGER.exception("unhandled error on %s: %s", request.url.path, exc)
-        return JSONResponse(
-            status_code=500, content={"error": {"code": "INTERNAL", "message": "internal server error"}}
-        )
+    @app.post("/api/click")
+    async def handle_click(payload: dict):
+        x = int(payload.get("x", 0))
+        y = int(payload.get("y", 0))
+        method = payload.get("method", "hybrid")
 
-    # ---------------------------------------------------------------- static web client
-    resolved_web_dir = web_dir or os.path.join(current_dir, "web")
-    app.add_middleware(BodyLimit, max_bytes=MAX_UPLOAD_BYTES)
-    if os.path.isdir(resolved_web_dir):
-        app.mount("/", StaticFiles(directory=resolved_web_dir, html=True), name="web")
+        res = pipeline.process_click(x, y, method=method)
+        if res.get("status") == "success":
+            return {
+                "status": "success",
+                "target_box": res["target_box"],
+                "classification": res["classification"],
+                "inventory": res["inventory"],
+                "crop_base64": encode_image_base64(res["crop_image"]),
+                "vis_base64": encode_image_base64(res["match_visualization"])
+            }
+        elif res.get("status") == "empty_slot":
+            return {
+                "status": "empty_slot",
+                "empty_slot": res["empty_slot"],
+                "message": res["message"]
+            }
+        return JSONResponse(status_code=404, content=res)
+
+    @app.post("/api/update_stock")
+    async def update_stock(payload: dict):
+        sku_id = payload.get("sku_id")
+        delta = int(payload.get("delta", -1))
+        ok = pipeline.inventory.update_stock(sku_id, delta)
+        if ok:
+            p = pipeline.inventory.get_product(sku_id)
+            return {"status": "success", "number_on": p["number_on"], "number_out": p["number_out"]}
+        return JSONResponse(status_code=400, content={"status": "error"})
 
     return app
 
 
-# ---------------------------------------------------------------------------
-# entry point
-# ---------------------------------------------------------------------------
-def main() -> None:
-    parser = argparse.ArgumentParser(description="Dorayaki - shelf recognition server")
-    parser.add_argument("--host", type=str, default=None)
-    parser.add_argument("--port", type=int, default=None)
-    parser.add_argument("--device", type=str, default=None, help="cpu | mps | cuda | cuda:0")
-    parser.add_argument("--imgsz", type=int, default=None)
-    parser.add_argument("--weights", type=str, default=None)
-    parser.add_argument("--stock-dir", type=str, default=None)
-    parser.add_argument("--classify-workers", type=int, default=None)
-    parser.add_argument("--live-method", type=str, default=None, choices=list(CLASSIFY_METHODS))
-    parser.add_argument("--live-conf", type=float, default=None)
-    parser.add_argument("--identity", type=str, default=None,
-                        choices=["matcher", "local", "gallery", "van"],
-                        help="which mechanism names products (default local; "
-                             "van is the always-argmax baseline)")
-    parser.add_argument("--live-inflight", type=int, default=None,
-                        help="classifications in flight per session")
-    parser.add_argument("--live-attempts", type=int, default=None,
-                        help="classification attempts per track")
-    parser.add_argument("--record-frames", type=str, default=None,
-                        help="write processed frames and their results under this directory")
-    parser.add_argument("--record-every", type=int, default=None,
-                        help="record one frame in every N (default 3)")
-    parser.add_argument("--no-tags", action="store_true", help="disable price-tag OCR")
-    parser.add_argument("--max-sessions", type=int, default=None)
-    parser.add_argument("--ssl-certfile", type=str, default=None)
-    parser.add_argument("--ssl-keyfile", type=str, default=None)
-    parser.add_argument("--log-level", type=str, default=None)
+def main():
+    parser = argparse.ArgumentParser(description="Dorayaki - Smart Supermarket Shelf AI")
+    parser.add_argument("--port", type=int, default=8080)
+    parser.add_argument("--host", type=str, default="0.0.0.0")
     args = parser.parse_args()
 
-    logging.basicConfig(
-        level=(args.log_level or server_settings_from_env().log_level or "info").upper(),
-        format="%(asctime)s %(levelname)s %(name)s: %(message)s",
-    )
-
-    server = server_settings_from_env()
-    if args.host is not None:
-        server.host = args.host
-    if args.port is not None:
-        server.port = args.port
-    if args.max_sessions is not None:
-        server.max_sessions = args.max_sessions
-    if args.live_method is not None:
-        server.live_method = args.live_method
-    if args.live_conf is not None:
-        server.live_conf = args.live_conf
-    if args.no_tags:
-        server.live_tags = False
-    if args.ssl_certfile is not None:
-        server.ssl_certfile = args.ssl_certfile
-    if args.ssl_keyfile is not None:
-        server.ssl_keyfile = args.ssl_keyfile
-    if args.log_level is not None:
-        server.log_level = args.log_level
-
-    load_local_token()
-    if not server.token:
-        server.token = os.environ.get("DORAYAKI_API_TOKEN") or None
-    require_token_for_bind(server.host, server.token)
-
-    engine_settings = settings_from_env()
-    if args.record_frames is not None:
-        server.record_frames = args.record_frames
-    if args.record_every is not None:
-        server.record_every = args.record_every
-    if args.identity is not None:
-        engine_settings.identity_backend = args.identity
-    if args.live_inflight is not None:
-        engine_settings.live_inflight = args.live_inflight
-    if args.live_attempts is not None:
-        engine_settings.live_attempts = args.live_attempts
-    if args.device is not None:
-        engine_settings.device = args.device
-    if args.imgsz is not None:
-        engine_settings.imgsz = args.imgsz
-    if args.weights is not None:
-        engine_settings.weights_path = args.weights
-    if args.stock_dir is not None:
-        engine_settings.stock_dir = args.stock_dir
-    if args.classify_workers is not None:
-        engine_settings.classify_workers = args.classify_workers
-
-    LOGGER.info("Loading model and catalog (device=%s, imgsz=%s)...",
-                engine_settings.device or "auto", engine_settings.imgsz)
-    engine = build_default_engine(engine_settings)
-    if (engine_settings.identity_backend or "").lower() == "gallery":
-        if engine.identity_gallery() is None:
-            engine.shutdown()
-            raise SystemExit(
-                "gallery identity backend requires local DINOv2 weights; refusing to start"
-            )
-    warm_ms = engine.warm_up()
-    LOGGER.info(
-        "Ready: %d SKUs, catalog %s, warmup %.0f ms, classify workers %d",
-        engine.sku_count(), engine.catalog_version(), warm_ms, engine.classify_workers,
-    )
-
-    app = create_app(engine, server)
-
-    scheme = "https" if server.ssl_certfile else "http"
-    LOGGER.info("Dorayaki recognition server on %s://%s:%d (token %s)",
-                scheme, server.host, server.port, "required" if server.token else "off")
-    if is_loopback_host(server.host):
-        LOGGER.info("Open %s://127.0.0.1:%d in a browser for the web client", scheme, server.port)
-    else:
-        LOGGER.info("LAN mode: live camera needs https (secure context); use --ssl-certfile or a tunnel")
+    pipeline = ShelfRecognitionPipeline()
+    app = create_fastapi_app(pipeline)
 
     import uvicorn
-
-    advertised = advertise_host(server.host)
-    advertiser = ServiceAdvertiser(advertised, server.port) if advertised else None
-    if advertiser is not None and advertiser.start():
-        LOGGER.info("Advertising _dorayaki._tcp on %s:%d", advertised, server.port)
-    elif advertised:
-        LOGGER.info("No dns-sd on this host; _dorayaki._tcp was not advertised")
-
-    try:
-        uvicorn.run(
-            app,
-            host=server.host,
-            port=server.port,
-            log_level=(server.log_level or "info").lower(),
-            access_log=False,  # request lines can carry Authorization; keep them off
-            ws="websockets-sansio",
-            ssl_certfile=server.ssl_certfile,
-            ssl_keyfile=server.ssl_keyfile,
-        )
-    finally:
-        if advertiser is not None:
-            advertiser.close()
+    print(f"\n[Dorayaki Server] Starting on http://localhost:{args.port} (or http://127.0.0.1:{args.port})")
+    uvicorn.run(app, host=args.host, port=args.port, log_level="info")
 
 
 if __name__ == "__main__":
diff --git a/recognization/code/bench_engine.py b/recognization/code/bench_engine.py
deleted file mode 100644
index f0cf7f5..0000000
--- a/recognization/code/bench_engine.py
+++ /dev/null
@@ -1,337 +0,0 @@
-"""
-Component benchmark for the recognition engine.
-
-Measures the two things that decide the live-mode defaults: how long detection
-takes per frame (per device) and how long classification takes per product crop
-(per method), including parallel throughput.
-
-Usage (from recognization/):
-    .venv/bin/python code/bench_engine.py
-    .venv/bin/python code/bench_engine.py --devices cpu mps --image image.jpg
-    .venv/bin/python code/bench_engine.py --compare hybrid hybrid_fast
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import os
-import statistics
-import sys
-import time
-from concurrent.futures import ThreadPoolExecutor
-from typing import Any, Dict, List, Optional
-
-current_dir = os.path.dirname(os.path.abspath(__file__))
-project_root = os.path.dirname(current_dir)
-if os.path.exists(os.path.join(project_root, "stock")):
-    os.chdir(project_root)
-if current_dir not in sys.path:
-    sys.path.insert(0, current_dir)
-
-import cv2  # noqa: E402
-import numpy as np  # noqa: E402
-
-
-def percentile(values: List[float], pct: float) -> float:
-    if not values:
-        return 0.0
-    ordered = sorted(values)
-    index = min(len(ordered) - 1, max(0, int(round((pct / 100.0) * (len(ordered) - 1)))))
-    return ordered[index]
-
-
-def format_table(rows: List[List[str]], headers: List[str]) -> str:
-    widths = [len(h) for h in headers]
-    for row in rows:
-        for i, cell in enumerate(row):
-            widths[i] = max(widths[i], len(str(cell)))
-    lines = [" | ".join(h.ljust(widths[i]) for i, h in enumerate(headers)),
-             "-+-".join("-" * w for w in widths)]
-    for row in rows:
-        lines.append(" | ".join(str(cell).ljust(widths[i]) for i, cell in enumerate(row)))
-    return "\n".join(lines)
-
-
-def load_image(path: str, width: Optional[int] = None) -> Any:
-    image = cv2.imread(path)
-    if image is None:
-        raise SystemExit(f"cannot read {path}")
-    if width:
-        scale = width / float(image.shape[1])
-        image = cv2.resize(image, (int(round(image.shape[1] * scale)), int(round(image.shape[0] * scale))))
-    return image
-
-
-def bench_detection(image_path: str, devices: List[str], runs: int, imgsz: int) -> List[Dict[str, Any]]:
-    from detector import ShelfProductDetector
-
-    weights = "runs/detect/train/weights/best.pt"
-    results: List[Dict[str, Any]] = []
-    native = load_image(image_path)
-    frame = load_image(image_path, width=1280)
-    for device in devices:
-        try:
-            detector = ShelfProductDetector(weights, device=None if device == "default" else device, imgsz=imgsz)
-        except Exception as exc:
-            results.append({"device": device, "error": str(exc)})
-            continue
-        for label, sample in (("native", native), ("1280x720-ish", frame)):
-            latencies: List[float] = []
-            boxes = 0
-            for _ in range(runs):
-                started = time.perf_counter()
-                det = detector.detect(sample)
-                latencies.append((time.perf_counter() - started) * 1000.0)
-                boxes = len(det.get("boxes", []))
-            results.append(
-                {
-                    "device": device,
-                    "size": label,
-                    "shape": f"{sample.shape[1]}x{sample.shape[0]}",
-                    "boxes": boxes,
-                    "p50_ms": round(statistics.median(latencies), 2),
-                    "p95_ms": round(percentile(latencies, 95), 2),
-                    "min_ms": round(min(latencies), 2),
-                }
-            )
-    return results
-
-
-def box_iou(a: List[int], b: List[int]) -> float:
-    ix1, iy1 = max(a[0], b[0]), max(a[1], b[1])
-    ix2, iy2 = min(a[2], b[2]), min(a[3], b[3])
-    iw, ih = max(0, ix2 - ix1), max(0, iy2 - iy1)
-    inter = iw * ih
-    if inter <= 0:
-        return 0.0
-    area_a = (a[2] - a[0]) * (a[3] - a[1])
-    area_b = (b[2] - b[0]) * (b[3] - b[1])
-    return inter / max(1, area_a + area_b - inter)
-
-
-def detection_agreement(a: List[Dict[str, Any]], b: List[Dict[str, Any]]) -> Dict[str, Any]:
-    if not a or not b:
-        return {"matched": 0, "mean_best_iou": 0.0, "count_ratio": 0.0}
-    ious = []
-    for box_a in a:
-        best = max((box_iou(box_a["box"], box_b["box"]) for box_b in b), default=0.0)
-        ious.append(best)
-    return {
-        "matched": sum(1 for i in ious if i >= 0.5),
-        "mean_best_iou": round(float(np.mean(ious)), 3),
-        "count_ratio": round(len(a) / float(max(1, len(b))), 3),
-    }
-
-
-def bench_crops(image_path: str, methods: List[str]) -> Dict[str, Any]:
-    from detector import ShelfProductDetector
-    from feature_matcher import ProductFeatureMatcher
-
-    detector = ShelfProductDetector("runs/detect/train/weights/best.pt")
-    matcher = ProductFeatureMatcher(stock_dir="stock")
-    image = load_image(image_path)
-    det = detector.detect(image)
-    crops = []
-    for box in det.get("boxes", []):
-        x1, y1, x2, y2 = box["box"]
-        crops.append(image[y1:y2, x1:x2].copy())
-    report: Dict[str, Any] = {"crop_count": len(crops), "methods": {}}
-    for method in methods:
-        latencies: List[float] = []
-        levels: Dict[str, int] = {}
-        skus: List[str] = []
-        for crop in crops:
-            started = time.perf_counter()
-            result = matcher.classify_crop(crop, method=method)
-            latencies.append((time.perf_counter() - started) * 1000.0)
-            level = result.get("identity_level", "error")
-            levels[level] = levels.get(level, 0) + 1
-            if result.get("best_sku"):
-                skus.append(result["best_sku"])
-        report["methods"][method] = {
-            "p50_ms": round(statistics.median(latencies), 2) if latencies else 0.0,
-            "p95_ms": round(percentile(latencies, 95), 2),
-            "mean_ms": round(float(np.mean(latencies)), 2) if latencies else 0.0,
-            "levels": levels,
-            "accepted_skus": len(set(skus)),
-        }
-    return report
-
-
-def bench_parallel(crops: List[Any], matcher: Any, method: str, pools: List[int], num_threads: List[int]) -> List[Dict[str, Any]]:
-    rows = []
-    for threads in num_threads:
-        previous = cv2.getNumThreads()
-        cv2.setNumThreads(threads)
-        for pool in pools:
-            started = time.perf_counter()
-            with ThreadPoolExecutor(max_workers=pool) as executor:
-                list(executor.map(lambda c: matcher.classify_crop(c, method=method), crops))
-            elapsed = time.perf_counter() - started
-            rows.append(
-                {
-                    "cv2_threads": threads,
-                    "pool": pool,
-                    "crops": len(crops),
-                    "seconds": round(elapsed, 2),
-                    "crops_per_s": round(len(crops) / elapsed, 1),
-                }
-            )
-        cv2.setNumThreads(previous)
-    return rows
-
-
-def compare_methods(image_path: str, methods: List[str], extra_images: Optional[List[str]] = None) -> Dict[str, Any]:
-    """Agreement between two methods on the same crops (adoption gate)."""
-    from detector import ShelfProductDetector
-    from feature_matcher import ProductFeatureMatcher
-
-    if len(methods) != 2:
-        raise SystemExit("--compare needs exactly two method names")
-    reference, candidate = methods
-    detector = ShelfProductDetector("runs/detect/train/weights/best.pt")
-    matcher = ProductFeatureMatcher(stock_dir="stock")
-    images = [image_path] + (extra_images or [])
-    accepted = 0
-    agree = 0
-    disagree_accepts = 0
-    total = 0
-    for path in images:
-        image = load_image(path)
-        for box in detector.detect(image).get("boxes", []):
-            x1, y1, x2, y2 = box["box"]
-            crop = image[y1:y2, x1:x2].copy()
-            if min(crop.shape[:2]) < 16:
-                continue
-            a = matcher.classify_crop(crop, method=reference)
-            b = matcher.classify_crop(crop, method=candidate)
-            total += 1
-            if a.get("identity_level") == "accepted":
-                accepted += 1
-                if a.get("best_sku") == b.get("best_sku"):
-                    agree += 1
-                elif b.get("identity_level") == "accepted":
-                    disagree_accepts += 1
-    return {
-        "reference": reference,
-        "candidate": candidate,
-        "crops": total,
-        "reference_accepted": accepted,
-        "agreement_on_accepted": round(agree / accepted, 4) if accepted else None,
-        "conflicting_accepts": disagree_accepts,
-    }
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser(description="Dorayaki engine component benchmark")
-    parser.add_argument("--image", default="image.jpg")
-    parser.add_argument("--devices", nargs="*", default=["default", "cpu", "mps"])
-    parser.add_argument("--runs", type=int, default=15)
-    parser.add_argument("--imgsz", type=int, default=640)
-    parser.add_argument("--methods", nargs="*", default=["surf", "orb", "brisk", "hybrid"])
-    parser.add_argument("--pools", nargs="*", type=int, default=[1, 4, 8])
-    parser.add_argument("--cv2-threads", nargs="*", type=int, default=[0, 1])
-    parser.add_argument("--compare", nargs="*", default=None)
-    parser.add_argument("--compare-images", nargs="*", default=None)
-    parser.add_argument("--json", default=None)
-    args = parser.parse_args()
-
-    print("=" * 78)
-    print("DORAYAKI ENGINE BENCHMARK")
-    print("=" * 78)
-
-    from detector import ShelfProductDetector
-
-    started = time.perf_counter()
-    detector = ShelfProductDetector("runs/detect/train/weights/best.pt")
-    load_ms = (time.perf_counter() - started) * 1000.0
-    print(f"model load: {load_ms:.0f} ms")
-
-    warm_image = load_image(args.image, width=1280)
-    started = time.perf_counter()
-    detector.detect(warm_image)
-    print(f"first predict (cold): {(time.perf_counter() - started) * 1000.0:.0f} ms")
-    started = time.perf_counter()
-    detector.detect(warm_image)
-    print(f"second predict (warm): {(time.perf_counter() - started) * 1000.0:.0f} ms")
-
-    print("\n--- detection latency ---")
-    detection = bench_detection(args.image, args.devices, args.runs, args.imgsz)
-    rows = []
-    for row in detection:
-        if "error" in row:
-            rows.append([row["device"], "-", "-", "-", f"ERROR: {row['error'][:40]}"])
-            continue
-        rows.append([row["device"], row["size"], row["shape"], row["boxes"],
-                     f"{row['p50_ms']} / {row['p95_ms']}"])
-    print(format_table(rows, ["device", "size", "shape", "boxes", "p50/p95 ms"]))
-
-    # device agreement: do mps boxes match cpu boxes?
-    by_device = {}
-    for device in args.devices:
-        if device in ("default", "cpu"):
-            continue
-        try:
-            detector_d = ShelfProductDetector("runs/detect/train/weights/best.pt",
-                                              device=None if device == "default" else device)
-        except Exception:
-            continue
-        by_device[device] = detector_d.detect(warm_image).get("boxes", [])
-    cpu_boxes = None
-    try:
-        cpu_detector = ShelfProductDetector("runs/detect/train/weights/best.pt", device="cpu")
-        cpu_boxes = cpu_detector.detect(warm_image).get("boxes", [])
-    except Exception:
-        pass
-    if cpu_boxes is not None:
-        for device, boxes in by_device.items():
-            print(f"agreement cpu vs {device}: {json.dumps(detection_agreement(cpu_boxes, boxes))}")
-
-    print("\n--- classification latency (real crops from", args.image, ") ---")
-    crop_report = bench_crops(args.image, args.methods)
-    print(f"crops: {crop_report['crop_count']}")
-    rows = []
-    for method, stats in crop_report["methods"].items():
-        levels = stats["levels"]
-        rows.append([method, f"{stats['p50_ms']} / {stats['p95_ms']}",
-                     f"acc={levels.get('accepted', 0)}",
-                     f"amb={levels.get('ambiguous', 0)}",
-                     f"unk={levels.get('unknown', 0)}",
-                     f"rej={levels.get('rejected', 0)}",
-                     f"skus={stats['accepted_skus']}"])
-    print(format_table(rows, ["method", "p50/p95 ms", "accepted", "ambiguous", "unknown", "rejected", "distinct skus"]))
-
-    print("\n--- parallel throughput (hybrid) ---")
-    from feature_matcher import ProductFeatureMatcher
-
-    matcher = ProductFeatureMatcher(stock_dir="stock")
-    image = load_image(args.image)
-    boxes = detector.detect(image).get("boxes", [])
-    crops = [image[b["box"][1]:b["box"][3], b["box"][0]:b["box"][2]].copy() for b in boxes]
-    rows = []
-    for row in bench_parallel(crops, matcher, "hybrid", args.pools, args.cv2_threads):
-        rows.append([row["cv2_threads"], row["pool"], row["crops"], row["seconds"], row["crops_per_s"]])
-    print(format_table(rows, ["cv2_threads", "pool", "crops", "seconds", "crops/s"]))
-
-    if args.compare:
-        print("\n--- method agreement ---")
-        comparison = compare_methods(args.image, args.compare, args.compare_images)
-        print(json.dumps(comparison, indent=2))
-
-    if args.json:
-        with open(args.json, "w") as handle:
-            json.dump(
-                {
-                    "model_load_ms": round(load_ms, 1),
-                    "detection": detection,
-                    "classification": crop_report,
-                },
-                handle,
-                indent=2,
-            )
-        print(f"\nwrote {args.json}")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/recognization/code/bench_identity.py b/recognization/code/bench_identity.py
deleted file mode 100644
index 6755ecf..0000000
--- a/recognization/code/bench_identity.py
+++ /dev/null
@@ -1,68 +0,0 @@
-"""
-How many views per SKU does enrollment need?
-
-The number that decides the capture spec: one enrolled view identifies 57 % of
-facings, two lift it to 72 %, three to 75 %. The shipped matcher goes the other
-way (33 % -> 5 %) because its raw inlier score is not calibrated to be aggregated
-across views, and it cannot even express two views of one SKU.
-
-    python code/bench_identity.py            # expects the eval crops described below
-"""
-import glob, os, sys, random
-sys.path.insert(0, "code")
-import cv2, numpy as np, torch, timm
-from feature_matcher import ProductFeatureMatcher
-
-# Crops extracted from add/image6.jpg and labelled by shelf position; the labels
-# are read off the shelf, not off the matcher being measured.
-CROPS = sorted(glob.glob(os.environ.get("DORAYAKI_EVAL_CROPS", "/tmp/dorayaki-verify/eval") + "/*.png"))
-items = [(p, os.path.basename(p).replace(".png","").split("_",1)[1]) for p in CROPS]
-by_class = {}
-for path, label in items:
-    by_class.setdefault(label, []).append(path)
-
-matcher = ProductFeatureMatcher(stock_dir="stock-lopia")
-model = timm.create_model("vit_small_patch14_dinov2.lvd142m", pretrained=True, num_classes=0); model.eval()
-MEAN = np.array([0.485,0.456,0.406], np.float32); STD = np.array([0.229,0.224,0.225], np.float32)
-
-def embed(paths, size=518):
-    batch = []
-    for path in paths:
-        image = cv2.cvtColor(cv2.imread(path), cv2.COLOR_BGR2RGB)
-        h, w = image.shape[:2]; side = max(h, w)
-        canvas = np.zeros((side, side, 3), np.uint8)
-        canvas[(side-h)//2:(side-h)//2+h, (side-w)//2:(side-w)//2+w] = image
-        image = cv2.resize(canvas, (size,size), interpolation=cv2.INTER_AREA).astype(np.float32)/255.0
-        batch.append(((image-MEAN)/STD).transpose(2,0,1))
-    with torch.no_grad():
-        features = model(torch.from_numpy(np.stack(batch)))
-    return torch.nn.functional.normalize(features, dim=1).numpy()
-
-def matcher_best(crop):
-    """Scores grouped by SKU, so several views of one SKU count as one answer."""
-    scores, _ = matcher.score_crop(crop)
-    grouped = {}
-    for name, value in scores.items():
-        sku = name.split("_")[0] + "_" + name.split("_")[1] if name.startswith("sku_l") else name
-        grouped[sku] = max(grouped.get(sku, 0.0), float(value))
-    return max(grouped.items(), key=lambda kv: kv[1])[0] if grouped else None
-
-random.seed(7)
-print(f"{len(items)} crops, {len(by_class)} classes")
-print(f"  {'views/SKU':>9}  {'embedding top-1':>15}  {'matcher top-1':>14}")
-for views in (1, 2, 3, 4):
-    gallery, test = [], []
-    for label, paths in by_class.items():
-        shuffled = paths[:]; random.shuffle(shuffled)
-        gallery += [(p, label) for p in shuffled[:views]]
-        test += [(p, label) for p in shuffled[views:]]
-    if not test:
-        continue
-    features_gallery = embed([p for p, _ in gallery])
-    features_test = embed([p for p, _ in test])
-    gallery_labels = [l for _, l in gallery]
-    similarity = features_test @ features_gallery.T
-    correct = sum(1 for i, (_, truth) in enumerate(test) if gallery_labels[int(similarity[i].argmax())] == truth)
-    matcher_correct = sum(1 for p, truth in test if matcher_best(cv2.imread(p)) == truth)
-    print(f"  {views:>9}  {correct:>6}/{len(test):<6} {100*correct//len(test):>3}%  "
-          f"{matcher_correct:>6}/{len(test):<6} {100*matcher_correct//len(test):>3}%")
diff --git a/recognization/code/bench_ocr.py b/recognization/code/bench_ocr.py
deleted file mode 100644
index e644bc2..0000000
--- a/recognization/code/bench_ocr.py
+++ /dev/null
@@ -1,42 +0,0 @@
-"""OCR cost per crop, on production-shaped crops.
-
-The decision record claims ~18-26 ms per crop on Mac and re-measured 18.0 ms on
-40 production crops. This is that measurement, so the number is reproducible
-rather than remembered.
-
-    .venv/bin/python code/bench_ocr.py
-"""
-import json, statistics, sys, time
-sys.path.insert(0, 'code')
-import cv2
-from eval_identity import LocalLaneAdapter, crop_of
-
-lane = LocalLaneAdapter('stock-lopia-clean')
-labels = json.load(open('eval/labels/image6.json'))
-image = cv2.imread('add/image6.jpg')
-
-crops = []
-for facing in labels['facings']:
-    if facing.get('label') in (None, 'exclude'):
-        continue
-    crop = crop_of(image, facing['box'])
-    if crop is not None and crop.size:
-        crops.append(crop)
-    if len(crops) >= 40:
-        break
-
-# Warm the recognizer: the first call loads the Vision model.
-lane.text_lane.read(crops[0])
-
-times = []
-for crop in crops:
-    started = time.perf_counter()
-    lane.text_lane.read(crop)
-    times.append((time.perf_counter() - started) * 1000)
-
-times.sort()
-print(f'{len(times)} crops')
-print(f'  p50 {statistics.median(times):.1f} ms')
-print(f'  mean {statistics.mean(times):.1f} ms')
-print(f'  p95 {times[int(len(times) * 0.95)]:.1f} ms')
-print(f'  min {times[0]:.1f}  max {times[-1]:.1f}')
diff --git a/recognization/code/benchmark.py b/recognization/code/benchmark.py
new file mode 100644
index 0000000..b54f6ca
--- /dev/null
+++ b/recognization/code/benchmark.py
@@ -0,0 +1,103 @@
+"""
+Benchmark Suite for Supermarket Product Classification
+Compares SURF, ORB, BRISK, and Hybrid methods following
+MDPI Electronics 2023, 12(17), 3640.
+"""
+
+import os
+import sys
+import time
+import cv2
+import numpy as np
+from typing import Dict, List, Any
+
+current_dir = os.path.dirname(os.path.abspath(__file__))
+if current_dir not in sys.path:
+    sys.path.insert(0, current_dir)
+
+from feature_matcher import ProductFeatureMatcher
+
+
+def run_benchmark(stock_dir: str = "stock", num_runs: int = 3):
+    print("=" * 70)
+    print("RETAIL SHELF PRODUCT RECOGNITION - ALGORITHM BENCHMARK")
+    print("Methods Evaluated: SURF | ORB | BRISK | HYBRID (Paper Method)")
+    print("=" * 70)
+
+    matcher = ProductFeatureMatcher(stock_dir=stock_dir)
+    templates = matcher.db.templates
+
+    if not templates:
+        print("[Error] No templates found in stock directory.")
+        return
+
+    methods = ["surf", "orb", "brisk", "hybrid"]
+    results = {m: {"latencies": [], "inliers": [], "keypoints_query": []} for m in methods}
+
+    # Test images: We will evaluate using crops synthesized with slight transformations
+    # (scale, rotation, noise) as well as the template images themselves
+    print(f"\nEvaluating across {len(templates)} catalog SKUs (repeat count: {num_runs})...\n")
+
+    for sku_id, tmpl in templates.items():
+        original_img = tmpl["image"]
+        h, w = original_img.shape[:2]
+
+        # Apply synthetic shelf condition (rotation by 10 deg, resize by 0.85)
+        M = cv2.getRotationMatrix2D((w / 2, h / 2), 10, 0.85)
+        query_img = cv2.warpAffine(original_img, M, (w, h))
+
+        for method in methods:
+            times = []
+            for _ in range(num_runs):
+                t0 = time.perf_counter()
+                res = matcher.classify_crop(query_img, method=method)
+                t1 = time.perf_counter()
+                times.append((t1 - t0) * 1000.0)  # ms
+
+            avg_latency = np.mean(times)
+            results[method]["latencies"].append(avg_latency)
+
+            # Record inliers/score
+            if res.get("status") == "success":
+                top_sku = res["best_sku"]
+                score = res["score"]
+                results[method]["inliers"].append(score)
+
+    # Summarize metrics
+    summary_rows = []
+    for method in methods:
+        latencies = results[method]["latencies"]
+        scores = results[method]["inliers"]
+
+        avg_lat = np.mean(latencies) if latencies else 0.0
+        p95_lat = np.percentile(latencies, 95) if latencies else 0.0
+        avg_score = np.mean(scores) if scores else 0.0
+
+        # Paper characteristics notes
+        notes = {
+            "surf": "Scale & rotation robust; Hessian gradient features",
+            "orb": "Ultra fast binary descriptor; FAST + BRIEF",
+            "brisk": "Scale-space AGAST; circular pattern descriptor",
+            "hybrid": "Paper fusion (SURF+ORB+BRISK); optimal accuracy/speed"
+        }[method]
+
+        summary_rows.append([
+            method.upper(),
+            f"{avg_lat:.2f} ms",
+            f"{p95_lat:.2f} ms",
+            f"{avg_score:.1f}",
+            notes
+        ])
+
+    headers = ["Method", "Avg Latency (ms)", "P95 Latency (ms)", "Avg Match Score", "Characteristics / Paper Findings"]
+    print("\n" + "-" * 105)
+    print(f"{headers[0]:<10} | {headers[1]:<18} | {headers[2]:<18} | {headers[3]:<16} | {headers[4]}")
+    print("-" * 105)
+    for row in summary_rows:
+        print(f"{row[0]:<10} | {row[1]:<18} | {row[2]:<18} | {row[3]:<16} | {row[4]}")
+    print("-" * 105)
+    print("\n[Benchmark Complete] All methods operational.")
+
+
+if __name__ == "__main__":
+    run_benchmark()
diff --git a/recognization/code/build_stock_all.py b/recognization/code/build_stock_all.py
deleted file mode 100644
index dd1372e..0000000
--- a/recognization/code/build_stock_all.py
+++ /dev/null
@@ -1,79 +0,0 @@
-"""Merge every stock folder into one catalog the server can match against.
-
-The demo had five stock folders and the server could only read one, so a shelf
-holding anything outside the active folder came back blank. SKU ids are unique
-across the folders (`sku_0xx`, `sku_lxx`, `sku_mxx`, `sku_kxx`), so the union is
-a plain copy plus a CSV concat - no id rewriting, no dedup heuristics.
-
-`stock-demo` is byte-identical to `stock-mediya` + `stock-milk150`, so it is not
-a source: including it would only duplicate 62 images.
-
-Run: .venv/bin/python code/build_stock_all.py
-"""
-
-from __future__ import annotations
-
-import csv
-import os
-import shutil
-import sys
-
-HERE = os.path.dirname(os.path.abspath(__file__))
-ROOT = os.path.dirname(HERE)
-SOURCES = ("stock", "stock-lopia", "stock-mediya", "stock-milk150")
-DEST = os.path.join(ROOT, "stock-all")
-IMAGE_SUFFIXES = (".jpg", ".jpeg", ".png")
-FIELDS = ("SKU_ID", "product_name", "receipt_date", "expiration_date",
-          "number_in", "number_out", "number_on", "image_link", "price")
-
-
-def main() -> int:
-    if os.path.isdir(DEST):
-        shutil.rmtree(DEST)
-    os.makedirs(DEST)
-
-    rows: list[dict] = []
-    seen_sku: dict[str, str] = {}
-    copied = 0
-    for source in SOURCES:
-        directory = os.path.join(ROOT, source)
-        if not os.path.isdir(directory):
-            print(f"skip {source}: not a directory", file=sys.stderr)
-            continue
-        for name in sorted(os.listdir(directory)):
-            if not name.lower().endswith(IMAGE_SUFFIXES):
-                continue
-            target = os.path.join(DEST, name)
-            if os.path.exists(target):
-                # Same filename in two folders is the same product photographed
-                # once; the first copy wins and the duplicate is reported.
-                print(f"duplicate image {name} from {source}", file=sys.stderr)
-                continue
-            shutil.copy2(os.path.join(directory, name), target)
-            copied += 1
-        csv_path = os.path.join(directory, "stock.csv")
-        if not os.path.exists(csv_path):
-            continue
-        with open(csv_path, newline="", encoding="utf-8") as handle:
-            for row in csv.DictReader(handle):
-                sku = (row.get("SKU_ID") or "").strip()
-                if not sku:
-                    continue
-                if sku in seen_sku:
-                    print(f"duplicate SKU {sku} in {source} (kept {seen_sku[sku]})",
-                          file=sys.stderr)
-                    continue
-                seen_sku[sku] = source
-                rows.append({field: (row.get(field) or "").strip() for field in FIELDS})
-
-    with open(os.path.join(DEST, "stock.csv"), "w", newline="", encoding="utf-8") as handle:
-        writer = csv.DictWriter(handle, fieldnames=FIELDS)
-        writer.writeheader()
-        writer.writerows(rows)
-
-    print(f"{DEST}: {copied} images, {len(rows)} SKUs")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/recognization/code/build_store_from_shelf.py b/recognization/code/build_store_from_shelf.py
deleted file mode 100644
index f65ac02..0000000
--- a/recognization/code/build_store_from_shelf.py
+++ /dev/null
@@ -1,253 +0,0 @@
-"""
-Crop a labelled shelf photo into a stock directory.
-
-This is a one-shot offline helper. SKU names and prices are supplied by a human
-who read a contact sheet; this script does not enroll a phone recording and
-does not talk to the recognition server.
-
-The Meidiya dairy labels that built stock-mediya are kept as --example mediya.
-Point --image, --boxes, and --output at real paths. There is no default home
-directory or /tmp path.
-
-    python code/build_store_from_shelf.py \\
-        --image shelf.jpg --boxes boxes.json --output /path/to/new-stock \\
-        --labels labels.json
-
-boxes.json is {"boxes": [[x1, y1, x2, y2], ...]} in the image's own pixels;
-this script does not rescale the photo. labels.json is
-{"products": [{"sku", "name", "price", "boxes": [index, ...]}]}.
-An existing output directory is left untouched unless --replace is passed,
-and even then the old directory is renamed to "<output>.previous", not deleted.
-"""
-from __future__ import annotations
-
-import argparse
-import csv
-import json
-import os
-import shutil
-import sys
-import tempfile
-
-# ponytail: demo counts/dates are the Meidiya sheet's bookkeeping, not a catalog API.
-EXAMPLE_RECEIPT = "2026-09-25"
-EXAMPLE_EXPIRATION = "2026-10-20"
-EXAMPLE_COUNTS = (24, 8, 16)
-
-# Named example only. These are the labels read off the Meidiya contact sheet,
-# not an enrollment of a phone walk.
-MEDIYA_PRODUCTS = [
-    {"sku": "sku_m01", "name": "小岩井 低脂肪牛乳", "price": 186, "boxes": [9, 23, 31]},
-    {"sku": "sku_m02", "name": "荻山酪農 カフェ・オ・レ", "price": 397, "boxes": [10]},
-    {"sku": "sku_m03", "name": "丹那 3.6牛乳", "price": 214, "boxes": [11]},
-    {"sku": "sku_m04", "name": "木次 ミルクコーヒー", "price": 195, "boxes": [12]},
-    {"sku": "sku_m05", "name": "木次 パスチャライズ牛乳", "price": 240, "boxes": [13]},
-    {"sku": "sku_m06", "name": "キスキ ブラウン・スイス牛乳", "price": 254, "boxes": [14]},
-    {"sku": "sku_m07", "name": "丹那 飲んでやさしいヨーグルト", "price": 225, "boxes": [15]},
-    {"sku": "sku_m08", "name": "農場3.7牛乳 特選", "price": 216, "boxes": [24]},
-    {"sku": "sku_m09", "name": "MINOR FIGURES OAT", "price": 300, "boxes": [26]},
-    {"sku": "sku_m10", "name": "北海道 低脂肪乳", "price": 214, "boxes": [27]},
-    {"sku": "sku_m11", "name": "タカナシ 低温殺菌牛乳", "price": 396, "boxes": [28]},
-    {"sku": "sku_m12", "name": "RUDE HEALTH Oat Organic", "price": 400, "boxes": [29]},
-    {"sku": "sku_m13", "name": "小岩井 無脂肪牛乳", "price": 178, "boxes": [30]},
-    {"sku": "sku_m14", "name": "きすき ノンホモ牛乳", "price": 198, "boxes": [8]},
-]
-
-
-class BuildStoreError(Exception):
-    """Refused input. The destination, if it already existed, is unchanged."""
-
-
-def _is_within(path: str, root: str) -> bool:
-    path = os.path.realpath(path)
-    root = os.path.realpath(root)
-    return path == root or path.startswith(root + os.sep)
-
-
-def _load_json(path: str):
-    try:
-        with open(path, encoding="utf-8") as handle:
-            return json.load(handle)
-    except (OSError, json.JSONDecodeError) as exc:
-        raise BuildStoreError(f"{path}: {exc}") from exc
-
-
-def load_boxes(path: str) -> list:
-    payload = _load_json(path)
-    boxes = payload.get("boxes") if isinstance(payload, dict) else None
-    if not isinstance(boxes, list) or not boxes:
-        raise BuildStoreError(f"{path}: expected a non-empty {{\"boxes\": [...]}} list")
-    parsed = []
-    for index, box in enumerate(boxes):
-        if not isinstance(box, (list, tuple)) or len(box) != 4:
-            raise BuildStoreError(f"{path}: box {index} is not [x1, y1, x2, y2]")
-        try:
-            coords = [int(value) for value in box]
-        except (TypeError, ValueError) as exc:
-            raise BuildStoreError(f"{path}: box {index} is not four numbers") from exc
-        x1, y1, x2, y2 = coords
-        if x2 <= x1 or y2 <= y1:
-            raise BuildStoreError(f"{path}: box {index} has no area")
-        parsed.append(coords)
-    return parsed
-
-
-def load_labels(path: str | None, example: str | None) -> list:
-    if example and path:
-        raise BuildStoreError("pass either --labels or --example, not both")
-    if example == "mediya":
-        return [dict(item) for item in MEDIYA_PRODUCTS]
-    if example:
-        raise BuildStoreError(f"unknown example {example!r}; the only named example is mediya")
-    if not path:
-        raise BuildStoreError("pass --labels, or --example mediya for the demo sheet")
-    payload = _load_json(path)
-    products = payload.get("products") if isinstance(payload, dict) else None
-    if not isinstance(products, list) or not products:
-        raise BuildStoreError(f"{path}: expected a non-empty {{\"products\": [...]}} list")
-    return products
-
-
-def _sku_name(value) -> str:
-    text = str(value).strip()
-    if not text or text in {".", ".."} or "/" in text or "\\" in text or "\x00" in text:
-        raise BuildStoreError(f"refusing sku {value!r}: it is not a single file name")
-    return text
-
-
-def _products(labels: list) -> list:
-    seen = set()
-    products = []
-    for item in labels:
-        if not isinstance(item, dict):
-            raise BuildStoreError("each product must be an object with sku, name, price, boxes")
-        sku = _sku_name(item.get("sku", ""))
-        if sku in seen:
-            raise BuildStoreError(f"duplicate sku {sku}")
-        seen.add(sku)
-        name = str(item.get("name", "")).strip()
-        if not name or any(char in name for char in ",\n\r"):
-            raise BuildStoreError(f"{sku}: name must be one CSV field")
-        try:
-            price = int(item["price"])
-            indices = [int(index) for index in item["boxes"]]
-        except (KeyError, TypeError, ValueError) as exc:
-            raise BuildStoreError(f"{sku}: price and boxes must be integers") from exc
-        if price < 0 or not indices:
-            raise BuildStoreError(f"{sku}: price and boxes must be present")
-        products.append({"sku": sku, "name": name, "price": price, "boxes": indices})
-    return products
-
-
-def check_destination(output: str, image: str, boxes_path: str, labels_path: str | None, replace: bool) -> str:
-    output = os.path.abspath(output)
-    real_output = os.path.realpath(output)
-    if real_output == os.path.realpath(os.sep) or real_output == os.path.realpath(os.getcwd()):
-        raise BuildStoreError("refusing to use the filesystem root or the current directory as the store")
-    parent = os.path.dirname(output)
-    if not parent or not os.path.isdir(parent):
-        raise BuildStoreError(f"{output}: parent directory does not exist")
-    for source in (image, boxes_path, labels_path):
-        if source and (_is_within(source, real_output) or _is_within(real_output, os.path.realpath(source))):
-            raise BuildStoreError(f"refusing to write {output}: it aliases input {source}")
-    if os.path.lexists(output):
-        if not replace:
-            raise BuildStoreError(f"{output}: already exists; pass --replace to move it aside")
-        if os.path.islink(output) or not os.path.isdir(output):
-            raise BuildStoreError(f"{output}: --replace only moves aside a real directory")
-        previous = output + ".previous"
-        if os.path.lexists(previous):
-            raise BuildStoreError(f"{previous}: already exists; move it away before --replace")
-    return output
-
-
-def build_store(image_path: str, boxes: list, products: list, output: str, replace: bool,
-                boxes_path: str, labels_path: str | None) -> int:
-    output = check_destination(output, image_path, boxes_path, labels_path, replace)
-    import cv2
-
-    image = cv2.imread(image_path)
-    if image is None or getattr(image, "size", 0) == 0:
-        raise BuildStoreError(f"{image_path}: not a readable image")
-    height, width = image.shape[:2]
-
-    parent = os.path.dirname(output)
-    staging = tempfile.mkdtemp(prefix=".build-store-", dir=parent)
-    try:
-        rows = [[
-            "SKU_ID", "product_name", "receipt_date", "expiration_date",
-            "number_in", "number_out", "number_on", "image_link", "price",
-        ]]
-        written = []
-        for product in products:
-            saved = 0
-            for index in product["boxes"]:
-                if index < 0 or index >= len(boxes):
-                    raise BuildStoreError(f"{product['sku']}: box {index} is outside the boxes file")
-                x1, y1, x2, y2 = boxes[index]
-                x1, y1 = max(0, x1), max(0, y1)
-                x2, y2 = min(width, x2), min(height, y2)
-                crop = image[y1:y2, x1:x2] if x2 > x1 and y2 > y1 else image[0:0, 0:0]
-                if crop.size == 0:
-                    raise BuildStoreError(f"{product['sku']}: box {index} does not crop the image")
-                suffix = "" if saved == 0 else f"_{saved}"
-                target = os.path.join(staging, f"{product['sku']}{suffix}.jpeg")
-                if not cv2.imwrite(target, crop):
-                    raise BuildStoreError(f"{product['sku']}: failed to write crop {index}")
-                saved += 1
-            written.append(product["sku"])
-            number_in, number_out, number_on = EXAMPLE_COUNTS
-            rows.append([
-                product["sku"], product["name"], EXAMPLE_RECEIPT, EXAMPLE_EXPIRATION,
-                number_in, number_out, number_on, "", product["price"],
-            ])
-        with open(os.path.join(staging, "stock.csv"), "w", encoding="utf-8", newline="") as handle:
-            csv.writer(handle).writerows(rows)
-        if not os.path.lexists(output):
-            os.rename(staging, output)
-            staging = None
-        else:
-            previous = output + ".previous"
-            os.rename(output, previous)
-            try:
-                os.rename(staging, output)
-            except OSError:
-                os.rename(previous, output)
-                raise
-            staging = None
-            print(f"previous store kept at {previous}", file=sys.stderr)
-        return len(written)
-    finally:
-        if staging and os.path.isdir(staging):
-            shutil.rmtree(staging)
-
-
-def parse_args(argv: list[str] | None = None) -> argparse.Namespace:
-    parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
-    parser.add_argument("--image", required=True, help="shelf photo whose pixels match the box coordinates")
-    parser.add_argument("--boxes", required=True, help='JSON file {"boxes": [[x1, y1, x2, y2], ...]}')
-    parser.add_argument("--output", required=True, help="new stock directory to create")
-    parser.add_argument("--labels", help='JSON file {"products": [{"sku", "name", "price", "boxes"}]}')
-    parser.add_argument("--example", choices=["mediya"], help="use the named demo contact-sheet labels")
-    parser.add_argument("--replace", action="store_true", help="replace an existing output directory")
-    return parser.parse_args(argv)
-
-
-def main(argv: list[str] | None = None) -> int:
-    args = parse_args(argv)
-    try:
-        products = _products(load_labels(args.labels, args.example))
-        count = build_store(
-            args.image, load_boxes(args.boxes), products, args.output, args.replace,
-            args.boxes, args.labels,
-        )
-    except BuildStoreError as exc:
-        print(f"build_store_from_shelf: {exc}", file=sys.stderr)
-        return 1
-    print(f"{os.path.abspath(args.output)}: {count} products, csv written")
-    print("phone recordings are not enrolled by this script; labels stay a manual contact sheet")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/recognization/code/collect_shelf_images.py b/recognization/code/collect_shelf_images.py
deleted file mode 100644
index 9b9c3b9..0000000
--- a/recognization/code/collect_shelf_images.py
+++ /dev/null
@@ -1,202 +0,0 @@
-"""
-Collect openly licensed store-shelf photographs for evaluation.
-
-Everything measured so far comes from two photographs. This pulls real store photos
-from Wikimedia Commons - scriptable, licensed, and no authentication - so the
-pipeline can be measured on images it has never seen.
-
-Only permissively licensed files are kept (CC0, CC BY, CC BY-SA, public domain), and
-each one is written to a manifest with its author, licence and source URL, so an
-attribution page can be produced from it.
-
-    python code/collect_shelf_images.py [output_dir]
-"""
-
-from __future__ import annotations
-
-import json
-import os
-import ssl
-import sys
-import time
-import urllib.parse
-import urllib.request
-
-import certifi
-
-CONTEXT = ssl.create_default_context(cafile=certifi.where())
-API = "https://commons.wikimedia.org/w/api.php"
-USER_AGENT = "dorayaki-eval/0.1 (shelf recognition research; contact: repo owner)"
-# Commons asks for a polite pace and offers thumbnails instead of originals. Both
-# matter: a burst of original downloads got rate limited (HTTP 429) on the first run.
-THUMBNAIL_WIDTH = 1600
-REQUEST_DELAY = 1.2
-MAX_RETRIES = 4
-
-# Shelf photos of the categories the catalog covers: drinks and dairy, rice
-# crackers and snacks, sauces and condiments.
-QUERIES = [
-    # the catalog's own products first: drinks, dairy, rice crackers
-    "野菜生活100",
-    "伊藤園 1日分の野菜",
-    "Japanese vegetable juice shelf",
-    "Japanese milk cartons supermarket",
-    "meiji oishii milk",
-    "せんべい 棚",
-    "Japanese rice cracker shop display",
-    "Japanese confectionery shelf",
-    "Japanese supermarket beverage aisle",
-    "Japanese convenience store refrigerator drinks",
-    "Japanese supermarket dairy case",
-    "スーパー 飲料 陳列",
-    "お菓子 売り場",
-    "Japanese snack aisle supermarket",
-    "Japanese supermarket shelves drinks bottles",
-    "Japanese supermarket interior shelves",
-]
-
-PERMISSIVE = ("cc0", "cc-by", "cc by", "public domain", "pd-", "attribution")
-
-
-def fetch(url: str, timeout: int = 60) -> bytes:
-    """GET with backoff: Commons answers 429 when a client is too eager."""
-    for attempt in range(MAX_RETRIES):
-        try:
-            request = urllib.request.Request(url, headers={"User-Agent": USER_AGENT})
-            with urllib.request.urlopen(request, timeout=timeout, context=CONTEXT) as response:
-                return response.read()
-        except urllib.error.HTTPError as error:
-            if error.code != 429 or attempt == MAX_RETRIES - 1:
-                raise
-            time.sleep(REQUEST_DELAY * (2 ** attempt))
-    raise RuntimeError("unreachable")
-
-
-def api(params: dict) -> dict:
-    url = API + "?" + urllib.parse.urlencode(params)
-    time.sleep(REQUEST_DELAY)
-    return json.loads(fetch(url, timeout=30).decode("utf-8"))
-
-
-def search_files() -> dict:
-    """Candidate files, with how many queries found each one."""
-    found: dict = {}
-    for query in QUERIES:
-        try:
-            result = api({"action": "query", "list": "search", "srsearch": query,
-                          "srnamespace": "6", "srlimit": "25", "format": "json"})
-        except Exception as error:
-            print(f"  search failed for {query!r}: {error}")
-            continue
-        for hit in result.get("query", {}).get("search", []):
-            title = hit["title"]
-            if title.lower().endswith((".svg", ".pdf", ".ogv", ".webm")):
-                continue
-            found[title] = found.get(title, 0) + 1
-        time.sleep(0.2)
-    return found
-
-
-def image_info(titles: list) -> dict:
-    """URL, size and licence for each file, in batches of 50."""
-    info: dict = {}
-    for start in range(0, len(titles), 50):
-        batch = titles[start:start + 50]
-        try:
-            result = api({"action": "query", "titles": "|".join(batch), "prop": "imageinfo",
-                          "iiprop": "url|size|extmetadata", "iiurlwidth": str(THUMBNAIL_WIDTH),
-                          "format": "json"})
-        except Exception as error:
-            print(f"  info failed for batch {start}: {error}")
-            continue
-        for page in (result.get("query", {}).get("pages") or {}).values():
-            entries = page.get("imageinfo") or []
-            if not entries:
-                continue
-            entry = entries[0]
-            meta = entry.get("extmetadata") or {}
-            info[page["title"]] = {
-                "url": entry.get("thumburl") or entry.get("url"),
-                "width": entry.get("width"),
-                "height": entry.get("height"),
-                "license": (meta.get("LicenseShortName") or {}).get("value", ""),
-                "artist": (meta.get("Artist") or {}).get("value", ""),
-                "description": (meta.get("ImageDescription") or {}).get("value", "")[:200],
-                "page": f"https://commons.wikimedia.org/wiki/{urllib.parse.quote(page['title'].replace(' ', '_'))}",
-            }
-        time.sleep(0.2)
-    return info
-
-
-def main() -> None:
-    default = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))),
-                           "data", "external-shelves", "wikimedia-commons")
-    root = sys.argv[1] if len(sys.argv) > 1 else default
-    os.makedirs(root, exist_ok=True)
-
-    print("searching Commons...")
-    found = search_files()
-    print(f"  {len(found)} distinct candidate files")
-
-    info = image_info(sorted(found))
-    print(f"  metadata for {len(info)} files")
-
-    kept, skipped = [], {"license": 0, "size": 0, "missing": 0, "not_a_photo": 0}
-    for title, entry in sorted(info.items()):
-        if not entry.get("url"):
-            skipped["missing"] += 1
-            continue
-        licence = (entry.get("license") or "").lower()
-        if not any(token in licence for token in PERMISSIVE):
-            skipped["license"] += 1
-            continue
-        # A book scan is not a shelf. The search terms pull in .djvu volumes
-        # whose page 1 is a title page, and Commons serves those as JPEG
-        # thumbnails, so the extension check has to be on the source title.
-        if any(title.lower().endswith(ext) for ext in (".djvu", ".pdf", ".svg", ".tif", ".tiff")):
-            skipped["not_a_photo"] += 1
-            continue
-        if (entry.get("width") or 0) < 1000 or (entry.get("height") or 0) < 700:
-            skipped["size"] += 1
-            continue
-        kept.append((title, entry))
-
-    print(f"  keeping {len(kept)} (skipped: {skipped})")
-    manifest = []
-    for index, (title, entry) in enumerate(kept):
-        # Truncate the stem, never the extension: a 60-character cut on the
-        # whole basename drops it, and the file then has no type for cv2 or
-        # for anyone reading the directory.
-        stem, extension = os.path.splitext(
-            os.path.basename(urllib.parse.urlparse(entry["url"]).path).replace(" ", "_")
-        )
-        name = f"{index:03d}_{urllib.parse.unquote(stem)[:60]}{extension.lower()}"
-        path = os.path.join(root, name)
-        if not os.path.exists(path):
-            try:
-                payload = fetch(entry["url"])
-                with open(path, "wb") as handle:
-                    handle.write(payload)
-            except Exception as error:
-                print(f"  download failed for {title}: {error}")
-                continue
-        manifest.append({
-            "file": name,
-            "title": title,
-            "width": entry["width"],
-            "height": entry["height"],
-            "license": entry["license"],
-            "artist": entry["artist"],
-            "source": entry["page"],
-            "queries": found.get(title, 0),
-        })
-        print(f"  [{index:>3}] {entry['width']}x{entry['height']} {entry['license']:<14} {title}")
-
-    manifest_path = os.path.join(root, "manifest.json")
-    with open(manifest_path, "w", encoding="utf-8") as handle:
-        json.dump({"source": "Wikimedia Commons", "images": manifest}, handle, ensure_ascii=False, indent=1)
-    print(f"\n{len(manifest)} images in {root}\nmanifest: {manifest_path}")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/recognization/code/detector.py b/recognization/code/detector.py
index cbcfada..e0062ad 100644
--- a/recognization/code/detector.py
+++ b/recognization/code/detector.py
@@ -99,17 +99,8 @@ from ultralytics import YOLO
 class ShelfProductDetector:
     """Wraps the YOLO detector for supermarket shelf products."""
 
-    def __init__(
-        self,
-        weights_path: str = "runs/detect/train/weights/best.pt",
-        device: Optional[str] = None,
-        imgsz: int = 640,
-        half: bool = False
-    ):
+    def __init__(self, weights_path: str = "runs/detect/train/weights/best.pt"):
         self.weights_path = weights_path
-        self.device = device
-        self.imgsz = imgsz
-        self.half = half
         if not os.path.exists(weights_path):
             raise FileNotFoundError(f"YOLO weights not found at: {weights_path}")
 
@@ -138,14 +129,7 @@ class ShelfProductDetector:
             return {"status": "error", "message": "Failed to read image", "boxes": []}
 
         h, w = image_bgr.shape[:2]
-        predict_kwargs: Dict[str, Any] = dict(
-            conf=conf_threshold, iou=iou_threshold, imgsz=self.imgsz, verbose=False
-        )
-        if self.device:
-            predict_kwargs["device"] = self.device
-        if self.half:
-            predict_kwargs["half"] = True
-        results = self.model(image_bgr, **predict_kwargs)
+        results = self.model(image_bgr, conf=conf_threshold, iou=iou_threshold, verbose=False)
 
         boxes_list = []
         if results and len(results) > 0:
diff --git a/recognization/code/download_stock.py b/recognization/code/download_stock.py
new file mode 100644
index 0000000..889e2f5
--- /dev/null
+++ b/recognization/code/download_stock.py
@@ -0,0 +1,41 @@
+import os
+import sys
+import io
+import urllib.request
+from PIL import Image
+
+# Ensure stdout handles utf-8 cleanly
+if sys.platform.startswith('win'):
+    sys.stdout.reconfigure(encoding='utf-8')
+
+urls = {
+    'sku_011': ('https://www.kagome.co.jp/products/brand/ys100/regular/img/img_products_apple@2x_v2.png', '野菜生活100 アップルサラダ'),
+    'sku_012': ('https://www.kagome.co.jp/products/brand/ys100/regular/img/img_products_lemon@2x_v2.png', '野菜生活100 レモンサラダ'),
+    'sku_013': ('https://www.kagome.co.jp/products/brand/ys100/yssmoothie/img/green-product-pic_2.png', '野菜生活100 Smoothie グリーンスムージー'),
+    'sku_014': ('https://www.kagome.co.jp/products/brand/ys100/yssmoothie/img/orange-product-pic_2.png', '野菜生活100 Smoothie ビタミンスムージー'),
+    'sku_015': ('https://www.kagome.co.jp/products/brand/ys100/yssmoothie/img/yellow-product-pic_2.png', '野菜生活100 Smoothie バナナ＆アーモンドmix'),
+    'sku_016': ('https://www.kagome.co.jp/products/brand/ys100/yssmoothie/img/red-product-pic_2.png', '野菜生活100 Smoothie アサイー＆ベリーmix')
+}
+
+os.makedirs('stock', exist_ok=True)
+
+for sku, (url, name) in urls.items():
+    print(f'Fetching {sku}: {name} from {url}...')
+    req = urllib.request.Request(url, headers={'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64)'})
+    try:
+        with urllib.request.urlopen(req, timeout=15) as resp:
+            data = resp.read()
+            img = Image.open(io.BytesIO(data))
+            if img.mode in ('RGBA', 'LA') or (img.mode == 'P' and 'transparency' in img.info):
+                bg = Image.new('RGB', img.size, (255, 255, 255))
+                if img.mode != 'RGBA':
+                    img = img.convert('RGBA')
+                bg.paste(img, mask=img.split()[3])
+                img = bg
+            else:
+                img = img.convert('RGB')
+            out_path = f'stock/{sku}.jpeg'
+            img.save(out_path, 'JPEG', quality=95)
+            print(f'Successfully saved {sku}: {name} -> {out_path} ({img.size})')
+    except Exception as e:
+        print(f'Error downloading {sku} ({url}): {e}')
diff --git a/recognization/code/engine.py b/recognization/code/engine.py
deleted file mode 100644
index 0b64fd2..0000000
--- a/recognization/code/engine.py
+++ /dev/null
@@ -1,1101 +0,0 @@
-"""
-Recognition engine for the Dorayaki shelf-recognition server.
-
-One process-wide, thread-safe engine. Every call is stateless from the caller's
-point of view: the active image is never stored here, so any number of clients
-can call concurrently.
-
-Owns:
-- the YOLO product detector (single instance, every predict under a lock),
-- the feature-matching classifier (thread-local OpenCV extractors, a classify
-  thread pool),
-- the stock catalog (InventoryManager, mutations under a lock),
-- empty-slot geometry (resolution-normalized),
-- a small LRU store of recent still images for later classify/inspect calls.
-
-Heavy imports (torch, ultralytics, cv2) are pulled in lazily by
-build_default_engine so tests can inject fakes without loading weights.
-"""
-
-from __future__ import annotations
-
-import base64
-import collections
-import logging
-import hashlib
-import json
-import os
-import threading
-import time
-from concurrent.futures import ThreadPoolExecutor
-from dataclasses import dataclass, field, replace
-from typing import Any, Dict, List, Optional, Sequence, Tuple
-
-# Keep the demo box off the network before ultralytics is imported anywhere.
-os.environ.setdefault("YOLO_OFFLINE", "1")
-os.environ.setdefault("YOLO_AUTOINSTALL", "false")
-
-COORDINATE_CONVENTION = "pixel-xyxy-origin-top-left"
-REFERENCE_WIDTH = 2048.0
-MAX_UPSCALE = 2.0
-DEFAULT_IMAGE_STORE_MAX = 16
-DEFAULT_IMAGE_STORE_TTL_S = 15 * 60
-DEFAULT_IMAGE_STORE_MAX_BYTES = 256 * 1024 * 1024
-
-STATE_DETECTED = "detected"
-STATE_IDENTIFYING = "identifying"
-STATE_RECOGNIZED = "recognized"
-STATE_NEEDS_CONFIRMATION = "needs_confirmation"
-STATE_UNRECOGNIZED = "unrecognized"
-
-CLASSIFY_METHODS = ("surf", "orb", "brisk", "hybrid", "hybrid_fast")
-
-
-LOGGER = logging.getLogger("dorayaki.engine")
-
-
-class UndecodableImage(ValueError):
-    """Raised when an uploaded payload is not a readable image."""
-
-
-class UnknownMethod(ValueError):
-    """Raised for a classification method outside CLASSIFY_METHODS."""
-
-
-class ImageExpired(KeyError):
-    """Raised when an image_id is no longer in the store."""
-
-
-@dataclass
-class EngineSettings:
-    weights_path: str = "runs/detect/train/weights/best.pt"
-    stock_dir: str = "stock"
-    stock_csv: str = "stock.csv"
-    # Measured on this project (Apple M5, bench_engine.py): CPU detects in ~27 ms
-    # p50 while MPS takes ~112 ms with identical boxes, so CPU is the default.
-    # Override with --device / DORAYAKI_DEVICE (cpu | mps | cuda | cuda:0).
-    device: Optional[str] = "cpu"
-    # Live-mode defaults, measured with stream_client.py --pace result:
-    # hybrid classifies at ~44 ms/crop p50 and is the most accurate method
-    # (21/21 SKUs on image.jpg). hybrid_fast (ORB shortlist) is 3.4x faster but
-    # agreed with hybrid on only 47% of accepted crops, so it is NOT the default
-    # and stays available for experiments only (bench_engine.py --compare).
-    # Measured for a moving camera with hybrid classification: 16 in flight keeps
-    # the queue draining while detection keeps its share of the CPU. A demo walk
-    # over a nearly static shelf wants more; that is a CLI override, not a default.
-    live_inflight: int = 16
-    live_attempts: int = 2
-    # Which mechanism names products. `local` is the RootSIFT lane behind a
-    # calibrated open-set gate: measured on the corrected image6 labels it shows
-    # 36 of 61 in-catalog facings at 95% precision with the text lane, against
-    # 35 at 35% for Van's always-argmax matcher. `matcher` keeps Van's composite
-    # as a selectable baseline lane (E-1). `van` is that same scorer with the
-    # gate removed - it answers its top SKU every time, which is Van's actual
-    # contract and the only way to reproduce the stream comparison in
-    # DECISION.md. `gallery` is fenced off: raw DINOv2 is 12-21% top-1 on 7
-    # SKUs and its recall@10 falls to 7/42 at 493 SKUs.
-    identity_backend: str = "local"
-    # Cosine similarity below which the gallery calls a facing unknown. Measured:
-    # same-product pairs sit above 0.6, different products below 0.5.
-    gallery_threshold: float = 0.55
-    gallery_head_threshold: float = 0.40
-    gallery_image_size: int = 518
-    imgsz: int = 640
-    # 0 -> max(2, cpu_count - 2). Measured default below (see build_default_engine).
-    classify_workers: int = 5
-    live_method: str = "hybrid"
-    live_conf: float = 0.1
-    detect_conf: float = 0.25
-    detect_iou: float = 0.45
-    image_store_max: int = DEFAULT_IMAGE_STORE_MAX
-    image_store_ttl_s: float = DEFAULT_IMAGE_STORE_TTL_S
-    image_store_max_bytes: int = DEFAULT_IMAGE_STORE_MAX_BYTES
-
-
-@dataclass
-class StoredImage:
-    image_id: str
-    image: Any  # numpy BGR array
-    width: int
-    height: int
-    detections: List[Dict[str, Any]] = field(default_factory=list)
-    created_at: float = 0.0
-    nbytes: int = 0
-    # The tag pass for this still (tags, bands, box->tag), kept so a later
-    # classify can re-reconcile against the identities it just produced.
-    tag_bundle: Optional[Dict[str, Any]] = None
-
-
-def group_by_sku(scores: Dict[str, float]) -> Dict[str, float]:
-    """Collapse per-view template names onto their canonical SKU.
-
-    A SKU's score is its best view, which is also how the gallery ranks several
-    views. `sku_l01_1` and `sku_l01_2` become one `sku_l01`, so two views of one
-    product can no longer compete with each other and no view-suffixed id can
-    reach the wire.
-    """
-    from rebuild_catalog import sku_of
-
-    grouped: Dict[str, float] = {}
-    for name, value in (scores or {}).items():
-        number = float(value)
-        sku = sku_of(str(name))
-        grouped[sku] = max(grouped.get(sku, 0.0), number)
-    return grouped
-
-
-def identity_state_from_level(level: Optional[str]) -> str:
-    """Map a decide_identity() level to the wire state vocabulary."""
-    if level == "accepted":
-        return STATE_RECOGNIZED
-    if level in ("ambiguous", "unknown"):
-        return STATE_NEEDS_CONFIRMATION
-    return STATE_UNRECOGNIZED
-
-
-def identity_payload(decision: Optional[Dict[str, Any]], *, attempts: int = 1) -> Dict[str, Any]:
-    """One wire-shaped identity object from a decide_identity() result."""
-    if not decision:
-        return {
-            "state": STATE_DETECTED,
-            "sku_id": None,
-            "identity_level": None,
-            "confidence": None,
-            "score": None,
-            "candidates": [],
-            "attempts": 0,
-            "method": None,
-        }
-    level = decision.get("identity_level")
-    state = identity_state_from_level(level)
-    candidates = [
-        {"sku_id": row.get("sku_id"), "score": row.get("score")}
-        for row in (decision.get("ranked_candidates") or [])[:3]
-    ]
-    return {
-        "state": state,
-        "sku_id": decision.get("best_sku") if state == STATE_RECOGNIZED else None,
-        "identity_level": level,
-        "confidence": decision.get("confidence"),
-        "score": decision.get("score"),
-        "candidates": candidates,
-        "attempts": attempts,
-        "method": decision.get("method"),
-    }
-
-
-class ImageStore:
-    """Bounded LRU of decoded still images keyed by a random image_id."""
-
-    def __init__(self, max_items: int = DEFAULT_IMAGE_STORE_MAX,
-                 ttl_s: float = DEFAULT_IMAGE_STORE_TTL_S,
-                 max_bytes: int = DEFAULT_IMAGE_STORE_MAX_BYTES):
-        self.max_items = max(1, int(max_items))
-        self.ttl_s = float(ttl_s)
-        self.max_bytes = max(1, int(max_bytes))
-        self._items: "collections.OrderedDict[str, StoredImage]" = collections.OrderedDict()
-        self._bytes = 0
-        self._lock = threading.Lock()
-
-    def put(self, image: Any, detections: Optional[List[Dict[str, Any]]] = None) -> StoredImage:
-        height, width = image.shape[:2]
-        stored = StoredImage(
-            image_id="img_" + os.urandom(8).hex(),
-            image=image,
-            width=int(width),
-            height=int(height),
-            detections=list(detections or []),
-            created_at=time.time(),
-            nbytes=int(image.nbytes),
-        )
-        with self._lock:
-            self._evict_locked()
-            self._items[stored.image_id] = stored
-            self._bytes += stored.nbytes
-            while len(self._items) > self.max_items or self._bytes > self.max_bytes:
-                if len(self._items) <= 1:
-                    break
-                _, dropped = self._items.popitem(last=False)
-                self._bytes -= dropped.nbytes
-        return stored
-
-    def get(self, image_id: str) -> StoredImage:
-        with self._lock:
-            stored = self._items.get(image_id)
-            if stored is None or time.time() - stored.created_at > self.ttl_s:
-                if stored is not None:
-                    self._items.pop(image_id, None)
-                    self._bytes -= stored.nbytes
-                raise ImageExpired(image_id)
-            self._items.move_to_end(image_id)
-            return stored
-
-    def update_detections(self, image_id: str, detections: List[Dict[str, Any]]) -> None:
-        with self._lock:
-            stored = self._items.get(image_id)
-            if stored is not None:
-                stored.detections = list(detections)
-
-    def set_tag_bundle(self, image_id: str, bundle: Dict[str, Any]) -> None:
-        with self._lock:
-            stored = self._items.get(image_id)
-            if stored is not None:
-                stored.tag_bundle = bundle
-
-    def get_tag_bundle(self, image_id: str) -> Optional[Dict[str, Any]]:
-        with self._lock:
-            stored = self._items.get(image_id)
-            return stored.tag_bundle if stored is not None else None
-
-    def _evict_locked(self) -> None:
-        now = time.time()
-        for image_id in [k for k, v in self._items.items() if now - v.created_at > self.ttl_s]:
-            dropped = self._items.pop(image_id)
-            self._bytes -= dropped.nbytes
-
-
-class RecognitionEngine:
-    """Thread-safe facade over detector + matcher + catalog."""
-
-    def __init__(
-        self,
-        detector: Any,
-        matcher: Any,
-        inventory: Any,
-        *,
-        settings: Optional[EngineSettings] = None,
-        classify_executor: Optional[ThreadPoolExecutor] = None,
-        owns_executor: bool = True,
-    ):
-        self.detector = detector
-        self.matcher = matcher
-        self.inventory = inventory
-        self.settings = settings or EngineSettings()
-        # The embedding gallery is built on first use (it loads a model and every
-        # enrolled view), and cached as False when it cannot be built at all.
-        self._gallery: Any = None
-        self._gallery_lock = threading.Lock()
-        # The local identity lane, built on first use. It loads every reference
-        # view and its descriptors, which is why it is lazy like the gallery.
-        self._lane: Any = None
-        self._lane_lock = threading.Lock()
-        # The text lane, built on first use from the catalog and the OCR engine.
-        self._text_lane: Any = None
-        self._text_lane_lock = threading.Lock()
-        workers = self.settings.classify_workers or max(2, (os.cpu_count() or 4) - 2)
-        self.classify_workers = int(workers)
-        self._executor = classify_executor or ThreadPoolExecutor(
-            max_workers=self.classify_workers, thread_name_prefix="classify"
-        )
-        self._owns_executor = owns_executor
-        self._detect_lock = threading.Lock()
-        self._inventory_lock = threading.Lock()
-        # OCR is the slowest stage (0.2-3.4 s). It gets its own executor so tag
-        # passes can never occupy a classification worker, and a lock so several
-        # live sessions do not pile OCR requests onto the CPU at once.
-        self.ocr_engine: Optional[Any] = None
-        self.ocr_lock = threading.Lock()
-        self._tag_executor = ThreadPoolExecutor(max_workers=1, thread_name_prefix="tags")
-        self._owns_tag_executor = True
-        self.image_store = ImageStore(
-            self.settings.image_store_max,
-            self.settings.image_store_ttl_s,
-            self.settings.image_store_max_bytes,
-        )
-        self._catalog_cache: Optional[Tuple[str, List[Dict[str, Any]]]] = None
-        # (version, csv mtime_ns, csv size, sorted image name:size). Version is
-        # the content digest; the rest is the cheap fingerprint that invalidates it.
-        self._catalog_version_cache: Optional[Tuple[str, int, int, Tuple[Tuple[str, int], ...]]] = None
-        self._catalog_version_lock = threading.Lock()
-        self._started_at = time.time()
-
-    # ------------------------------------------------------------------ lifecycle
-    @property
-    def executor(self) -> ThreadPoolExecutor:
-        return self._executor
-
-    @property
-    def tag_executor(self) -> ThreadPoolExecutor:
-        return self._tag_executor
-
-    def set_ocr_engine(self, engine: Any) -> None:
-        """Attach an OCR backend; None disables tag reading."""
-        self.ocr_engine = engine
-
-    def read_tags(self, image: Any) -> Any:
-        """OCR one crop under the shared lock (Vision is happy with concurrency,
-        but the CPU is not: one OCR at a time keeps detection responsive)."""
-        if self.ocr_engine is None:
-            return None
-        with self.ocr_lock:
-            return self.ocr_engine.read(image)
-
-    def warm_up(self) -> float:
-        """One dummy predict so the first real frame is not the slow one.
-
-        The identity lanes are built here too. `TextLane.from_catalog` OCRs
-        every enrolled packshot, which is seconds of work; left lazy, the first
-        live frame would pay it inside a classify worker while the phone waits.
-        """
-        import numpy as np
-
-        blank = np.zeros((self.settings.imgsz, self.settings.imgsz, 3), dtype="uint8")
-        started = time.perf_counter()
-        self.detect(blank, conf=self.settings.detect_conf)
-        # A lane that cannot build here is not fatal: the lazy builders retry it
-        # on first use, so a bad stock dir or a Vision failure degrades the
-        # backend instead of refusing to start the server.
-        for name, build in (("local", self.identity_lane), ("text", self.identity_text_lane)):
-            try:
-                build()
-            except Exception as exc:
-                LOGGER.warning("identity %s lane not warmed: %s", name, exc)
-        return (time.perf_counter() - started) * 1000.0
-
-    def shutdown(self) -> None:
-        if self._owns_executor:
-            self._executor.shutdown(wait=False, cancel_futures=True)
-        if self._owns_tag_executor:
-            self._tag_executor.shutdown(wait=False, cancel_futures=True)
-
-    # ------------------------------------------------------------------ images
-    def decode_image(self, payload: bytes) -> Any:
-        import cv2
-        import numpy as np
-
-        if not payload:
-            raise UndecodableImage("empty payload")
-        array = np.frombuffer(payload, dtype=np.uint8)
-        image = cv2.imdecode(array, cv2.IMREAD_COLOR)
-        if image is None or image.size == 0:
-            raise UndecodableImage("payload is not a decodable image")
-        return image
-
-    def encode_jpeg(self, image: Any, quality: int = 85) -> bytes:
-        import cv2
-
-        ok, buffer = cv2.imencode(".jpg", image, [cv2.IMWRITE_JPEG_QUALITY, quality])
-        if not ok:
-            return b""
-        return bytes(buffer)
-
-    def encode_jpeg_data_url(self, image: Any, quality: int = 85) -> str:
-        payload = self.encode_jpeg(image, quality=quality)
-        if not payload:
-            return ""
-        return "data:image/jpeg;base64," + base64.b64encode(payload).decode("ascii")
-
-    # ------------------------------------------------------------------ detection
-    def detect(self, image: Any, conf: Optional[float] = None) -> List[Dict[str, Any]]:
-        """Runs the detector under a lock; returns normalized box dicts."""
-        threshold = self.settings.detect_conf if conf is None else float(conf)
-        with self._detect_lock:
-            result = self.detector.detect(
-                image, conf_threshold=threshold, iou_threshold=self.settings.detect_iou
-            )
-        boxes = result.get("boxes") or []
-        normalized: List[Dict[str, Any]] = []
-        for index, box in enumerate(boxes):
-            x1, y1, x2, y2 = [int(v) for v in box["box"]]
-            if x2 <= x1 or y2 <= y1:
-                continue
-            normalized.append(
-                {
-                    "id": index,
-                    "box": [x1, y1, x2, y2],
-                    "confidence": float(box.get("confidence", 0.0)),
-                    "class_id": int(box.get("class_id", 0)),
-                    "width": x2 - x1,
-                    "height": y2 - y1,
-                    "center": [(x1 + x2) // 2, (y1 + y2) // 2],
-                }
-            )
-        for index, box in enumerate(normalized):
-            box["id"] = index
-        return normalized
-
-    def crop(self, image: Any, box: Sequence[int]) -> Any:
-        x1, y1, x2, y2 = [int(v) for v in box]
-        height, width = image.shape[:2]
-        x1 = max(0, min(x1, width))
-        x2 = max(0, min(x2, width))
-        y1 = max(0, min(y1, height))
-        y2 = max(0, min(y2, height))
-        return image[y1:y2, x1:x2].copy()
-
-    # ------------------------------------------------------------------ empty slots
-    def empty_slots(self, image_shape: Sequence[int], boxes: Sequence[Dict[str, Any]]) -> List[Dict[str, Any]]:
-        """Empty-shelf gaps, computed in a 2048 px-wide reference frame so the
-        detector's absolute pixel thresholds do not change with capture size."""
-        height, width = int(image_shape[0]), int(image_shape[1])
-        if not boxes or width <= 0 or height <= 0:
-            return []
-        scale = min(MAX_UPSCALE, REFERENCE_WIDTH / float(width))
-        ref_boxes = []
-        for box in boxes:
-            x1, y1, x2, y2 = [int(v) for v in box["box"]]
-            ref_boxes.append(
-                {
-                    "box": [
-                        int(round(x1 * scale)),
-                        int(round(y1 * scale)),
-                        int(round(x2 * scale)),
-                        int(round(y2 * scale)),
-                    ]
-                }
-            )
-        ref_shape = (int(round(height * scale)), int(round(width * scale)))
-        slots = self.detector.detect_empty_slots(ref_shape, ref_boxes)
-        if scale == 1.0:
-            return slots
-        inv = 1.0 / scale
-        for slot in slots:
-            x1, y1, x2, y2 = slot["box"]
-            slot["box"] = [int(round(x1 * inv)), int(round(y1 * inv)),
-                           int(round(x2 * inv)), int(round(y2 * inv))]
-            slot["width"] = int(round(slot["width"] * inv))
-            slot["height"] = int(round(slot["height"] * inv))
-            slot["center"] = [int(round(slot["center"][0] * inv)), int(round(slot["center"][1] * inv))]
-        return slots
-
-    # ------------------------------------------------------------------ classification
-    @property
-    def identity_scale(self) -> str:
-        """What the scores from `score_crop` mean.
-
-        `local` is the RootSIFT lane's margin-over-runner-up, `cosine` the
-        gallery's similarity, `inlier` Van's composite. The live path reads this
-        to pick the matching gate, so the three must never be mixed.
-        """
-        backend = (self.settings.identity_backend or "matcher").lower()
-        if backend == "gallery":
-            return "cosine"
-        if backend == "local":
-            return "local"
-        if backend == "van":
-            return "van"
-        return "inlier"
-
-    def identity_text_lane(self) -> Any:
-        """The text lane, built once from the catalog. None without OCR.
-
-        Signatures come from the catalog rows (their printed names plus the OCR
-        of their enrolled packshots), never from an evaluation photo, so a row
-        with no image can still be named. That is what lets the lane reject the
-        near-unknown siblings no geometric threshold can separate.
-        """
-        if self.identity_scale != "local":
-            return None
-        if self._text_lane is None:
-            with self._text_lane_lock:
-                if self._text_lane is None:
-                    from identity_lanes import TextLane
-
-                    self._text_lane = TextLane.from_catalog(
-                        self.catalog(), self.settings.stock_dir, self.ocr_engine,
-                        lock=self.ocr_lock,
-                    )
-                    LOGGER.info("identity text lane: %d signatures", len(self._text_lane.signatures))
-        return self._text_lane
-
-    def decide_crop(self, crop: Any, method: Optional[str] = None) -> Dict[str, Any]:
-        """One identity decision for one crop, fusing every lane that is on.
-
-        The local lane answers first and the text lane vetoes or fills in, which
-        is the measured design: geometry ranks fine-grained siblings correctly
-        and text is the only thing that rejects a sibling the catalog does not
-        know about. Without OCR the local lane stands alone.
-        """
-        from identity import decide_identity
-        from identity_lanes import LOCAL_ACCEPT, TEXT_MIN_SHARED, DecisionPolicy
-
-        if self.identity_scale != "local":
-            return decide_identity(
-                self.score_crop(crop, method), method=method or self.settings.live_method,
-                scale=self.identity_scale,
-            )
-        lane = self.identity_lane()
-        if lane is None:
-            return decide_identity({}, method="local", scale="local")
-        text_lane = self.identity_text_lane()
-        text_rank: Any = None
-        read = ""
-        if text_lane is not None:
-            read = text_lane.read(crop)
-            text_rank = sorted(
-                text_lane.scores_from_text(read).items(), key=lambda kv: kv[1], reverse=True
-            )
-        policy = DecisionPolicy(
-            local_accept=float(os.environ.get("DORAYAKI_LOCAL_ACCEPT", LOCAL_ACCEPT)),
-            text_min_shared=int(os.environ.get("DORAYAKI_TEXT_MIN_SHARED", TEXT_MIN_SHARED)),
-            signatures=text_lane.signatures if text_lane else None,
-        )
-        return policy.decide(lane.rank(crop), text_rank, text_read=read)
-
-    def score_crop_lanes(
-        self, crop: Any, method: Optional[str] = None, skip_ocr: bool = False
-    ) -> Tuple[Dict[str, float], List[Tuple[str, float]], str]:
-        """Both lanes' evidence for one crop: local scores, text ranking, raw read.
-
-        The live path accumulates evidence across looks, so it needs the lanes
-        separately rather than one fused decision. How it combines them depends
-        on the scale: the local lane decides on the newest crop (its value is a
-        margin over the runner-up, and averaging in an empty observation halves
-        it below the accept line), while the cosine and inlier scales average
-        the scores over the track's window. The text reading is always the
-        newest. The raw read travels with the ranking because the decision needs
-        it to tell a product name from a size line. The still path calls
-        `decide_crop`, which fuses a single crop.
-        """
-        scores = self.score_crop(crop, method)
-        if skip_ocr:
-            return scores, [], ""
-        text_lane = self.identity_text_lane()
-        if text_lane is None:
-            return scores, [], ""
-        read = text_lane.read(crop)
-        return scores, sorted(
-            text_lane.scores_from_text(read).items(), key=lambda kv: kv[1], reverse=True
-        ), read
-
-    def identity_lane(self) -> Any:
-        """The local lane, built once. None when the backend is not `local`."""
-        if self.identity_scale != "local":
-            return None
-        if self._lane is None:
-            with self._lane_lock:
-                if self._lane is None:
-                    from identity_lanes import LocalLane
-
-                    self._lane = LocalLane(self.settings.stock_dir)
-                    LOGGER.info(
-                        "identity lane: %d reference views over %d SKUs",
-                        self._lane.view_count(),
-                        len(self._lane.sku_ids),
-                    )
-        return self._lane
-
-    def score_crop(self, crop: Any, method: Optional[str] = None) -> Dict[str, float]:
-        """Score one crop, keyed by canonical SKU, in the lane's rank order.
-
-        The insertion order is the ranking and callers rely on it: the local
-        lane's order comes from its rank key while its value is the margin over
-        the runner-up, so re-sorting by value names a different SKU than the
-        lane chose. `list(scores.items())` is therefore the ranked sequence.
-
-        Every backend groups by SKU before returning. Scoring each reference
-        view separately is what made the demo's default catalog report 22-29 of
-        109 facings as "ambiguous" when the top two candidates were two views of
-        the same product, and what put view-suffixed ids like `sku_l01_1` on the
-        wire, where no catalog row exists to supply a name or price.
-        """
-        if self.identity_scale == "cosine":
-            gallery = self.identity_gallery()
-            if not gallery:
-                return {}
-            match = gallery.match(crop)
-            if match.rejected or not match.sku_id:
-                return {}
-            scores = {match.sku_id: match.similarity}
-            if match.runner_up:
-                scores[match.runner_up] = match.runner_up_similarity
-            return {str(sku): float(score) for sku, score in scores.items()}
-        if self.identity_scale == "local":
-            lane = self.identity_lane()
-            if lane is None:
-                return {}
-            return {sku: float(value) for sku, value in lane.rank(crop)}
-        method = (method or self.settings.live_method).lower()
-        if method not in CLASSIFY_METHODS:
-            raise UnknownMethod(method)
-        if method == "hybrid_fast":
-            scores, _ = self.matcher.score_crop_fast(crop)
-        else:
-            scores, _ = self.matcher.score_crop(crop, method=method)
-        return group_by_sku(scores)
-
-    def identity_gallery(self) -> Any:
-        """The embedding gallery, loaded once (None when unavailable)."""
-        if self._gallery is False:
-            return None
-        if self._gallery is not None:
-            return self._gallery
-        with self._gallery_lock:
-            if self._gallery is False:
-                return None
-            if self._gallery is not None:
-                return self._gallery
-            from gallery import ProductGallery
-
-            gallery = ProductGallery(
-                self.settings.stock_dir,
-                [product["sku_id"] for product in self.catalog()],
-                threshold=self.settings.gallery_threshold,
-                head_threshold=self.settings.gallery_head_threshold,
-                image_size=self.settings.gallery_image_size,
-            )
-            try:
-                loaded = gallery.load()
-            except Exception as exc:  # torch/timm missing, no weights, no network
-                LOGGER.warning("identity gallery unavailable: %s", exc)
-                loaded = False
-            # False means tried and failed. Do not retry and do not use the matcher.
-            self._gallery = gallery if loaded else False
-            if loaded:
-                LOGGER.info(
-                    "identity gallery: %d views over %d SKUs on %s",
-                    gallery.view_count(),
-                    len(gallery.sku_ids),
-                    gallery.device,
-                )
-                if gallery.head_reason != "ok":
-                    LOGGER.warning("identity head: %s (%s)", gallery.head_reason, gallery._head_path)
-            else:
-                LOGGER.warning("identity gallery unavailable; gallery requests stay unrecognized")
-            return gallery if loaded else None
-
-    def classify_boxes(
-        self,
-        image: Any,
-        boxes: Sequence[Dict[str, Any]],
-        method: Optional[str] = None,
-    ) -> List[Dict[str, Any]]:
-        """Classifies every box in parallel; returns one entry per box, ordered by area."""
-        method = (method or self.settings.live_method).lower()
-        if method not in CLASSIFY_METHODS:
-            raise UnknownMethod(method)
-        if not boxes:
-            return []
-
-        def area_of(box: Dict[str, Any]) -> int:
-            if "width" in box and "height" in box:
-                return int(box["width"]) * int(box["height"])
-            x1, y1, x2, y2 = [int(v) for v in box["box"]]
-            return max(0, x2 - x1) * max(0, y2 - y1)
-
-        ordered = sorted(boxes, key=area_of, reverse=True)
-        crops = [self.crop(image, box["box"]) for box in ordered]
-
-        if self.identity_scale == "cosine":
-            gallery = self.identity_gallery()
-            # No matcher fallback: an unavailable gallery is unrecognized, not a different model.
-            return self._classify_with_gallery(gallery, ordered, crops)
-        from identity import decide_identity
-
-        # Every backend goes through decide_crop, so the scores are keyed by
-        # canonical SKU and the text lane is fused in whichever lane produced them.
-        futures = [self._executor.submit(self.decide_crop, crop, method) for crop in crops]
-        results: List[Dict[str, Any]] = []
-        for box, future in zip(ordered, futures):
-            try:
-                decision = future.result()
-            except Exception as exc:  # a single bad crop must not fail the batch
-                LOGGER.debug("classification failed for box %s: %s", box.get("id"), exc)
-                # A rejected decision, not the "not yet classified" payload: an
-                # operational failure must stay distinguishable from absence.
-                decision = decide_identity({}, method=method, scale=self.identity_scale)
-            decision = identity_payload(decision)
-            row = {
-                "detection_id": int(box["id"]),
-                "box": list(box["box"]),
-                "identity": decision,
-            }
-            # The catalog row travels with an accepted answer. Without it the
-            # phone gets a bare SKU id and has to look the name and price up
-            # itself, which is what left 41 of 41 accepted still-image answers
-            # without a product row.
-            if decision.get("state") == STATE_RECOGNIZED:
-                product = self.product(decision.get("sku_id"))
-                if product is not None:
-                    row["product"] = product
-            results.append(row)
-        return results
-
-    def inspect(self, image: Any, box: Sequence[int], method: Optional[str] = None) -> Dict[str, Any]:
-        """Full single-crop result, using the same backend as recognize/live."""
-        from identity import decision_for_match
-
-        crop = self.crop(image, box)
-        if self.identity_scale == "cosine":
-            gallery = self.identity_gallery()
-            match = gallery.match(crop) if gallery else None
-            # match() already applied the gallery's own threshold. Ranking again
-            # with the published cosine gates must not invent a second line.
-            decision = decision_for_match(match)
-            return self._inspect_payload(box, "gallery", decision, crop, visualize=False)
-        method = (method or self.settings.live_method).lower()
-        if method not in CLASSIFY_METHODS:
-            raise UnknownMethod(method)
-        decision = self.decide_crop(crop, method)
-        visual_method = "hybrid" if method == "hybrid_fast" else method
-        return self._inspect_payload(box, method, decision, crop, visual_method=visual_method)
-
-    def _inspect_payload(
-        self,
-        box: Sequence[int],
-        method: str,
-        decision: Dict[str, Any],
-        crop: Any,
-        *,
-        visual_method: Optional[str] = None,
-        visualize: bool = True,
-    ) -> Dict[str, Any]:
-        identity = identity_payload(decision)
-        top_details = decision.get("details") or {}
-        sub_details = None
-        if isinstance(top_details, dict):
-            raw = top_details.get("sub_details")
-            if raw:
-                sub_details = {key: dict(value) for key, value in raw.items()}
-
-        product = None
-        visualization = None
-        visualized_sku = None
-        is_candidate = False
-        if identity["state"] == STATE_RECOGNIZED:
-            product = self.product(identity["sku_id"])
-            visualized_sku = identity["sku_id"]
-        elif identity["candidates"]:
-            visualized_sku = identity["candidates"][0]["sku_id"]
-            is_candidate = True
-        if visualize and visualized_sku and visual_method:
-            try:
-                visualization = self.matcher.generate_match_visualization(
-                    crop, visualized_sku, method=visual_method
-                )
-            except Exception:
-                visualization = None
-
-        return {
-            "box": [int(value) for value in box],
-            "method": method,
-            "identity": identity,
-            "sub_details": sub_details,
-            "product": product,
-            "visualized_sku_id": visualized_sku,
-            "is_candidate": is_candidate,
-            "crop_jpeg_b64": self.encode_jpeg_data_url(crop),
-            "match_visualization_jpeg_b64": (
-                self.encode_jpeg_data_url(visualization) if visualization is not None else None
-            ),
-        }
-
-    # ------------------------------------------------------------------ catalog
-    def catalog_version(self) -> str:
-        """12-hex digest of stock.csv bytes plus template image names and sizes.
-
-        Unchanged files reuse the digest. A stock update that has not been written
-        back to the CSV still changes the digest, because the in-memory fingerprint
-        is dropped. Same-size content rewritten without a new mtime is not detected
-        until the next stock update or a real stat change.
-        """
-        csv_path = os.path.join(self.settings.stock_dir, self.settings.stock_csv)
-        try:
-            stat = os.stat(csv_path)
-            csv_key = (stat.st_mtime_ns, stat.st_size)
-        except OSError:
-            csv_key = None
-        try:
-            entries = tuple(sorted(
-                (name, os.path.getsize(os.path.join(self.settings.stock_dir, name)))
-                for name in os.listdir(self.settings.stock_dir)
-                if name.lower().endswith((".jpeg", ".jpg", ".png"))
-            ))
-        except OSError:
-            entries = ()
-        fingerprint = (
-            csv_key[0] if csv_key else -1,
-            csv_key[1] if csv_key else -1,
-            entries,
-        )
-        with self._catalog_version_lock:
-            cached = self._catalog_version_cache
-            if cached is not None and csv_key is not None and cached[1:] == fingerprint:
-                return cached[0]
-        digest = hashlib.sha256()
-        if csv_key is None:
-            digest.update(b"missing-csv")
-        else:
-            try:
-                with open(csv_path, "rb") as handle:
-                    digest.update(handle.read())
-            except OSError:
-                digest.update(b"missing-csv")
-                csv_key = None
-        for name, size in entries:
-            digest.update(f"{name}:{size}".encode("utf-8"))
-        version = digest.hexdigest()[:12]
-        if csv_key is not None:
-            with self._catalog_version_lock:
-                self._catalog_version_cache = (version, csv_key[0], csv_key[1], entries)
-        return version
-
-    def model_version(self) -> str:
-        try:
-            import ultralytics
-
-            library = f"ultralytics-{ultralytics.__version__}"
-        except Exception:
-            library = "ultralytics-unknown"
-        return f"{os.path.basename(self.settings.weights_path)}@{library}"
-
-    def catalog(self) -> List[Dict[str, Any]]:
-        version = self.catalog_version()
-        cached = self._catalog_cache
-        if cached and cached[0] == version:
-            return cached[1]
-        products: List[Dict[str, Any]] = []
-        for sku_id in sorted(self.inventory.list_all_skus()):
-            product = self.inventory.get_product(sku_id) or {}
-            products.append(self._product_payload(sku_id, product))
-        self._catalog_cache = (version, products)
-        return products
-
-    def product(self, sku_id: Optional[str]) -> Optional[Dict[str, Any]]:
-        if not sku_id:
-            return None
-        with self._inventory_lock:
-            product = self.inventory.get_product(sku_id)
-            if product is None:
-                return None
-            payload = self._product_payload(sku_id, product)
-        self._catalog_cache = None
-        return payload
-
-    def _product_payload(self, sku_id: str, product: Dict[str, Any]) -> Dict[str, Any]:
-        return {
-            "sku_id": sku_id,
-            "product_name": product.get("product_name", ""),
-            "price": product.get("price", 0.0),
-            "number_in": int(product.get("number_in", 0)),
-            "number_out": int(product.get("number_out", 0)),
-            "number_on": int(product.get("number_on", 0)),
-            "receipt_date": product.get("receipt_date", ""),
-            "expiration_date": product.get("expiration_date", ""),
-            "image_url": f"/v1/catalog/{sku_id}/image",
-        }
-
-    def catalog_image_path(self, sku_id: str) -> Optional[str]:
-        """Resolve a template image path through the inventory map only."""
-        with self._inventory_lock:
-            product = self.inventory.get_product(sku_id)
-            if product is None:
-                return None
-            path = product.get("local_image_path")
-        if not path or not os.path.exists(path):
-            return None
-        return path
-
-    def update_stock(self, sku_id: str, delta: int) -> Optional[Dict[str, Any]]:
-        with self._inventory_lock:
-            ok = self.inventory.update_stock(sku_id, int(delta))
-            if not ok:
-                return None
-            product = self.inventory.get_product(sku_id) or {}
-            payload = self._product_payload(sku_id, product)
-        self._catalog_cache = None
-        with self._catalog_version_lock:
-            self._catalog_version_cache = None
-        return payload
-
-    def sku_count(self) -> int:
-        return len(self.inventory.list_all_skus())
-
-    def uptime_s(self) -> float:
-        return time.time() - self._started_at
-
-    # ------------------------------------------------------------------ recognize
-    def recognize(
-        self,
-        image: Any,
-        *,
-        classify: bool = True,
-        method: Optional[str] = None,
-        conf: Optional[float] = None,
-        with_empty_slots: bool = True,
-    ) -> Dict[str, Any]:
-        """Stateless still-image recognition (photo mode and one-shot scans)."""
-        started = time.perf_counter()
-        boxes = self.detect(image, conf=conf)
-        detected_at = time.perf_counter()
-        detections: List[Dict[str, Any]] = []
-        identities: List[Dict[str, Any]] = []
-        if classify and boxes:
-            identities = self.classify_boxes(image, boxes, method=method)
-        identities_by_id = {row["detection_id"]: row for row in identities}
-        for box in boxes:
-            row = identities_by_id.get(box["id"])
-            detection = {
-                "id": box["id"],
-                "box": box["box"],
-                "confidence": box["confidence"],
-                "identity": (row or {}).get("identity") or identity_payload(None),
-            }
-            if (row or {}).get("product") is not None:
-                detection["product"] = row["product"]
-            detections.append(detection)
-        slots = self.empty_slots(image.shape, boxes) if with_empty_slots else []
-        classified_at = time.perf_counter()
-        summary = self.summarize(detections, slots)
-        stored = self.image_store.put(image, detections)
-        return {
-            "image_id": stored.image_id,
-            "width": stored.width,
-            "height": stored.height,
-            "catalog_version": self.catalog_version(),
-            "model_version": self.model_version(),
-            "coordinate_convention": COORDINATE_CONVENTION,
-            "detections": detections,
-            "empty_slots": slots,
-            "summary": summary,
-            "timing_ms": {
-                "detect": round((detected_at - started) * 1000.0, 2),
-                "classify": round((classified_at - detected_at) * 1000.0, 2),
-                "total": round((classified_at - started) * 1000.0, 2),
-            },
-        }
-
-    def _classify_with_gallery(
-        self,
-        gallery: Any,
-        ordered: Sequence[Dict[str, Any]],
-        crops: Sequence[Any],
-    ) -> List[Dict[str, Any]]:
-        """Identity by retrieval: nearest enrolled view, or an honest unknown.
-
-        The gallery scores a SKU by its best view and rejects when nothing is close
-        enough, which is the behaviour the matcher cannot provide - measured on 54
-        unseen store photos, the matcher labelled milk cartons as mayonnaise and its
-        score could not be thresholded to stop it.
-        """
-        from identity import decision_for_match
-
-        results: List[Dict[str, Any]] = []
-        for box, crop in zip(ordered, crops):
-            match = None
-            if gallery is not None:
-                try:
-                    match = gallery.match(crop)
-                except Exception as exc:  # a bad crop must not fail the batch
-                    match = None
-                    LOGGER.debug("gallery match failed: %s", exc)
-            decision = decision_for_match(match)
-            results.append({
-                "detection_id": int(box["id"]),
-                "box": list(box["box"]),
-                "identity": identity_payload(decision),
-            })
-        return results
-
-    @staticmethod
-    def summarize(
-        detections: Sequence[Dict[str, Any]],
-        slots: Sequence[Dict[str, Any]],
-        *,
-        include_identifying: bool = False,
-    ) -> Dict[str, Any]:
-        counts = {
-            STATE_RECOGNIZED: 0,
-            STATE_NEEDS_CONFIRMATION: 0,
-            STATE_UNRECOGNIZED: 0,
-            STATE_DETECTED: 0,
-        }
-        if include_identifying:
-            counts[STATE_IDENTIFYING] = 0
-        facings: Dict[str, int] = {}
-        for row in detections:
-            state = (row.get("identity") or {}).get("state") or STATE_DETECTED
-            if state not in counts:
-                state = STATE_DETECTED
-            counts[state] += 1
-            sku_id = (row.get("identity") or {}).get("sku_id")
-            if state == STATE_RECOGNIZED and sku_id:
-                facings[sku_id] = facings.get(sku_id, 0) + 1
-        summary: Dict[str, Any] = {
-            "in_view": len(detections),
-            "recognized": counts[STATE_RECOGNIZED],
-            "needs_confirmation": counts[STATE_NEEDS_CONFIRMATION],
-            "unrecognized": counts[STATE_UNRECOGNIZED],
-            "detected": counts[STATE_DETECTED],
-            "empty_slots": len(slots),
-            "by_sku": [
-                {"sku_id": sku, "facings": count}
-                for sku, count in sorted(facings.items(), key=lambda kv: (-kv[1], kv[0]))
-            ],
-        }
-        if include_identifying:
-            summary["identifying"] = counts[STATE_IDENTIFYING]
-        return summary
-
-
-def build_default_engine(settings: Optional[EngineSettings] = None) -> RecognitionEngine:
-    """Construct the real detector/matcher/inventory. Heavy imports happen here."""
-    import sys
-
-    settings = settings or EngineSettings()
-    # Detection and classification share the CPU. torch defaults to every core,
-    # which starves the classification pool (and the pool then slows detection
-    # back). Measured on the M5: capping torch at 4 threads and classifying with
-    # 5 workers gave 12.2 results/s single-client (46 ms detect) versus 11.1/s
-    # (49 ms) uncapped, and 8.5/s each for two clients. Override with
-    # DORAYAKI_TORCH_THREADS (0 = leave torch's default).
-    torch_threads = int(os.environ.get("DORAYAKI_TORCH_THREADS", "4") or 0)
-    if torch_threads > 0:
-        import torch
-
-        torch.set_num_threads(torch_threads)
-    current_dir = os.path.dirname(os.path.abspath(__file__))
-    if current_dir not in sys.path:
-        sys.path.insert(0, current_dir)
-
-    from detector import ShelfProductDetector
-    from feature_matcher import ProductFeatureMatcher
-    from inventory import InventoryManager
-
-    detector = ShelfProductDetector(
-        weights_path=settings.weights_path, device=settings.device, imgsz=settings.imgsz
-    )
-    matcher = ProductFeatureMatcher(stock_dir=settings.stock_dir)
-    inventory = InventoryManager(stock_dir=settings.stock_dir, csv_filename=settings.stock_csv)
-    engine = RecognitionEngine(detector, matcher, inventory, settings=settings)
-
-    from ocr import build_ocr_engine
-
-    try:
-        ocr_engine = build_ocr_engine()
-    except Exception as exc:  # a broken OCR install must not stop recognition
-        print(f"[Engine] OCR disabled: {exc}")
-        ocr_engine = None
-    engine.set_ocr_engine(ocr_engine)
-    print(f"[Engine] OCR engine: {getattr(ocr_engine, 'name', 'none')}")
-    return engine
-
-
-def settings_from_env(env: Optional[Dict[str, str]] = None) -> EngineSettings:
-    """Build EngineSettings from DORAYAKI_* environment variables."""
-    env = dict(os.environ if env is None else env)
-    base = EngineSettings()
-    return replace(
-        base,
-        weights_path=env.get("DORAYAKI_WEIGHTS", base.weights_path),
-        stock_dir=env.get("DORAYAKI_STOCK_DIR", base.stock_dir),
-        device=env.get("DORAYAKI_DEVICE") or base.device,
-        imgsz=int(env.get("DORAYAKI_IMGSZ", base.imgsz)),
-        classify_workers=int(env.get("DORAYAKI_CLASSIFY_WORKERS", base.classify_workers)),
-        live_method=env.get("DORAYAKI_LIVE_METHOD", base.live_method),
-        live_conf=float(env.get("DORAYAKI_LIVE_CONF", base.live_conf)),
-        live_inflight=int(env.get("DORAYAKI_LIVE_INFLIGHT", base.live_inflight)),
-        live_attempts=int(env.get("DORAYAKI_LIVE_ATTEMPTS", base.live_attempts)),
-        identity_backend=env.get("DORAYAKI_IDENTITY", base.identity_backend),
-        detect_conf=float(env.get("DORAYAKI_DETECT_CONF", base.detect_conf)),
-    )
-
-
-if __name__ == "__main__":  # tiny smoke check: python code/engine.py
-    engine = build_default_engine(settings_from_env())
-    print("[Engine] warmup ms:", round(engine.warm_up(), 1))
-    print("[Engine] catalog:", engine.sku_count(), "skus, version", engine.catalog_version())
-    result = engine.recognize(engine.decode_image(open("image.jpg", "rb").read()))
-    print("[Engine] detected:", len(result["detections"]), "recognized:", result["summary"]["recognized"])
-    print("[Engine] timing:", json.dumps(result["timing_ms"]))
-    engine.shutdown()
diff --git a/recognization/code/eval_distractors.py b/recognization/code/eval_distractors.py
deleted file mode 100644
index edc4492..0000000
--- a/recognization/code/eval_distractors.py
+++ /dev/null
@@ -1,136 +0,0 @@
-"""Open-world false-accept probe: unrelated shelf photos through the shipped lane.
-
-data/external-shelves/ holds Wikimedia Commons supermarket photos of shelves
-that share nothing with the LOPIA catalog. Every accept here is a false accept
-by construction, which is what makes the set useful: the image6 shelf can only
-measure false accepts on the products it happens to contain.
-
-Measured the way the server measures: a 1280-long-side frame detected at
-`LiveSettings.conf`. The sources are 4032-4512 px photos, and detecting them at
-full resolution and at the default `detect_conf` instead measures a
-configuration the phone never runs, which is how a threshold change reads as a
-pass on evidence the product does not produce.
-
-    .venv/bin/python code/eval_distractors.py --stock stock-lopia-clean \
-        data/external-shelves/wikimedia-commons
-"""
-
-from __future__ import annotations
-
-import argparse
-import glob
-import json
-import os
-import sys
-import time
-from typing import Any, Dict, List
-
-current_dir = os.path.dirname(os.path.abspath(__file__))
-if current_dir not in sys.path:
-    sys.path.insert(0, current_dir)
-
-import cv2  # noqa: E402
-
-from eval_identity import (  # noqa: E402
-    LIVE_LONG_SIDE,
-    LOCAL_ACCEPT,
-    LocalLaneAdapter,
-    at_live_resolution,
-)
-from identity_lanes import TEXT_MIN_SHARED  # noqa: E402
-
-
-def main(argv: List[str] | None = None) -> int:
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("directories", nargs="+")
-    parser.add_argument("--stock", default="stock-lopia-clean")
-    parser.add_argument("--write", action="store_true")
-    parser.add_argument("--limit", type=int, default=0, help="images per set (0 = all)")
-    args = parser.parse_args(argv)
-
-    from engine import build_default_engine, EngineSettings
-    from live import LiveSettings
-
-    engine = build_default_engine(EngineSettings(stock_dir=args.stock))
-    lane = LocalLaneAdapter(args.stock)
-    conf = LiveSettings().conf
-    for directory in args.directories:
-        manifest_path = os.path.join(directory, "manifest.json")
-        try:
-            with open(manifest_path) as handle:
-                manifest = json.load(handle)
-        except (OSError, ValueError):
-            manifest = {"images": []}
-        meta = {row["file"]: row for row in manifest.get("images", [])}
-        rows: List[Dict[str, Any]] = []
-
-        paths = sorted(
-            path for path in glob.glob(os.path.join(directory, "*"))
-            if os.path.splitext(path)[1].lower() in (".jpg", ".jpeg", ".png")
-        )
-        if args.limit:
-            paths = paths[: args.limit]
-        for path in paths:
-            name = os.path.basename(path)
-            image = cv2.imread(path)
-            if image is None:
-                continue
-            started = time.perf_counter()
-            frame = at_live_resolution(image)
-            detections = engine.detect(frame, conf=conf)
-            accepted = 0
-            for detection in detections:
-                box = detection.get("box") or detection.get("bbox")
-                if not box:
-                    continue
-                x1, y1, x2, y2 = [int(v) for v in box]
-                crop = frame[max(0, y1):y2, max(0, x1):x2]
-                if crop.size == 0:
-                    continue
-                decision = lane.decide(crop, lane.rank(crop), accept=LOCAL_ACCEPT,
-                                       margin=0.0, scale="full")
-                if decision.get("best_sku"):
-                    accepted += 1
-            rows.append({
-                "file": name,
-                "title": meta.get(name, {}).get("title", ""),
-                "detections": len(detections),
-                "false_accepts": accepted,
-                "ms": round((time.perf_counter() - started) * 1000),
-            })
-
-        detections = sum(r["detections"] for r in rows)
-        accepts = sum(r["false_accepts"] for r in rows)
-        report = {
-            "dataset": os.path.basename(directory.rstrip("/")),
-            "catalog": args.stock,
-            "note": "Unrelated shelves: every accept is a false accept by construction.",
-            # The configuration the numbers above were measured under, so the
-            # stored evidence is checkable against the constants it claims.
-            "config": {
-                "frame_long_side": LIVE_LONG_SIDE,
-                "detect_conf": conf,
-                "local_accept": LOCAL_ACCEPT,
-                "text_min_shared": TEXT_MIN_SHARED,
-            },
-            "images": len(rows),
-            "detections": detections,
-            "false_accepts": accepts,
-            "false_accept_rate": round(accepts / detections, 4) if detections else None,
-            "rows": rows,
-        }
-        print(f"{report['dataset']}: {len(rows)} images, {detections} detections, "
-              f"{accepts} false accepts ({report['false_accept_rate'] or 0:.2%})")
-        for row in sorted(rows, key=lambda r: -r["false_accepts"])[:5]:
-            if row["false_accepts"]:
-                print(f"    {row['false_accepts']:>3}  {row['file'][:55]}")
-        if args.write:
-            out = os.path.join(directory, "eval_result.json")
-            with open(out, "w") as handle:
-                json.dump(report, handle, ensure_ascii=False, indent=1)
-            print(f"    wrote {out}")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/recognization/code/eval_external.py b/recognization/code/eval_external.py
deleted file mode 100644
index 1635d98..0000000
--- a/recognization/code/eval_external.py
+++ /dev/null
@@ -1,127 +0,0 @@
-"""Score external-proxy image sets through the shipped identity lane.
-
-These sets are not Tier 2 (see data/external-eval/README.md). They exist to test
-the pipeline on data it had no part in building, so the one thing this script
-must never do is score an image that has since been enrolled - that is
-training-set accuracy wearing a held-out label.
-
-`leaked()` is the same check the shelf evaluator runs, and it is applied here
-for that reason: enrolling a packshot from an external set is a normal thing to
-do (it is how F-12 was closed), and it silently converts one of these rows into
-a leak unless something refuses it.
-
-    .venv/bin/python code/eval_external.py --stock stock-lopia-clean \
-        data/external-eval/kewpie data/external-eval/other-mayo
-"""
-
-from __future__ import annotations
-
-import argparse
-import glob
-import json
-import os
-import sys
-from typing import Any, Dict, List
-
-current_dir = os.path.dirname(os.path.abspath(__file__))
-if current_dir not in sys.path:
-    sys.path.insert(0, current_dir)
-
-import cv2  # noqa: E402
-
-from eval_identity import LOCAL_ACCEPT, LocalLaneAdapter, leaked  # noqa: E402
-
-
-def score_set(lane: Any, directory: str, stock_dir: str) -> Dict[str, Any]:
-    rows: List[Dict[str, Any]] = []
-    for path in sorted(glob.glob(os.path.join(directory, "*.jpg"))):
-        name = os.path.basename(path)
-        truth = "sku_" + name.split("_")[1]
-        image = cv2.imread(path)
-        if image is None:
-            rows.append({"file": name, "truth": truth, "verdict": "unreadable"})
-            continue
-        # The whole image is the crop: an external packshot is not a shelf facing.
-        box = (0, 0, image.shape[1], image.shape[0])
-        leak = leaked(image, box, stock_dir)
-        if leak:
-            rows.append({
-                "file": name, "truth": truth, "verdict": "leaked",
-                "leaked_into": leak,
-                "note": "enrolled in the catalog; scoring it would be training accuracy",
-            })
-            continue
-        decision = lane.decide(
-            image, lane.rank(image), accept=LOCAL_ACCEPT, margin=0.0, scale="full"
-        )
-        best = decision.get("best_sku")
-        verdict = "correct" if best == truth else ("abstain" if not best else "wrong")
-        rows.append({
-            "file": name, "truth": truth, "best": best,
-            "confidence": decision.get("confidence"),
-            "status": decision.get("status"),
-            "source": decision.get("source"),
-            "verdict": verdict,
-        })
-
-    scored = [r for r in rows if r["verdict"] in ("correct", "abstain", "wrong")]
-    counts = {v: sum(1 for r in scored if r["verdict"] == v)
-              for v in ("correct", "abstain", "wrong")}
-    # Per SKU, because a set total hides which products the lane can and cannot
-    # name. Every SKU here has images from two independent source domains, so a
-    # SKU that scores the same on both is not one lucky photograph.
-    by_sku: Dict[str, Dict[str, int]] = {}
-    for row in scored:
-        bucket = by_sku.setdefault(row["truth"], {"correct": 0, "abstain": 0, "wrong": 0})
-        bucket[row["verdict"]] += 1
-    return {
-        "dataset": os.path.basename(directory.rstrip("/")),
-        "catalog": stock_dir,
-        "note": "External proxy images, not Tier 2. Scored through the shipped "
-                "LocalLaneAdapter + DecisionPolicy.",
-        "rows": rows,
-        "scored": len(scored),
-        "leaked": sum(1 for r in rows if r["verdict"] == "leaked"),
-        "by_sku": by_sku,
-        **counts,
-    }
-
-
-def main(argv: List[str] | None = None) -> int:
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("directories", nargs="+", help="external set directories")
-    parser.add_argument("--stock", default="stock-lopia-clean")
-    parser.add_argument("--write", action="store_true",
-                        help="write eval_result.json into each set directory")
-    args = parser.parse_args(argv)
-
-    lane = LocalLaneAdapter(args.stock)
-    totals = {"correct": 0, "abstain": 0, "wrong": 0, "leaked": 0, "scored": 0}
-    for directory in args.directories:
-        report = score_set(lane, directory, args.stock)
-        for key in totals:
-            totals[key] += report[key]
-        print(f"{report['dataset']}: correct {report['correct']} | "
-              f"abstain {report['abstain']} | wrong {report['wrong']} | "
-              f"leaked {report['leaked']} of {report['scored']} scored")
-        for sku, bucket in sorted(report["by_sku"].items()):
-            print(f"    {sku:<10} correct {bucket['correct']}  "
-                  f"abstain {bucket['abstain']}  wrong {bucket['wrong']}")
-        for row in report["rows"]:
-            if row["verdict"] in ("wrong", "leaked"):
-                print(f"    {row['verdict'].upper():<7} {row['file']} "
-                      f"truth={row['truth']} best={row.get('best')} "
-                      f"{row.get('leaked_into', '')}")
-        if args.write:
-            out = os.path.join(directory, "eval_result.json")
-            with open(out, "w") as handle:
-                json.dump(report, handle, ensure_ascii=False, indent=1)
-            print(f"    wrote {out}")
-    print(f"\nTOTAL: {totals['scored']} scored, correct {totals['correct']}, "
-          f"abstain {totals['abstain']}, FALSE ACCEPTS {totals['wrong']}, "
-          f"leaked {totals['leaked']}")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/recognization/code/eval_external_images.py b/recognization/code/eval_external_images.py
deleted file mode 100644
index 2014b6a..0000000
--- a/recognization/code/eval_external_images.py
+++ /dev/null
@@ -1,59 +0,0 @@
-"""
-Run the pipeline over store photos it has never seen.
-
-    python code/eval_external_images.py [data/external-shelves/wikimedia-commons]
-
-Reports detection and identity per image, and writes a JSON summary. See
-docs/EXTERNAL-EVAL.md for what the numbers mean.
-
-The images live in the repository (`data/external-shelves/`), not in a home
-directory: they are evaluation evidence, and evidence that only exists on one
-machine cannot be re-checked.
-"""
-import json, os, sys, time
-sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
-import cv2, numpy as np
-from engine import build_default_engine, EngineSettings
-from tags import TagService, TagSettings
-from reconcile import attach_tags_to_boxes
-
-DEFAULT_ROOT = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))),
-                            "data", "external-shelves", "wikimedia-commons")
-ROOT = os.path.expanduser(sys.argv[1] if len(sys.argv) > 1 else DEFAULT_ROOT)
-
-engine = build_default_engine(EngineSettings(stock_dir="stock-lopia"))
-catalog_by_sku = {p["sku_id"]: p for p in engine.catalog()}
-service = TagService(engine.ocr_engine, settings=TagSettings())
-
-rows = []
-for entry in manifest["images"]:
-    path = os.path.join(ROOT, entry["file"])
-    image = cv2.imread(path)
-    if image is None:
-        continue
-    # keep the long side at 1600 so detection cost is comparable across images
-    height, width = image.shape[:2]
-    if max(height, width) > 1600:
-        scale = 1600.0 / max(height, width)
-        image = cv2.resize(image, (int(width*scale), int(height*scale)), interpolation=cv2.INTER_AREA)
-    started = time.perf_counter()
-    result = engine.recognize(image, classify=True, method="hybrid", with_empty_slots=True)
-    detections = result["detections"]
-    recognized = sum(1 for d in detections if (d.get("identity") or {}).get("state") == "recognized")
-    rejected = sum(1 for d in detections if (d.get("identity") or {}).get("state") == "rejected")
-    rows.append({
-        "file": entry["file"], "title": entry["title"], "license": entry["license"],
-        "size": f"{image.shape[1]}x{image.shape[0]}",
-        "detections": len(detections), "recognized": recognized, "rejected": rejected,
-        "ms": round((time.perf_counter()-started)*1000),
-    })
-    print(f"  {entry['file'][:34]:<34} {image.shape[1]}x{image.shape[0]:<5} "
-          f"det={len(detections):>4} rec={recognized:>3} rej={rejected:>3} {rows[-1]['ms']:>5} ms")
-
-total_det = sum(r["detections"] for r in rows)
-total_rec = sum(r["recognized"] for r in rows)
-with_det = sum(1 for r in rows if r["detections"] > 0)
-print(f"\n{len(rows)} images, {total_det} detections, {total_rec} recognized "
-      f"({100*total_rec//max(1,total_det)}%), images with any detection: {with_det}")
-json.dump(rows, open(os.path.join(ROOT, "eval.json"), "w"), ensure_ascii=False, indent=1)
-print(f"summary: {os.path.join(ROOT, 'eval.json')}")
diff --git a/recognization/code/eval_identity.py b/recognization/code/eval_identity.py
deleted file mode 100644
index 1763f8a..0000000
--- a/recognization/code/eval_identity.py
+++ /dev/null
@@ -1,788 +0,0 @@
-"""Score identity lanes on labelled shelf facings. Architecture-neutral.
-
-The original matcher is a candidate final architecture, not just a baseline.
-Every lane is scored the same way, on the same crops, at the same resolutions:
-
-    local          RootSIFT pyramid + OCR text lane (the shipped default)
-    matcher        full-catalog SIFT/ORB/BRISK + RANSAC (the original demo)
-    gallery        DINOv2 embedding, raw or with the trained head
-    hybrid-verify  DINO shortlist, then the matcher re-ranks inside it
-
-Reported per lane, as four separate ceilings rather than one conflated number:
-
-    catalog ceiling      in-catalog facings with an enrolled view / all in-catalog
-    identity ceiling     ungated top-1 rank correct on enrolled / enrolled
-    decision ceiling     accepted-and-correct on enrolled / enrolled
-    end-to-end coverage  accepted-and-correct / all in-catalog facings
-
-plus precision over every accept — including accepts of unenrolled in-catalog
-facings, which are false accepts and the most dangerous kind — the prediction
-histogram, and the largest single-SKU share of accepts (the L09-apocalypse
-detector). The hybrid lane also reports shortlist recall, because a true SKU
-outside the shortlist is a hard ceiling no verifier can lift.
-
-    python code/eval_identity.py \
-        --labels eval/labels/image6.json \
-        --image add/image6.jpg --stock-dir stock-lopia-clean --lanes matcher --live
-
-Labels are {"image", "facings": [{"box", "label"}]} with "unknown" for facings
-not in the catalog and "exclude" for rows two label passes disagreed on. A
-facing whose crop is a catalog template is refused as leakage, not scored:
-those crops are how the store was enrolled.
-"""
-
-from __future__ import annotations
-
-import argparse
-import csv
-import hashlib
-import json
-import os
-import subprocess
-import sys
-import time
-from collections import Counter
-from typing import Any, Collection, Dict, List, Optional, Sequence, Tuple
-
-current_dir = os.path.dirname(os.path.abspath(__file__))
-if current_dir not in sys.path:
-    sys.path.insert(0, current_dir)
-
-import cv2  # noqa: E402
-import numpy as np  # noqa: E402
-
-from rebuild_catalog import sku_of  # noqa: E402
-
-from identity import LOCAL_ACCEPT  # noqa: E402
-
-UNKNOWN = "unknown"
-# Two independent label passes disagreed here, so the row is not ground truth.
-# Scoring it would be scoring a coin flip.
-EXCLUDE = "exclude"
-LANES = ("local", "matcher", "van", "gallery", "hybrid-verify")
-# A single SKU above this share of accepts is the failure the last audit found:
-# every dark package collapsing onto one enrolled product.
-HUB_SHARE = 0.30
-# Live frames arrive at this long side; the original demo classified a 2048 px
-# still. Scoring both is how resolution stops being an invisible variable.
-LIVE_LONG_SIDE = 1280
-
-# Pixel equality is not a leak test: a template re-cut from the same photo with
-# a different box passes a same-shape comparison. A 64x64 grayscale signature
-# catches most of those, but it is a heuristic, not a proof — measured on
-# the corrected labels against stock-lopia, re-cuts in the realistic 0.94..1.06 box band
-# land 7..37 MAD from their facing's signature while the closest pair of
-# genuinely distinct same-SKU facings is 23.1, so the distributions overlap and
-# no threshold is clean. 20.0 catches 81% of that band with zero false
-# positives on 5,886 genuine facing pairs and 7 clean packshots. The real fix is
-# the enrollment protocol's session manifest (DECISION §8); this is the cheap
-# backstop that runs without one.
-LEAK_SIGNATURE_MAD = 20.0
-# A signature hit is confirmed by correlation before it counts as a leak. A
-# genuine re-cut, re-encode or rescale of an enrolled view correlates at 0.99+;
-# two different photographs of the same product sit near 0.3. Measured on the
-# l09 pair that the signature alone misjudged (0.28) and on re-encoded and
-# rescaled copies of the enrolled view (0.999, 0.997).
-LEAK_CORRELATION = 0.90
-
-
-def load_labels(path: str) -> Dict[str, Any]:
-    """Load a label file, refusing one whose rows carry no provenance.
-
-    F-01: the v1 labels were wrong on 23% of facings and nothing in the file
-    said where any of them came from, so a bad label was indistinguishable from
-    a good one. Every row must now name its `source`; a row that cannot be
-    traced is a row that cannot be argued with.
-    """
-    with open(path) as handle:
-        payload = json.load(handle)
-    if not isinstance(payload, dict) or "facings" not in payload:
-        raise SystemExit(f"{path}: expected {{'image', 'facings': [...]}}")
-    missing = [
-        row.get("index", i)
-        for i, row in enumerate(payload["facings"])
-        if not row.get("source")
-    ]
-    if missing:
-        raise SystemExit(
-            f"{path}: {len(missing)} facings carry no 'source' "
-            f"(first: index {missing[0]}). A label without provenance cannot be "
-            "audited; see the enrollment protocol in DECISION.md."
-        )
-    return payload
-
-
-def file_hash(path: str) -> Optional[str]:
-    """SHA-256 of a file's bytes, or None when it cannot be read."""
-    try:
-        with open(path, "rb") as handle:
-            return hashlib.sha256(handle.read()).hexdigest()
-    except OSError:
-        return None
-
-
-def catalog_hash(stock_dir: str) -> Optional[str]:
-    """SHA-256 over the catalog's CSV bytes and each view's bytes.
-
-    The view bytes are hashed, not just their names. Names alone were not
-    enough: replacing `sku_l09.jpg` with a different product's packshot left
-    the hash unchanged, so a report could claim the same catalog while every
-    l09 decision in it had moved. Re-encoding the same image does move the
-    hash, which is the price of not being able to lie about what was scored.
-    """
-    digest = hashlib.sha256()
-    csv_path = os.path.join(stock_dir, "stock.csv")
-    try:
-        with open(csv_path, "rb") as handle:
-            digest.update(handle.read())
-    except OSError:
-        digest.update(b"missing-csv")
-    try:
-        names = sorted(
-            name for name in os.listdir(stock_dir)
-            if os.path.splitext(name)[1].lower() in (".jpg", ".jpeg", ".png")
-        )
-    except OSError:
-        names = []
-    for name in names:
-        digest.update(b"\n")
-        digest.update(name.encode("utf-8"))
-        try:
-            with open(os.path.join(stock_dir, name), "rb") as handle:
-                digest.update(handle.read())
-        except OSError:
-            digest.update(b"unreadable")
-    return digest.hexdigest()
-
-
-def code_revision() -> Optional[str]:
-    """The checked-out revision, or None outside a git worktree."""
-    try:
-        result = subprocess.run(
-            ["git", "rev-parse", "HEAD"],
-            cwd=os.path.dirname(os.path.abspath(__file__)),
-            capture_output=True, text=True, timeout=10,
-        )
-    except (OSError, subprocess.SubprocessError):
-        return None
-    return result.stdout.strip() or None if result.returncode == 0 else None
-
-
-def crop_of(image: Any, box: Sequence[int]) -> Any:
-    x1, y1, x2, y2 = [int(v) for v in box]
-    return image[max(0, y1):y2, max(0, x1):x2]
-
-
-def at_live_resolution(image: Any, long_side: int = LIVE_LONG_SIDE) -> Any:
-    height, width = image.shape[:2]
-    if max(height, width) <= long_side:
-        return image
-    scale = long_side / float(max(height, width))
-    return cv2.resize(image, (int(width * scale), int(height * scale)), interpolation=cv2.INTER_AREA)
-
-
-def _signature(image: Any) -> Any:
-    """64x64 grayscale signature. Cheap, and tolerant of a re-cut."""
-    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) if image.ndim == 3 else image
-    return cv2.resize(gray, (64, 64), interpolation=cv2.INTER_AREA).astype(np.float32)
-
-
-def _signatures(image: Any) -> List[Any]:
-    """The crop's signature and a centre 90% crop's.
-
-    A re-cut box shifts the content inside the frame. Comparing a second,
-    tighter crop against the template absorbs that shift: measured on the
-    realistic re-cut band it catches 81% of re-cuts against 68% for the single
-    signature, at no cost in false positives.
-    """
-    height, width = image.shape[:2]
-    dy, dx = int(height * 0.05), int(width * 0.05)
-    centre = image[dy:height - dy, dx:width - dx]
-    return [_signature(image), _signature(centre if centre.size else image)]
-
-
-_TEMPLATES: Dict[str, List[Tuple[str, str, Tuple[int, int], List[Any]]]] = {}
-
-
-def _catalog_templates(stock_dir: str) -> List[Tuple[str, str, Tuple[int, int], List[Any]]]:
-    """(stem, name, shape, signatures) per catalog image, read once per stock dir.
-
-    Cached because the signature is what catches a re-cut crop, and re-reading
-    every template for every facing is what made the old check expensive.
-    """
-    cached = _TEMPLATES.get(stock_dir)
-    if cached is None:
-        cached = []
-        for name in sorted(os.listdir(stock_dir)):
-            stem, ext = os.path.splitext(name)
-            if ext.lower() not in (".jpg", ".jpeg", ".png"):
-                continue
-            template = cv2.imread(os.path.join(stock_dir, name))
-            if template is None:
-                continue
-            cached.append((stem, name, template.shape[:2], _signatures(template)))
-        _TEMPLATES[stock_dir] = cached
-    return cached
-
-
-def _correlation(left: Any, right: Any) -> float:
-    """Mean-subtracted correlation of two images, resizing `right` to `left`.
-
-    Scale- and re-encode-tolerant, unlike a pixel comparison: the enrolled view
-    and a re-saved or rescaled copy of it correlate at 0.99+, while two
-    different photographs of the same product sit near 0.3.
-    """
-    if right.shape[:2] != left.shape[:2]:
-        right = cv2.resize(right, (left.shape[1], left.shape[0]),
-                           interpolation=cv2.INTER_AREA)
-    a = cv2.cvtColor(left, cv2.COLOR_BGR2GRAY).astype(np.float32).ravel()
-    b = cv2.cvtColor(right, cv2.COLOR_BGR2GRAY).astype(np.float32).ravel()
-    a -= a.mean()
-    b -= b.mean()
-    denom = float(np.linalg.norm(a) * np.linalg.norm(b))
-    return float((a * b).sum() / denom) if denom else 0.0
-
-
-def leaked(image: Any, box: Sequence[int], stock_dir: str) -> Optional[str]:
-    """Name of the catalog template this crop is, or None when it is held out.
-
-    Byte-identical crops and near-identical pastes both count: enrolment crops
-    scored as test data are training-set accuracy, not recognition.
-
-    Pixel equality is not a leak test. A template re-cut from the same photo
-    with a slightly different box is the same leak and passes a same-shape
-    comparison, so grayscale signatures are compared as well. That check is a
-    heuristic with overlapping distributions (see LEAK_SIGNATURE_MAD): it
-    catches most re-cuts and no genuine facing in the measured set, but a
-    determined re-cut can still pass. The enrollment protocol's session manifest
-    is the real refusal; this is the backstop for catalogs that lack one.
-    """
-    crop = crop_of(image, box)
-    if crop.size == 0:
-        return None
-    crop_signatures = _signatures(crop)
-    for stem, name, shape, signatures in _catalog_templates(stock_dir):
-        close = min(float(np.abs(signature - crop_signature).mean())
-                    for signature in signatures for crop_signature in crop_signatures)
-        if close < LEAK_SIGNATURE_MAD:
-            # The signature is a 64x64 grayscale thumbnail, so it is coarse
-            # enough to fire on two different photos of the same product: the
-            # l09 rear label from a retailer page sits 19.5 from the enrolled
-            # front packshot, under the 20.0 line, while being a different
-            # photograph (resized MAD 26.3). Confirm before calling it a leak.
-            # A genuine re-cut or re-encode correlates at 0.99+; that pair
-            # correlates at 0.28, so the two signals together are decisive
-            # where either alone is not.
-            template = cv2.imread(os.path.join(stock_dir, name))
-            if template is None or _correlation(crop, template) >= LEAK_CORRELATION:
-                return stem
-            continue
-        if shape != crop.shape[:2]:
-            continue
-        template = cv2.imread(os.path.join(stock_dir, name))
-        if template is None:
-            continue
-        if np.array_equal(template, crop):
-            return stem
-        left = crop.astype(np.float32).ravel()
-        right = template.astype(np.float32).ravel()
-        denom = float(np.linalg.norm(left) * np.linalg.norm(right))
-        if denom and float((left * right).sum() / denom) >= 0.98:
-            return stem
-    return None
-
-
-def catalog_sku_ids(stock_dir: str) -> List[str]:
-    """SKU ids from the store's catalog CSV, falling back to enrolled images.
-
-    Candidate sets must come from the catalog, never from the labels: a lane
-    told which SKUs the shelf holds is being scored against the answer key, and
-    a retrieval failure can never show up.
-    """
-    path = os.path.join(stock_dir, "stock.csv")
-    if os.path.isfile(path):
-        with open(path, newline="") as handle:
-            ids = [row["SKU_ID"].strip() for row in csv.DictReader(handle)
-                   if row.get("SKU_ID", "").strip()]
-        if ids:
-            return sorted(set(ids))
-    return sorted({sku_of(name) for name in os.listdir(stock_dir)
-                   if os.path.splitext(name)[1].lower() in (".jpg", ".jpeg", ".png")})
-
-
-def group_by_sku(scores: Dict[str, float]) -> Dict[str, float]:
-    """Collapse per-view template names onto their SKU.
-
-    The matcher scores `sku_l01_1` and `sku_l01_2` separately; a SKU's score is
-    its best view, which is also how the gallery ranks several views. `sku_of`
-    is the canonical collapse, so a view outside the `sku_l` prefix groups with
-    its SKU instead of ranking as a product of its own.
-    """
-    grouped: Dict[str, float] = {}
-    for name, value in (scores or {}).items():
-        sku = sku_of(name)
-        grouped[sku] = max(grouped.get(sku, 0.0), float(value))
-    return grouped
-
-
-class Lane:
-    """One identity architecture, scored identically to every other."""
-
-    name = "lane"
-
-    def rank(self, crop: Any) -> List[Tuple[str, float]]:
-        """Catalog candidates, best first. Empty means nothing to answer."""
-        raise NotImplementedError
-
-    def shortlist(self, crop: Any, size: int) -> List[str]:
-        """Candidate SKUs the verifier is allowed to consider."""
-        return [sku for sku, _ in self.rank(crop)[:size]]
-
-    def decide(self, crop: Any, ranked: List[Tuple[str, float]], *, accept: float,
-               margin: float, scale: str) -> Dict[str, Any]:
-        """The lane's own decision. Default: the shared gate over its ranking.
-
-        A lane whose decision is not `decide_identity` over one ranking - the
-        fused local+text lane is one - overrides this, so every lane is still
-        scored through the same four ceilings.
-        """
-        from identity import decide_identity
-
-        return decide_identity(dict(ranked), method=self.name, scale=scale,
-                               min_score=accept, min_margin=margin)
-
-
-class MatcherLane(Lane):
-    """The original demo: local features and RANSAC over the whole catalog."""
-
-    name = "matcher"
-
-    def __init__(self, stock_dir: str, method: str = "hybrid") -> None:
-        from feature_matcher import ProductFeatureMatcher
-
-        self.matcher = ProductFeatureMatcher(stock_dir=stock_dir)
-        self.method = method
-
-    def rank(self, crop: Any) -> List[Tuple[str, float]]:
-        scores, _ = self.matcher.score_crop(crop, method=self.method)
-        return sorted(group_by_sku(scores).items(), key=lambda kv: kv[1], reverse=True)
-
-
-class VanLane(MatcherLane):
-    """Van's contract: the matcher's top SKU, always answered.
-
-    The `matcher` lane applies `decide_identity`, which is not what the original
-    demo did - it answered every crop with its best-scoring SKU and had no
-    accept line at all. Comparing the new system against the gated matcher
-    understates the baseline's coverage and overstates its precision, so the
-    completion gate's "Van" row is this lane: same scores, no gate.
-    """
-
-    name = "van"
-
-    def decide(self, crop: Any, ranked: List[Tuple[str, float]], *, accept: float,
-               margin: float, scale: str) -> Dict[str, Any]:
-        from identity import IDENTITY_ACCEPTED
-
-        if not ranked:
-            return {
-                "status": IDENTITY_ACCEPTED, "method": self.name, "best_sku": None,
-                "score": 0.0, "confidence": 0.0, "identity_level": IDENTITY_ACCEPTED,
-                "ranked_candidates": [], "details": {}, "message": "no candidate",
-                "source": None,
-            }
-        best, score = ranked[0]
-        return {
-            "status": IDENTITY_ACCEPTED, "method": self.name, "best_sku": best,
-            "score": round(float(score), 2), "confidence": round(float(score), 4),
-            "identity_level": IDENTITY_ACCEPTED,
-            "ranked_candidates": [{"sku_id": sku, "score": round(float(v), 2)}
-                                  for sku, v in ranked[:5]],
-            "details": {}, "message": "Always-argmax baseline", "source": "van",
-        }
-
-
-class LocalLaneAdapter(Lane):
-    """The shipped default: RootSIFT pyramid plus the OCR text lane.
-
-    Wraps `identity_lanes` so the production decision is what gets scored,
-    rather than a re-implementation of it. `rank` returns the lane's own order
-    (its rank key, not its gate value), and `decide` runs the same
-    `DecisionPolicy` the server runs.
-    """
-
-    name = "local"
-
-    def __init__(self, stock_dir: str, ocr: Any = None, use_text: bool = True) -> None:
-        from identity_lanes import LocalLane, TextLane
-
-        self.lane = LocalLane(stock_dir)
-        # E-3 asks what the text lane is worth, which is only answerable by
-        # running the same crops without it. `--no-text` therefore drops the
-        # lane whole: nothing in the decision reads it, and an ablation that
-        # still had to build an OCR engine could not be run on a machine
-        # without one.
-        self.use_text = use_text
-        self.text_lane = (
-            TextLane.from_catalog(
-                _catalog_rows(stock_dir), stock_dir, ocr if ocr is not None else _ocr_engine()
-            )
-            if use_text else None
-        )
-
-    def rank(self, crop: Any) -> List[Tuple[str, float]]:
-        return self.lane.rank(crop)
-
-    def decide(self, crop: Any, ranked: List[Tuple[str, float]], *, accept: float,
-               margin: float, scale: str) -> Dict[str, Any]:
-        from identity_lanes import DecisionPolicy
-
-        if not self.use_text:
-            # E-3's other arm: the same policy, over the same ranking, with no
-            # text evidence. `dict(ranked)` would not do - it drops the lane's
-            # order, and the value is not the order (see `LocalLane.rank`).
-            return DecisionPolicy(local_accept=accept).decide(ranked)
-        read = self.text_lane.read(crop)
-        text_rank = sorted(
-            self.text_lane.scores_from_text(read).items(),
-            key=lambda kv: kv[1], reverse=True,
-        )
-        return DecisionPolicy(
-            local_accept=accept, signatures=self.text_lane.signatures
-        ).decide(ranked, text_rank, text_read=read)
-
-
-def _catalog_rows(stock_dir: str) -> List[Dict[str, Any]]:
-    """The catalog's rows, in the shape TextLane.from_catalog expects."""
-    from inventory import InventoryManager
-
-    return InventoryManager(stock_dir).products.values()
-
-
-def _ocr_engine() -> Any:
-    from ocr import build_ocr_engine
-
-    return build_ocr_engine()
-
-
-class GalleryLane(Lane):
-    """DINOv2 embedding, with the store's trained head when it has one."""
-
-    name = "gallery"
-
-    def __init__(self, stock_dir: str, sku_ids: Sequence[str], image_size: int = 518) -> None:
-        from gallery import ProductGallery
-
-        self.gallery = ProductGallery(stock_dir, list(sku_ids), image_size=image_size)
-        if not self.gallery.load():
-            raise SystemExit(f"gallery unavailable for {stock_dir}")
-
-    def rank(self, crop: Any) -> List[Tuple[str, float]]:
-        # Ungated: the lane's own accept floor is applied once, by score_lane.
-        # Gating here too would reject before the floor is even consulted.
-        return self.gallery.rank(crop)
-
-
-class HybridLane(Lane):
-    """DINO shortlist, then the original matcher re-ranks inside it."""
-
-    name = "hybrid-verify"
-
-    def __init__(self, shortlist_lane: Lane, verifier: MatcherLane, size: int = 5) -> None:
-        self.shortlist_lane = shortlist_lane
-        self.verifier = verifier
-        self.size = int(size)
-        self.recall_hits = 0
-        self.recall_total = 0
-
-    def rank(self, crop: Any) -> List[Tuple[str, float]]:
-        allowed = self.shortlist_lane.shortlist(crop, self.size)
-        if not allowed:
-            return []
-        scores, _ = self.verifier.matcher.score_crop(crop, method=self.verifier.method)
-        grouped = group_by_sku(scores)
-        ranked = [(sku, float(grouped.get(sku, 0.0))) for sku in allowed]
-        return sorted(ranked, key=lambda kv: kv[1], reverse=True)
-
-
-def score_lane(
-    lane: Lane,
-    facings: Sequence[Dict[str, Any]],
-    image: Any,
-    *,
-    accept: float,
-    margin: float,
-    scale: str,
-    enrolled_skus: Collection[str],
-) -> Dict[str, Any]:
-    """One lane over one shelf. Accepts only unique, above-floor answers.
-
-    `top1` and `coverage` were both accepted-correct over enrolled facings, so
-    ranking and deciding were never separated. They are now four quantities:
-
-      catalog_ceiling      in-catalog facings with an enrolled view / all in-catalog
-      identity_ceiling     ungated top-1 rank correct on enrolled / enrolled
-      decision_ceiling     accepted-and-correct on enrolled / enrolled
-      end_to_end_coverage  accepted-and-correct / all in-catalog facings
-
-    Precision divides by every accept, unenrolled in-catalog facings included:
-    the SKU is on the shelf but has no reference, so an accept there is wrong
-    and is the failure the enrollment protocol exists to prevent.
-    """
-    in_catalog = [f for f in facings if f["label"] != UNKNOWN]
-    out_catalog = [f for f in facings if f["label"] == UNKNOWN]
-    enrolled_count = sum(1 for f in in_catalog if f["label"] in enrolled_skus)
-    unenrolled_count = len(in_catalog) - enrolled_count
-
-    correct = wrong = rejected = top1_correct = 0
-    # The decision ceiling is over enrolled facings only. The text lane can
-    # answer an unenrolled sibling correctly, and counting that in a ratio
-    # whose denominator is the enrolled count reports a ceiling above 100%.
-    enrolled_correct = 0
-    unknown_false = unenrolled_false = 0
-    accepts: Counter = Counter()
-    per_sku: Dict[str, List[int]] = {}
-    started = time.perf_counter()
-
-    for facing in facings:
-        crop = crop_of(image, facing["box"])
-        if crop.size == 0:
-            continue
-        ranked = lane.rank(crop)
-        decision = lane.decide(crop, ranked, accept=accept, margin=margin, scale=scale)
-        # decide_identity reports an accept as status "success"; identity_level
-        # is the stable field across both the inlier and cosine scales.
-        answered = decision.get("best_sku") if decision.get("identity_level") == "accepted" else None
-        truth = facing["label"]
-        if truth != UNKNOWN:
-            bucket = per_sku.setdefault(truth, [0, 0])
-            bucket[1] += 1
-            if truth in enrolled_skus and ranked and ranked[0][0] == truth:
-                top1_correct += 1
-            if answered == truth:
-                correct += 1
-                bucket[0] += 1
-                if truth in enrolled_skus:
-                    enrolled_correct += 1
-            elif answered:
-                wrong += 1
-                if truth not in enrolled_skus:
-                    unenrolled_false += 1
-        elif answered:
-            wrong += 1
-            unknown_false += 1
-        if answered:
-            accepts[answered] += 1
-        else:
-            rejected += 1
-
-    elapsed = time.perf_counter() - started
-    total_accepts = sum(accepts.values())
-    hub = max(accepts.values()) / total_accepts if total_accepts else 0.0
-    return {
-        "lane": lane.name,
-        "accept": accept,
-        "margin": margin,
-        "in_catalog": len(in_catalog),
-        "out_catalog": len(out_catalog),
-        "enrolled": enrolled_count,
-        "correct": correct,
-        "wrong": wrong,
-        "rejected": rejected,
-        "catalog_ceiling": round(enrolled_count / len(in_catalog), 4) if in_catalog else None,
-        "identity_ceiling": round(top1_correct / enrolled_count, 4) if enrolled_count else None,
-        "decision_ceiling": round(enrolled_correct / enrolled_count, 4) if enrolled_count else None,
-        "end_to_end_coverage": round(correct / len(in_catalog), 4) if in_catalog else None,
-        "false_accepts": wrong,
-        "unknown_false_accepts": unknown_false,
-        "unenrolled_false_accepts": unenrolled_false,
-        "false_accept_rate": round(unknown_false / len(out_catalog), 4) if out_catalog else None,
-        "near_unknown_false_accept_rate": round(unenrolled_false / unenrolled_count, 4) if unenrolled_count else None,
-        "precision": round(correct / total_accepts, 4) if total_accepts else None,
-        "max_sku_share": round(hub, 4),
-        "hub_sku": accepts.most_common(1)[0][0] if accepts else None,
-        "hub_alarm": hub > HUB_SHARE,
-        "histogram": dict(accepts.most_common()),
-        "per_sku": {sku: {"correct": hit, "total": seen} for sku, (hit, seen) in sorted(per_sku.items())},
-        "ms_per_crop": round(elapsed * 1000 / max(1, len(facings)), 1),
-    }
-
-
-def shortlist_recall(
-    lane: HybridLane,
-    facings: Sequence[Dict[str, Any]],
-    image: Any,
-    enrolled_skus: Collection[str],
-) -> Dict[str, Any]:
-    """Share of enrolled facings whose true SKU survives the shortlist.
-
-    This is the hybrid's hard ceiling: a verifier cannot pick a SKU the
-    shortlist already dropped. The denominator is enrolled facings only — a
-    facing whose SKU has no reference cannot be retrieved by any shortlist, so
-    counting it here reports a retrieval failure that is really a catalog gap.
-    Measured on the corrected labels, that distinction is 67% vs 98% at top-5.
-    """
-    hits = total = 0
-    misses: List[Dict[str, Any]] = []
-    for facing in facings:
-        if facing["label"] == UNKNOWN or facing["label"] not in enrolled_skus:
-            continue
-        crop = crop_of(image, facing["box"])
-        if crop.size == 0:
-            continue
-        total += 1
-        allowed = lane.shortlist_lane.shortlist(crop, lane.size)
-        if facing["label"] in allowed:
-            hits += 1
-        else:
-            misses.append({"index": facing.get("index"), "truth": facing["label"], "shortlist": allowed})
-    return {
-        "size": lane.size,
-        "hits": hits,
-        "total": total,
-        "recall": round(hits / total, 4) if total else None,
-        "misses": misses,
-    }
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
-    parser.add_argument("--labels", required=True, help="labelled facings JSON")
-    parser.add_argument("--image", help="shelf photo; defaults to the labels' own image")
-    parser.add_argument("--stock-dir", default="stock-lopia")
-    parser.add_argument("--lanes", default="local,matcher,gallery,hybrid-verify")
-    parser.add_argument("--shortlist", type=int, default=5)
-    parser.add_argument("--accept", type=float, default=None, help="override the lane's own floor")
-    parser.add_argument("--no-text", action="store_true",
-                        help="run the local lane without the text lane (E-3)")
-    parser.add_argument("--margin", type=float, default=None)
-    parser.add_argument("--live", action="store_true", help="also score at live resolution")
-    parser.add_argument("--json", help="write the report here")
-    args = parser.parse_args()
-
-    payload = load_labels(args.labels)
-    image_path = args.image or payload["image"]
-    image = cv2.imread(image_path)
-    if image is None:
-        raise SystemExit(f"cannot read {image_path}")
-
-    facings = payload["facings"]
-    held: List[Dict[str, Any]] = []
-    refused: List[Dict[str, Any]] = []
-    for facing in facings:
-        if facing["label"] == EXCLUDE:
-            continue
-        template = leaked(image, facing["box"], args.stock_dir)
-        if template:
-            refused.append({**facing, "template": template})
-        else:
-            held.append(facing)
-
-    wanted = [name.strip() for name in args.lanes.split(",") if name.strip()]
-    # Candidate sets come from the catalog, never from the labels: a lane told
-    # which SKUs the shelf holds is being scored against the answer key.
-    catalog_skus = catalog_sku_ids(args.stock_dir)
-    # A SKU with no enrolled view cannot be answered by any lane. Counting those
-    # facings as lane failures hides the real ceiling: catalog coverage.
-    enrolled_skus = {sku_of(name) for name in os.listdir(args.stock_dir)
-                     if os.path.splitext(name)[1].lower() in (".jpg", ".jpeg", ".png")}
-    unenrolled = Counter(f["label"] for f in held if f["label"] != UNKNOWN and f["label"] not in enrolled_skus)
-
-    lanes: Dict[str, Lane] = {}
-    if "matcher" in wanted or "hybrid-verify" in wanted or "van" in wanted:
-        lanes["matcher"] = MatcherLane(args.stock_dir)
-    if "van" in wanted:
-        lanes["van"] = VanLane(args.stock_dir)
-    if "local" in wanted:
-        lanes["local"] = LocalLaneAdapter(args.stock_dir, use_text=not args.no_text)
-    if "gallery" in wanted or "hybrid-verify" in wanted:
-        lanes["gallery"] = GalleryLane(args.stock_dir, catalog_skus)
-    if "hybrid-verify" in wanted:
-        lanes["hybrid-verify"] = HybridLane(lanes["gallery"], lanes["matcher"], size=args.shortlist)
-    # Boxes are in the original image's coordinates. Scoring a downscaled copy
-    # needs the boxes scaled with it, or every crop is taken from the wrong place.
-    resolutions = [("full", image, 1.0)]
-    if args.live:
-        live = at_live_resolution(image)
-        resolutions.append(("live", live, live.shape[1] / float(image.shape[1])))
-
-    report: Dict[str, Any] = {
-        "image": image_path,
-        "stock_dir": args.stock_dir,
-        "labels": args.labels,
-        # The enrollment protocol's versioning rule: every report records the
-        # catalog hash, the label-file hash and the code revision, so a number
-        # can be traced to the exact inputs that produced it.
-        "provenance": {
-            "catalog_hash": catalog_hash(args.stock_dir),
-            "labels_hash": file_hash(args.labels),
-            "image_hash": file_hash(image_path),
-            "code_revision": code_revision(),
-        },
-        "facings": len(facings),
-        "leakage": {"excluded": len(refused), "labels": refused},
-        "hub_share_limit": HUB_SHARE,
-        "enrollment": {
-            "catalog_skus": catalog_skus,
-            "enrolled_skus": sorted(enrolled_skus),
-            "unenrolled_skus": dict(unenrolled.most_common()),
-            "facings_without_enrollment": sum(unenrolled.values()),
-            "scored_facings": len(held),
-        },
-        "results": {},
-    }
-
-    for resolution, picture, factor in resolutions:
-        scaled = held if factor == 1.0 else [
-            {**f, "box": [int(round(v * factor)) for v in f["box"]]} for f in held
-        ]
-        rows = []
-        for name in wanted:
-            lane = lanes[name]
-            scale = {"gallery": "cosine", "local": "local"}.get(name, "inlier")
-            default_accept = {"cosine": 0.60, "local": LOCAL_ACCEPT}.get(scale, 1.0)
-            default_margin = 0.04 if scale == "cosine" else 0.15
-            accept = args.accept if args.accept is not None else default_accept
-            margin = args.margin if args.margin is not None else default_margin
-            row = score_lane(lane, scaled, picture, accept=accept, margin=margin, scale=scale,
-                             enrolled_skus=enrolled_skus)
-            if isinstance(lane, HybridLane):
-                row["shortlist_recall"] = shortlist_recall(lane, scaled, picture, enrolled_skus)
-            rows.append(row)
-        report["results"][resolution] = rows
-
-    if args.json:
-        with open(args.json, "w") as handle:
-            json.dump(report, handle, indent=1)
-
-    enrollment = report["enrollment"]
-    if enrollment["unenrolled_skus"]:
-        print(f"\nenrollment ceiling: {enrollment['facings_without_enrollment']} facing(s) belong to "
-              f"{len(enrollment['unenrolled_skus'])} SKU(s) with no enrolled view — "
-              f"unanswerable by any lane: {enrollment['unenrolled_skus']}")
-    for resolution, rows in report["results"].items():
-        print(f"\n{resolution} resolution — {enrollment['scored_facings']} scored facings "
-              f"({sum(1 for f in held if f['label'] != UNKNOWN)} in-catalog, "
-              f"{sum(1 for f in held if f['label'] == UNKNOWN)} unknown)")
-        print(f"  {'lane':<14} {'catalog':>8} {'identity':>9} {'decision':>9} {'end-to-end':>11} "
-              f"{'precision':>10} {'false acc':>10} {'max SKU':>8} {'ms/crop':>8}")
-        for row in rows:
-            cells = [f"{row[key]:.0%}" if row[key] is not None else "-" for key in
-                     ("catalog_ceiling", "identity_ceiling", "decision_ceiling",
-                      "end_to_end_coverage", "precision")]
-            flag = " HUB!" if row["hub_alarm"] else ""
-            print(f"  {row['lane']:<14} {cells[0]:>8} {cells[1]:>9} {cells[2]:>9} {cells[3]:>11} "
-                  f"{cells[4]:>10} {row['false_accepts']:>10} {row['max_sku_share']:>7.0%} "
-                  f"{row['ms_per_crop']:>8.1f}{flag}")
-            if "shortlist_recall" in row:
-                recall = row["shortlist_recall"]
-                share = f"{recall['recall']:.0%}" if recall["recall"] is not None else "-"
-                print(f"    shortlist top-{recall['size']} recall: {share} "
-                      f"({recall['hits']}/{recall['total']}) — hard ceiling for this lane")
-    if refused:
-        print(f"\nleakage: {len(refused)} facing(s) refused as catalog templates")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/recognization/code/eval_live.py b/recognization/code/eval_live.py
deleted file mode 100644
index 55b3671..0000000
--- a/recognization/code/eval_live.py
+++ /dev/null
@@ -1,861 +0,0 @@
-"""Replay a recorded walk through LiveSession and count what it actually decided.
-
-The recording format is FrameRecorder's: DIR/<session>/index.jsonl plus the JPEG
-bytes the client sent. Optional labels live in labels.json next to that index
-(or at --labels). A label is {"frame", "box", "sku_id"}.
-
-Template leakage is refused, not scored. A label whose crop matches a catalog
-template (byte-identical, or IoU >= 0.98 against a same-sized template pasted
-into the frame) is excluded and counted under leakage. Those crops are how the
-store was enrolled; scoring them is training-set accuracy.
-
-    python code/eval_live.py RECORDING_DIR [--stock-dir DIR] [--labels PATH]
-                              [--json PATH] [--iou 0.5]
-                              [--replay] [--identity matcher|local|gallery|van] [--device cpu|mps|cuda]
-
-Prints a human count table and writes the same report as JSON (default
-<recording>/eval_live.json). Missing input exits 2 with {"error": ...} on
-stdout. A recording that names identity_backend is scored as that backend unless
---identity repeats it. A recording that does not name one requires --identity;
-omitting it does not score the matcher. --identity gallery uses the same
-LiveSession path as the server and exits 2 when that gallery cannot load; it
-does not score the matcher instead.
-The report names the backend, whether the head loaded, and the weight pins.
-The same pinned recording, catalog, weights, and labels produce the
-same counts; timings are omitted because they are not.
-
-No held-out labels means denominators and fingerprints only. That is not a
-walk accuracy number.
-"""
-
-from __future__ import annotations
-
-import argparse
-import hashlib
-import json
-import os
-import sys
-from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple
-
-current_dir = os.path.dirname(os.path.abspath(__file__))
-if current_dir not in sys.path:
-    sys.path.insert(0, current_dir)
-
-LEVELS = ("accepted", "ambiguous", "rejected", "unknown", "unsettled")
-TAG_STATES = ("readable", "unreadable", "absent", "detected")
-
-
-class EvalInputError(ValueError):
-    """The recording, labels, or catalog cannot be scored as given."""
-
-
-def file_sha256(path: str) -> Optional[str]:
-    digest = hashlib.sha256()
-    try:
-        with open(path, "rb") as handle:
-            for chunk in iter(lambda: handle.read(1 << 20), b""):
-                digest.update(chunk)
-    except OSError:
-        return None
-    return digest.hexdigest()
-
-
-def content_sha256(payload: bytes) -> str:
-    return hashlib.sha256(payload).hexdigest()
-
-# Every backend the server can be started with, so a recording made by the
-# shipped `run-demo-server.sh` (--identity local) can be replayed. `local` is
-# the default; `matcher` is Van's scorer kept as the E-1 baseline lane.
-IDENTITY_BACKENDS = ("matcher", "local", "gallery", "van")
-
-
-def _iou(a: Sequence[float], b: Sequence[float]) -> float:
-    if len(a) != 4 or len(b) != 4:
-        return 0.0
-    ax1, ay1, ax2, ay2 = (float(v) for v in a)
-    bx1, by1, bx2, by2 = (float(v) for v in b)
-    ix1, iy1 = max(ax1, bx1), max(ay1, by1)
-    ix2, iy2 = min(ax2, bx2), min(ay2, by2)
-    inter = max(0.0, ix2 - ix1) * max(0.0, iy2 - iy1)
-    if inter <= 0.0:
-        return 0.0
-    area_a = max(0.0, ax2 - ax1) * max(0.0, ay2 - ay1)
-    area_b = max(0.0, bx2 - bx1) * max(0.0, by2 - by1)
-    union = area_a + area_b - inter
-    return inter / union if union > 0.0 else 0.0
-
-
-def load_index(recording_dir: str) -> List[Dict[str, Any]]:
-    path = os.path.join(recording_dir, "index.jsonl")
-    if not os.path.isfile(path):
-        raise EvalInputError(f"missing recording index: {path}")
-    rows: List[Dict[str, Any]] = []
-    with open(path, "r", encoding="utf-8") as handle:
-        for number, line in enumerate(handle, start=1):
-            line = line.strip()
-            if not line:
-                continue
-            try:
-                entry = json.loads(line)
-            except json.JSONDecodeError as exc:
-                raise EvalInputError(f"{path}:{number}: {exc.msg}") from exc
-            if not isinstance(entry, dict) or "frame" not in entry or "path" not in entry:
-                raise EvalInputError(f"{path}:{number}: line needs frame and path")
-            rows.append(entry)
-    if not rows:
-        raise EvalInputError(f"empty recording index: {path}")
-    return rows
-
-
-def recorded_identity(rows: Sequence[Dict[str, Any]]) -> Optional[str]:
-    """Backend named by the recording, when every present value agrees.
-
-    FrameRecorder writes identity_backend on each line when the server constructed
-    it with the active backend. Older indexes may omit it, or name it once under
-    identity.backend. Silence is not a backend. A mix is not a default.
-    """
-    found: List[str] = []
-    for row in rows:
-        raw = row.get("identity_backend")
-        if raw is None and isinstance(row.get("identity"), dict):
-            raw = row["identity"].get("backend")
-        if raw is None:
-            continue
-        name = str(raw).strip().lower()
-        if name not in IDENTITY_BACKENDS:
-            raise EvalInputError(
-                f"recorded identity backend is not one of {', '.join(IDENTITY_BACKENDS)}: {raw}"
-            )
-        found.append(name)
-    unique = set(found)
-    if len(unique) > 1:
-        raise EvalInputError("recording names more than one identity backend")
-    return found[0] if found else None
-
-
-def resolve_identity(requested: Optional[str], recorded: Optional[str]) -> str:
-    """CLI and recording must name the same backend. Silence is not matcher."""
-    chosen = (requested or recorded or "").strip().lower()
-    if not chosen:
-        raise EvalInputError(
-            "recording does not name an identity backend; pass --identity "
-            + " or --identity ".join(IDENTITY_BACKENDS)
-        )
-    if chosen not in IDENTITY_BACKENDS:
-        raise EvalInputError(
-            f"identity must be one of {', '.join(IDENTITY_BACKENDS)}, not {requested or recorded}"
-        )
-    if recorded and requested and recorded != chosen:
-        raise EvalInputError(
-            f"recording identity is {recorded}; pass --identity {recorded}"
-        )
-    return chosen
-
-
-def recording_fingerprint(recording_dir: str, rows: Sequence[Dict[str, Any]]) -> Dict[str, Any]:
-    """Hash the JPEGs the client sent, in index order. Missing files are an error."""
-    digest = hashlib.sha256()
-    missing: List[str] = []
-    for row in rows:
-        name = str(row["path"])
-        path = os.path.join(recording_dir, name)
-        payload_hash = file_sha256(path)
-        if payload_hash is None:
-            missing.append(name)
-            continue
-        digest.update(f"{row['frame']}:{name}:{payload_hash}\n".encode("utf-8"))
-    if missing:
-        raise EvalInputError("missing recorded frames: " + ", ".join(missing[:8]))
-    return {"sha256": digest.hexdigest(), "frames": len(rows)}
-
-
-def load_labels(path: Optional[str]) -> List[Dict[str, Any]]:
-    if not path:
-        return []
-    if not os.path.isfile(path):
-        raise EvalInputError(f"missing labels: {path}")
-    try:
-        with open(path, "r", encoding="utf-8") as handle:
-            payload = json.loads(handle.read())
-    except json.JSONDecodeError as exc:
-        raise EvalInputError(f"labels are not JSON: {exc.msg}") from exc
-    rows = payload.get("labels") if isinstance(payload, dict) else payload
-    if not isinstance(rows, list):
-        raise EvalInputError("labels must be a list or {\"labels\": [...]}")
-    cleaned: List[Dict[str, Any]] = []
-    for index, row in enumerate(rows):
-        if not isinstance(row, dict):
-            raise EvalInputError(f"labels[{index}] is not an object")
-        if "frame" not in row or "sku_id" not in row or "box" not in row:
-            raise EvalInputError(f"labels[{index}] needs frame, box, sku_id")
-        box = row["box"]
-        if not isinstance(box, (list, tuple)) or len(box) != 4:
-            raise EvalInputError(f"labels[{index}].box must be [x1, y1, x2, y2]")
-        cleaned.append(
-            {
-                "frame": int(row["frame"]),
-                "box": [int(v) for v in box],
-                "sku_id": str(row["sku_id"]),
-                "source": row.get("source"),
-            }
-        )
-    return cleaned
-
-
-def catalog_fingerprint(stock_dir: str, stock_csv: str = "stock.csv") -> Dict[str, Any]:
-    """Same inputs as RecognitionEngine.catalog_version, plus the full digest and head."""
-    digest = hashlib.sha256()
-    csv_path = os.path.join(stock_dir, stock_csv)
-    csv_hash = file_sha256(csv_path)
-    if csv_hash is None:
-        digest.update(b"missing-csv")
-    else:
-        digest.update(bytes.fromhex(csv_hash))
-    try:
-        entries = sorted(
-            (name, os.path.getsize(os.path.join(stock_dir, name)))
-            for name in os.listdir(stock_dir)
-            if name.lower().endswith((".jpeg", ".jpg", ".png"))
-        )
-    except OSError as exc:
-        raise EvalInputError(f"cannot read catalog {stock_dir}: {exc.strerror}") from exc
-    for name, size in entries:
-        digest.update(f"{name}:{size}".encode("utf-8"))
-    full = digest.hexdigest()
-    head_path = os.path.join(stock_dir, "identity_head.pt")
-    return {
-        "stock_dir": os.path.basename(os.path.abspath(stock_dir)),
-        "version": full[:12],
-        "sha256": full,
-        "templates": len(entries),
-        "csv_sha256": csv_hash,
-        "identity_head_sha256": file_sha256(head_path),
-    }
-
-
-def model_fingerprint(
-    weights_path: str,
-    *,
-    identity: str,
-    gallery: Any = None,
-) -> Dict[str, Any]:
-    """YOLO pin plus, for gallery, the backbone and head that actually loaded."""
-    try:
-        import ultralytics
-
-        library = f"ultralytics-{ultralytics.__version__}"
-    except Exception:
-        library = "ultralytics-unknown"
-    pin: Dict[str, Any] = {
-        "version": f"{os.path.basename(weights_path)}@{library}" if weights_path else f"none@{library}",
-        "weights_sha256": file_sha256(weights_path) if weights_path else None,
-        "identity": identity,
-    }
-    if identity != "gallery" or gallery is None:
-        if identity == "gallery":
-            pin["gallery_loaded"] = False
-            pin["gallery_head_loaded"] = False
-        return pin
-    from gallery import DINOV2_SHA256, dinov2_weights_path
-
-    backbone = dinov2_weights_path()
-    digest = file_sha256(backbone)
-    pin.update(
-        {
-            "gallery_loaded": True,
-            "gallery_head": getattr(gallery, "head_reason", None),
-            "gallery_head_loaded": getattr(gallery, "_head", None) is not None,
-            "gallery_views": gallery.view_count() if hasattr(gallery, "view_count") else None,
-            "dinov2_path": backbone,
-            "dinov2_sha256": digest,
-            "dinov2_sha256_expected": DINOV2_SHA256,
-            "dinov2_pinned": digest == DINOV2_SHA256,
-        }
-    )
-    return pin
-
-
-def _decode_image(payload: bytes) -> Any:
-    import cv2
-    import numpy as np
-
-    image = cv2.imdecode(np.frombuffer(payload, dtype=np.uint8), cv2.IMREAD_COLOR)
-    if image is None:
-        raise EvalInputError("recorded frame is not a decodable image")
-    return image
-
-
-def template_crops(stock_dir: str) -> List[Tuple[str, Any]]:
-    """Catalog photos keyed by SKU stem (sku_l05, sku_l05_1, ...)."""
-    import cv2
-
-    crops: List[Tuple[str, Any]] = []
-    try:
-        names = sorted(os.listdir(stock_dir))
-    except OSError:
-        return crops
-    for name in names:
-        stem, extension = os.path.splitext(name)
-        if extension.lower() not in (".jpeg", ".jpg", ".png"):
-            continue
-        image = cv2.imread(os.path.join(stock_dir, name))
-        if image is None or image.size == 0:
-            continue
-        crops.append((stem, image))
-    return crops
-
-
-def _same_picture(crop: Any, template: Any) -> bool:
-    """True when two same-sized images are the same photo after JPEG."""
-    import numpy as np
-
-    left = crop.astype("float32").reshape(-1)
-    right = template.astype("float32").reshape(-1)
-    if left.size < 16 or left.size != right.size:
-        return False
-    left = left - float(left.mean())
-    right = right - float(right.mean())
-    denom = float(np.sqrt((left * left).sum() * (right * right).sum()))
-    if denom <= 1e-6:
-        return bool(np.max(np.abs(crop.astype("int16") - template.astype("int16"))) <= 2)
-    return float((left * right).sum() / denom) >= 0.98
-
-
-def leaked_labels(
-    recording_dir: str,
-    rows: Sequence[Dict[str, Any]],
-    labels: Sequence[Dict[str, Any]],
-    stock_dir: str,
-) -> Tuple[List[Dict[str, Any]], List[Dict[str, Any]]]:
-    """Drop labels whose crop is a catalog template.
-
-    Byte identity catches a frame that is the template file. Geometric identity
-    catches a template pasted into a larger frame at the labelled box.
-    """
-    if not labels:
-        return [], []
-    templates = template_crops(stock_dir)
-    template_hashes = {}
-    try:
-        names = os.listdir(stock_dir)
-    except OSError:
-        names = []
-    for name in names:
-        digest = file_sha256(os.path.join(stock_dir, name))
-        if digest:
-            template_hashes.setdefault(digest, os.path.splitext(name)[0])
-    by_frame: Dict[int, List[Dict[str, Any]]] = {}
-    for label in labels:
-        by_frame.setdefault(int(label["frame"]), []).append(label)
-    held: List[Dict[str, Any]] = []
-    leaked: List[Dict[str, Any]] = []
-    frames = {int(row["frame"]): row for row in rows}
-    for frame_id, frame_labels in by_frame.items():
-        row = frames.get(frame_id)
-        image = None
-        payload_hash = None
-        if row is not None:
-            path = os.path.join(recording_dir, str(row["path"]))
-            try:
-                with open(path, "rb") as handle:
-                    payload = handle.read()
-            except OSError:
-                payload = b""
-            payload_hash = content_sha256(payload) if payload else None
-            if payload:
-                image = _decode_image(payload)
-        for label in frame_labels:
-            reason = None
-            matched = None
-            if payload_hash in template_hashes:
-                reason = "frame_is_template"
-                matched = template_hashes[payload_hash]
-            elif image is not None:
-                x1, y1, x2, y2 = label["box"]
-                crop = image[max(0, y1) : max(0, y2), max(0, x1) : max(0, x2)]
-                if crop.size:
-                    import cv2
-
-                    ch, cw = crop.shape[:2]
-                    for stem, template in templates:
-                        th, tw = template.shape[:2]
-                        if abs(th - ch) > 1 or abs(tw - cw) > 1:
-                            continue
-                        resized = template if (th, tw) == (ch, cw) else cv2.resize(template, (cw, ch))
-                        # JPEG re-encoding keeps layout and drops exact pixels.
-                        if _same_picture(crop, resized):
-                            reason = "crop_is_template"
-                            matched = stem
-                            break
-            if reason:
-                leaked.append({**label, "leak": reason, "template": matched})
-            else:
-                held.append(label)
-    return held, leaked
-
-
-def identity_level(track: Dict[str, Any]) -> str:
-    identity = track.get("identity") or {}
-    level = identity.get("identity_level")
-    if level in LEVELS:
-        return str(level)
-    state = identity.get("state")
-    if state == "recognized":
-        return "accepted"
-    if state == "needs_confirmation":
-        return "ambiguous"
-    if state == "unrecognized":
-        return "rejected"
-    return "unsettled"
-
-
-def tag_state(track: Dict[str, Any]) -> str:
-    tag = track.get("price_tag") or track.get("tag") or {}
-    state = tag.get("state")
-    if state in TAG_STATES:
-        return str(state)
-    if tag:
-        return "detected"
-    return "absent"
-
-
-def score_tracks(tracks: Iterable[Dict[str, Any]]) -> Dict[str, Any]:
-    """Denominators over settled decisions. Unsettled tracks are not in the rate."""
-    counts = {level: 0 for level in LEVELS}
-    tags = {state: 0 for state in TAG_STATES}
-    for track in tracks:
-        counts[identity_level(track)] += 1
-        tags[tag_state(track)] += 1
-    settled = counts["accepted"] + counts["ambiguous"] + counts["rejected"] + counts["unknown"]
-    return {
-        "tracks": sum(counts.values()),
-        "settled": settled,
-        "accepted": counts["accepted"],
-        "ambiguous": counts["ambiguous"],
-        "rejected": counts["rejected"],
-        "unknown": counts["unknown"],
-        "unsettled": counts["unsettled"],
-        "accepted_rate": _rate(counts["accepted"], settled),
-        "ambiguous_rate": _rate(counts["ambiguous"], settled),
-        "rejected_rate": _rate(counts["rejected"], settled),
-        "tags_detected": tags["detected"] + tags["readable"] + tags["unreadable"],
-        "tags_readable": tags["readable"],
-        "tags_unreadable": tags["unreadable"],
-        "tags_absent": tags["absent"],
-    }
-
-
-def _rate(numerator: int, denominator: int) -> Optional[float]:
-    if denominator <= 0:
-        return None
-    return round(numerator / denominator, 4)
-
-
-def track_churn(frames: Sequence[Dict[str, Any]]) -> Dict[str, Any]:
-    """How often one track id changes the SKU it is willing to name.
-
-    A change counts only after the id has already named a SKU. Restarting or
-    dropping the label (relabels/restarts) is reported beside that, from the
-    counters LiveSession already publishes.
-    """
-    previous: Dict[int, Optional[str]] = {}
-    changes: Dict[int, int] = {}
-    seen: Dict[int, int] = {}
-    relabels: Dict[int, int] = {}
-    restarts: Dict[int, int] = {}
-    for frame in frames:
-        for track in frame.get("tracks") or []:
-            track_id = track.get("track_id")
-            if track_id is None:
-                continue
-            track_id = int(track_id)
-            seen[track_id] = seen.get(track_id, 0) + 1
-            relabels[track_id] = max(relabels.get(track_id, 0), int(track.get("relabels") or 0))
-            restarts[track_id] = max(restarts.get(track_id, 0), int(track.get("restarts") or 0))
-            named = None
-            if identity_level(track) == "accepted":
-                named = (track.get("identity") or {}).get("sku_id")
-            prior = previous.get(track_id)
-            if prior and named and named != prior:
-                changes[track_id] = changes.get(track_id, 0) + 1
-            if named:
-                previous[track_id] = named
-    churned = sum(1 for count in changes.values() if count)
-    return {
-        "tracks": len(seen),
-        "tracks_with_sku_change": churned,
-        "sku_changes": sum(changes.values()),
-        "relabels": sum(relabels.values()),
-        "restarts": sum(restarts.values()),
-        "per_track": [
-            {
-                "track_id": track_id,
-                "observations": seen[track_id],
-                "sku_changes": changes.get(track_id, 0),
-                "relabels": relabels.get(track_id, 0),
-                "restarts": restarts.get(track_id, 0),
-            }
-            for track_id in sorted(seen)
-            if changes.get(track_id, 0) or relabels.get(track_id, 0) or restarts.get(track_id, 0)
-        ],
-    }
-
-
-def labelled_top1(
-    frames: Sequence[Dict[str, Any]],
-    labels: Sequence[Dict[str, Any]],
-    *,
-    iou_min: float,
-) -> Dict[str, Any]:
-    """Top-1 over held-out labels. Accepted wrong SKU is a miss, not a reject.
-
-    Denominator is labels that overlap a track. A label with no overlapping
-    track is unmatched and stays out of the top-1 rate.
-    """
-    by_frame: Dict[int, List[Dict[str, Any]]] = {}
-    for frame in frames:
-        by_frame.setdefault(int(frame.get("frame", frame.get("frame_id"))), []).append(frame)
-    correct = wrong = rejected = ambiguous = missed = 0
-    matched = 0
-    for label in labels:
-        observations = by_frame.get(int(label["frame"]), [])
-        best_iou = 0.0
-        best: Optional[Dict[str, Any]] = None
-        for observation in observations:
-            for track in observation.get("tracks") or []:
-                score = _iou(label["box"], track.get("box") or [])
-                if score > best_iou:
-                    best_iou = score
-                    best = track
-        if best is None or best_iou < iou_min:
-            missed += 1
-            continue
-        matched += 1
-        level = identity_level(best)
-        guess = (best.get("identity") or {}).get("sku_id")
-        if level == "accepted" and guess == label["sku_id"]:
-            correct += 1
-        elif level == "accepted":
-            wrong += 1
-        elif level == "ambiguous":
-            ambiguous += 1
-        else:
-            rejected += 1
-    return {
-        "labels": len(labels),
-        "matched": matched,
-        "unmatched": missed,
-        "correct": correct,
-        "wrong": wrong,
-        "rejected": rejected,
-        "ambiguous": ambiguous,
-        "top1": _rate(correct, matched),
-        "iou_min": iou_min,
-    }
-
-
-def score_recording(
-    recording_dir: str,
-    *,
-    stock_dir: str,
-    weights_path: str = "",
-    labels_path: Optional[str] = None,
-    iou_min: float = 0.5,
-    live_frames: Optional[Sequence[Dict[str, Any]]] = None,
-    identity: str,
-    gallery: Any = None,
-) -> Dict[str, Any]:
-    """Score one session directory. live_frames, when given, are the replayed results.
-
-    Without live_frames the recorded index is what the server already decided.
-    That still produces denominators; it is not a fresh model run. identity is
-    the backend those frames were named with, not a claim that this process
-    loaded it. gallery, when passed, is the loaded ProductGallery.
-    """
-    recording_dir = os.path.abspath(recording_dir)
-    rows = load_index(recording_dir)
-    sample = recording_fingerprint(recording_dir, rows)
-    labels_file = labels_path or os.path.join(recording_dir, "labels.json")
-    labels = load_labels(labels_file if os.path.isfile(labels_file) or labels_path else None)
-    held, leaked = leaked_labels(recording_dir, rows, labels, stock_dir)
-    source = list(live_frames) if live_frames is not None else rows
-    tracks = [track for frame in source for track in (frame.get("tracks") or [])]
-    decisions = score_tracks(tracks)
-    backend = identity.strip().lower()
-    if backend not in IDENTITY_BACKENDS:
-        raise EvalInputError(
-            f"identity must be one of {', '.join(IDENTITY_BACKENDS)}, not {identity}"
-        )
-    report = {
-        "recording": recording_dir,
-        "source": "live_replay" if live_frames is not None else "recorded_index",
-        "identity": {
-            "backend": backend,
-            "gallery_loaded": gallery is not None if backend == "gallery" else None,
-            "gallery_head": getattr(gallery, "head_reason", None) if gallery is not None else None,
-            "gallery_head_loaded": (
-                bool(getattr(gallery, "_head", None) is not None) if gallery is not None else None
-            ),
-        },
-        "fingerprints": {
-            "sample": sample,
-            "model": model_fingerprint(weights_path, identity=backend, gallery=gallery),
-            "catalog": catalog_fingerprint(stock_dir),
-        },
-        "decisions": decisions,
-        "tags": {
-            "detected": decisions["tags_detected"],
-            "readable": decisions["tags_readable"],
-            "unreadable": decisions["tags_unreadable"],
-            "absent": decisions["tags_absent"],
-        },
-        "churn": track_churn(source),
-        "leakage": {"excluded": len(leaked), "labels": leaked},
-        "labelled_top1": labelled_top1(source, held, iou_min=iou_min),
-        "evidence": _evidence(labels, held, leaked, live_frames is not None),
-    }
-    return report
-
-
-def _evidence(
-    labels: Sequence[Dict[str, Any]],
-    held: Sequence[Dict[str, Any]],
-    leaked: Sequence[Dict[str, Any]],
-    replayed: bool,
-) -> List[str]:
-    gaps: List[str] = []
-    if not replayed:
-        gaps.append("counts are from the recorded index, not a fresh LiveSession replay")
-    if not labels:
-        gaps.append("no held-out labels; labelled top-1 is not a walk accuracy")
-    elif not held:
-        gaps.append("every label matched a catalog template and was excluded")
-    if leaked:
-        gaps.append(f"{len(leaked)} template-derived label(s) excluded")
-    return gaps
-
-
-def replay_live(
-    recording_dir: str,
-    engine: Any,
-    *,
-    settings: Optional[Any] = None,
-) -> List[Dict[str, Any]]:
-    """Feed recorded JPEGs through one LiveSession, in index order.
-
-    Classification is asynchronous. Each frame is scored only after its
-    in-flight crops finish, then processed again so the label is on the
-    payload. Without that wait the same recording does not replay twice.
-    """
-    from live import LiveSession
-
-    rows = load_index(recording_dir)
-    session = LiveSession("eval", engine, settings=settings)
-    session.refresh_catalog()
-    if hasattr(session, "enable_tags"):
-        session.enable_tags()
-    frames: List[Dict[str, Any]] = []
-    for row in rows:
-        path = os.path.join(recording_dir, str(row["path"]))
-        with open(path, "rb") as handle:
-            payload = handle.read()
-        frame_id = int(row["frame"])
-        result = session.process_frame(frame_id, payload)
-        # min_hits is 2, so the first pass only detects. Keep passing the same
-        # bytes until a pass schedules nothing and everything in flight lands.
-        for _ in range(4):
-            _wait_until_settled(session)
-            result = session.process_frame(frame_id, payload)
-            if int((session.snapshot().get("stats") or {}).get("classify_pending") or 0) == 0:
-                break
-        result["frame"] = frame_id
-        frames.append(result)
-    engine_shutdown = getattr(engine, "shutdown", None)
-    if callable(engine_shutdown):
-        engine_shutdown()
-    return frames
-
-
-def _wait_until_settled(session: Any, timeout: float = 30.0) -> None:
-    import time
-
-    deadline = time.monotonic() + timeout
-    while time.monotonic() < deadline:
-        pending = int((session.snapshot().get("stats") or {}).get("classify_pending") or 0)
-        if pending == 0:
-            return
-        time.sleep(0.02)
-    raise EvalInputError("classification did not finish before the replay timeout")
-
-def format_report(report: Dict[str, Any]) -> str:
-    decisions = report["decisions"]
-    tags = report["tags"]
-    top1 = report["labelled_top1"]
-    churn = report["churn"]
-    fingerprints = report["fingerprints"]
-    identity = report.get("identity") or {}
-    model = fingerprints["model"]
-    head = identity.get("gallery_head")
-    head_note = f" head {head}" if head else ""
-    lines = [
-        f"source: {report['source']}",
-        f"identity: {identity.get('backend')}{head_note}",
-        (
-            f"decisions: accepted {decisions['accepted']}  ambiguous {decisions['ambiguous']}  "
-            f"rejected {decisions['rejected']}  unknown {decisions['unknown']}  "
-            f"unsettled {decisions['unsettled']}  settled {decisions['settled']}"
-        ),
-        (
-            f"rates: accepted {decisions['accepted_rate']}  "
-            f"ambiguous {decisions['ambiguous_rate']}  rejected {decisions['rejected_rate']}  "
-            f"(denominator settled)"
-        ),
-        (
-            f"tags: detected {tags['detected']}  readable {tags['readable']}  "
-            f"unreadable {tags['unreadable']}  absent {tags['absent']}"
-        ),
-        (
-            f"churn: tracks {churn['tracks']}  sku_changes {churn['sku_changes']}  "
-            f"relabels {churn['relabels']}  restarts {churn['restarts']}"
-        ),
-        (
-            f"labelled top-1: {top1['correct']}/{top1['matched']} = {top1['top1']}  "
-            f"wrong {top1['wrong']}  unmatched {top1['unmatched']}  "
-            f"leakage excluded {report['leakage']['excluded']}"
-        ),
-        (
-            f"fingerprints: sample {fingerprints['sample']['sha256'][:12]}  "
-            f"model {model['version']}  "
-            f"weights {str(model.get('weights_sha256') or 'none')[:12]}  "
-            f"catalog {fingerprints['catalog']['version']}"
-        ),
-    ]
-    if model.get("identity") == "gallery":
-        lines.append(
-            f"gallery: loaded {model.get('gallery_loaded')}  "
-            f"head {model.get('gallery_head')}  "
-            f"dinov2 {str(model.get('dinov2_sha256') or 'missing')[:12]}  "
-            f"pinned {model.get('dinov2_pinned')}"
-        )
-    for gap in report["evidence"]:
-        lines.append(f"gap: {gap}")
-    return "\n".join(lines)
-
-
-def replay_engine(stock_dir: str, weights: str, identity: str, device: Optional[str]) -> Tuple[Any, str, Any]:
-    """Build the engine the server would, and refuse a gallery that did not load.
-
-    The backbone file is checked before YOLO loads. A missing or unpinned file
-    is the same refusal the server makes, without scoring the matcher.
-    """
-    from engine import EngineSettings, build_default_engine
-
-    if identity == "gallery":
-        from gallery import DINOV2_SHA256, dinov2_weights_path
-
-        backbone = dinov2_weights_path()
-        digest = file_sha256(backbone)
-        if digest is None:
-            raise EvalInputError(
-                f"gallery identity backend requires local DINOv2 weights at {backbone}; "
-                "refusing to score the matcher"
-            )
-        if digest != DINOV2_SHA256:
-            raise EvalInputError(
-                f"DINOv2 weights at {backbone} are sha256 {digest}, expected {DINOV2_SHA256}"
-            )
-    settings = EngineSettings(stock_dir=stock_dir, identity_backend=identity)
-    if weights:
-        settings.weights_path = weights
-    if device:
-        settings.device = device
-    engine = build_default_engine(settings)
-    chosen = (engine.settings.identity_backend or "matcher").lower()
-    if chosen != identity:
-        engine.shutdown()
-        raise EvalInputError(f"engine identity is {chosen}, requested {identity}")
-    gallery = None
-    if identity == "gallery":
-        gallery = engine.identity_gallery()
-        if gallery is None:
-            engine.shutdown()
-            raise EvalInputError(
-                "gallery identity backend did not load; refusing to score the matcher"
-            )
-    return engine, engine.settings.weights_path, gallery
-
-
-def main(argv: Optional[Sequence[str]] = None) -> int:
-    parser = argparse.ArgumentParser(description="Score a recorded live walk.")
-    parser.add_argument("recording", nargs="?", help="session directory with index.jsonl")
-    parser.add_argument("--stock-dir", default="stock")
-    parser.add_argument("--weights", default="")
-    parser.add_argument("--labels", default=None)
-    parser.add_argument("--json", dest="json_path", default=None)
-    parser.add_argument("--iou", type=float, default=0.5)
-    parser.add_argument(
-        "--replay",
-        action="store_true",
-        help="re-run frames through LiveSession (needs the recognition stack)",
-    )
-    parser.add_argument("--method", default="hybrid")
-    parser.add_argument(
-        "--identity",
-        default=None,
-        choices=list(IDENTITY_BACKENDS),
-        help="which mechanism names products (required when the recording omits identity_backend)",
-    )
-    parser.add_argument("--device", default=None, help="cpu | mps | cuda | cuda:0")
-    args = parser.parse_args(argv)
-
-    def fail(message: str) -> int:
-        print(json.dumps({"error": message}, ensure_ascii=False))
-        print(f"error: {message}", file=sys.stderr)
-        return 2
-
-    if not args.recording:
-        return fail("recording directory is required")
-    recording = os.path.abspath(args.recording)
-    if not os.path.isdir(recording):
-        return fail(f"recording directory not found: {recording}")
-    try:
-        rows = load_index(recording)
-        identity = resolve_identity(args.identity, recorded_identity(rows))
-        live_frames = None
-        gallery = None
-        weights = args.weights
-        if args.replay:
-            from live import LiveSettings
-
-            engine, weights, gallery = replay_engine(
-                args.stock_dir, args.weights, identity, args.device
-            )
-            live_frames = replay_live(
-                recording, engine, settings=LiveSettings(method=args.method, tags=True)
-            )
-        report = score_recording(
-            recording,
-            stock_dir=args.stock_dir,
-            weights_path=weights,
-            labels_path=args.labels,
-            iou_min=args.iou,
-            live_frames=live_frames,
-            identity=identity,
-            gallery=gallery,
-        )
-    except EvalInputError as exc:
-        return fail(str(exc))
-    text = json.dumps(report, ensure_ascii=False, indent=1, sort_keys=True)
-    destination = args.json_path or os.path.join(recording, "eval_live.json")
-    try:
-        with open(destination, "w", encoding="utf-8") as handle:
-            handle.write(text + "\n")
-    except OSError as exc:
-        return fail(f"cannot write {destination}: {exc.strerror}")
-    print(format_report(report))
-    print(f"json: {destination}")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/recognization/code/fast_test.py b/recognization/code/fast_test.py
new file mode 100644
index 0000000..772ff4f
--- /dev/null
+++ b/recognization/code/fast_test.py
@@ -0,0 +1,6 @@
+from ultralytics import YOLO
+
+model = YOLO("/Users/nguyenhongvan/recongnize/runs/detect/train/weights/best.pt")
+image_path = "/Users/nguyenhongvan/recongnize/data/add/image7.jpg"
+results = model(image_path)
+results[0].show()
diff --git a/recognization/code/feature_matcher.py b/recognization/code/feature_matcher.py
index b0171d7..39f85c6 100644
--- a/recognization/code/feature_matcher.py
+++ b/recognization/code/feature_matcher.py
@@ -7,9 +7,7 @@ the methodology of Electronics 2023, 12(17), 3640.
 import os
 import cv2
 import numpy as np
-import threading
 from typing import Dict, List, Tuple, Optional, Any
-from identity import decide_identity
 
 
 class LocalFeatureExtractor:
@@ -124,18 +122,6 @@ class ProductFeatureMatcher:
     def __init__(self, stock_dir: str = "stock"):
         self.db = TemplateFeatureDatabase(stock_dir)
         self.extractors = self.db.extractors
-        self._thread_local = threading.local()
-
-    def thread_extractors(self) -> Dict[str, "LocalFeatureExtractor"]:
-        """One extractor set per thread: OpenCV Feature2D objects are not safe to
-        call concurrently from several threads."""
-        extractors = getattr(self._thread_local, "extractors", None)
-        if extractors is None:
-            extractors = {
-                name: LocalFeatureExtractor(name) for name in ("surf", "orb", "brisk")
-            }
-            self._thread_local.extractors = extractors
-        return extractors
 
     def match_descriptors(
         self,
@@ -187,29 +173,26 @@ class ProductFeatureMatcher:
         except Exception:
             return len(good_matches), good_matches, None
 
-    def score_crop(
+    def classify_crop(
         self,
         crop_bgr: np.ndarray,
         method: str = "hybrid"
-    ) -> Tuple[Dict[str, float], Dict[str, Dict[str, Any]]]:
+    ) -> Dict[str, Any]:
         """
-        Scores a product crop against every SKU template.
-        Returns (scores, details); raises ValueError for an unsupported method and
-        returns empty results for an empty crop.
+        Classifies a product crop against all SKU templates.
         Methods supported: 'surf', 'orb', 'brisk', 'hybrid'.
         """
         if crop_bgr is None or crop_bgr.size == 0:
-            return {}, {}
+            return {"status": "error", "message": "Empty crop image"}
 
         crop_gray = cv2.cvtColor(crop_bgr, cv2.COLOR_BGR2GRAY)
         method = method.lower()
-        extractors = self.thread_extractors()
 
         scores: Dict[str, float] = {}
         details: Dict[str, Dict[str, Any]] = {}
 
         if method in ["surf", "orb", "brisk"]:
-            extractor = extractors.get(method)
+            extractor = self.extractors.get(method)
             if extractor is None:
                 extractor = LocalFeatureExtractor(method)
             kp_query, des_query = extractor.detect_and_compute(crop_gray)
@@ -248,7 +231,7 @@ class ProductFeatureMatcher:
             # Pre-compute features for query crop across all 3 extractors
             query_features = {}
             for sm in sub_methods:
-                kp_q, des_q = extractors[sm].detect_and_compute(crop_gray)
+                kp_q, des_q = self.extractors[sm].detect_and_compute(crop_gray)
                 query_features[sm] = (kp_q, des_q)
 
             for sku_id, tmpl in self.db.templates.items():
@@ -259,7 +242,7 @@ class ProductFeatureMatcher:
                     kp_q, des_q = query_features[sm]
                     kp_t = tmpl["features"][sm]["keypoints"]
                     des_t = tmpl["features"][sm]["descriptors"]
-                    norm_t = extractors[sm].norm_type
+                    norm_t = self.extractors[sm].norm_type
 
                     matches = self.match_descriptors(des_q, des_t, norm_t, ratio_thresh=0.75)
                     inliers, inlier_list, _ = self.compute_geometric_inliers(kp_q, kp_t, matches)
@@ -283,103 +266,30 @@ class ProductFeatureMatcher:
                     "kp_query": query_features["surf"][0],
                     "kp_tmpl": tmpl["features"]["surf"]["keypoints"]
                 }
-        else:
-            raise ValueError(f"Unknown method: {method}")
-
-        return scores, details
-
-    def score_crop_fast(
-        self,
-        crop_bgr: np.ndarray,
-        shortlist: int = 5
-    ) -> Tuple[Dict[str, float], Dict[str, Dict[str, Any]]]:
-        """
-        ORB-shortlist then hybrid-score the top candidates only.
-
-        Same scoring formula as 'hybrid', but the expensive SURF/ORB/BRISK
-        descriptor matching runs on `shortlist` templates instead of all of them.
-        Every other template scores 0.0, so decide_identity() still ranks the
-        whole catalog. This is a live-mode speed path; validate it against
-        'hybrid' with bench_engine.py --compare before trusting it.
-        """
-        if crop_bgr is None or crop_bgr.size == 0:
-            return {}, {}
-        crop_gray = cv2.cvtColor(crop_bgr, cv2.COLOR_BGR2GRAY)
-        extractors = self.thread_extractors()
-
-        orb = extractors["orb"]
-        kp_orb, des_orb = orb.detect_and_compute(crop_gray)
-        orb_scores: Dict[str, float] = {}
-        for sku_id, tmpl in self.db.templates.items():
-            kp_t = tmpl["features"]["orb"]["keypoints"]
-            des_t = tmpl["features"]["orb"]["descriptors"]
-            matches = self.match_descriptors(des_orb, des_t, orb.norm_type, ratio_thresh=0.75)
-            inliers, _, _ = self.compute_geometric_inliers(kp_orb, kp_t, matches)
-            denom = max(1, min(len(kp_orb), len(kp_t)))
-            orb_scores[sku_id] = inliers * 1.5 + (inliers / denom) * 80.0
-
-        candidates = sorted(orb_scores.items(), key=lambda kv: kv[1], reverse=True)[:shortlist]
-
-        scores: Dict[str, float] = {sku_id: 0.0 for sku_id in self.db.templates}
-        details: Dict[str, Dict[str, Any]] = {}
-        sub_methods = ["surf", "orb", "brisk"]
-        weights = {"surf": 0.45, "orb": 0.25, "brisk": 0.30}
-        query_features = {}
-        for sm in sub_methods:
-            kp_q, des_q = extractors[sm].detect_and_compute(crop_gray)
-            query_features[sm] = (kp_q, des_q)
-
-        for sku_id, _ in candidates:
-            tmpl = self.db.templates[sku_id]
-            composite = 0.0
-            sku_sub_details = {}
-            for sm in sub_methods:
-                kp_q, des_q = query_features[sm]
-                kp_t = tmpl["features"][sm]["keypoints"]
-                des_t = tmpl["features"][sm]["descriptors"]
-                matches = self.match_descriptors(des_q, des_t, extractors[sm].norm_type, ratio_thresh=0.75)
-                inliers, _, _ = self.compute_geometric_inliers(kp_q, kp_t, matches)
-                denom = max(1, min(len(kp_q), len(kp_t)))
-                sub_score = inliers * 1.5 + (inliers / denom) * 80.0
-                composite += weights[sm] * sub_score
-                sku_sub_details[sm] = {
-                    "inliers": inliers,
-                    "matches": len(matches),
-                    "kp_q_count": len(kp_q),
-                    "kp_t_count": len(kp_t),
-                    "sub_score": round(sub_score, 2),
-                }
-            scores[sku_id] = composite
-            details[sku_id] = {
-                "score": composite,
-                "sub_details": sku_sub_details,
-                "kp_query": query_features["surf"][0],
-                "kp_tmpl": tmpl["features"]["surf"]["keypoints"],
-            }
-        return scores, details
-
-    def classify_crop(
-        self,
-        crop_bgr: np.ndarray,
-        method: str = "hybrid"
-    ) -> Dict[str, Any]:
-        """
-        Classifies a product crop against all SKU templates.
-        Methods supported: 'surf', 'orb', 'brisk', 'hybrid', 'hybrid_fast'.
-        """
-        if crop_bgr is None or crop_bgr.size == 0:
-            return {"status": "error", "message": "Empty crop image"}
-
-        method = method.lower()
-        if method == "hybrid_fast":
-            scores, details = self.score_crop_fast(crop_bgr)
-        elif method in ["surf", "orb", "brisk", "hybrid"]:
-            scores, details = self.score_crop(crop_bgr, method=method)
         else:
             return {"status": "error", "message": f"Unknown method: {method}"}
 
-        ranked = decide_identity(scores, details=details, method=method)
-        return ranked
+        # Rank templates by score
+        if not scores:
+            return {"status": "error", "message": "No templates available"}
+
+        sorted_candidates = sorted(scores.items(), key=lambda x: x[1], reverse=True)
+        top_sku, top_score = sorted_candidates[0]
+        runner_up_sku, runner_up_score = sorted_candidates[1] if len(sorted_candidates) > 1 else (None, 0.0)
+
+        confidence = round(min(1.0, top_score / max(1.0, (top_score + runner_up_score * 0.5))), 4)
+
+        return {
+            "status": "success",
+            "method": method,
+            "best_sku": top_sku,
+            "score": round(top_score, 2),
+            "confidence": confidence,
+            "ranked_candidates": [
+                {"sku_id": s, "score": round(sc, 2)} for s, sc in sorted_candidates[:5]
+            ],
+            "details": details[top_sku]
+        }
 
     def generate_match_visualization(
         self,
@@ -396,7 +306,7 @@ class ProductFeatureMatcher:
         crop_gray = cv2.cvtColor(crop_bgr, cv2.COLOR_BGR2GRAY)
         eval_method = method if method in ["surf", "orb", "brisk"] else "surf"
 
-        extractor = self.thread_extractors()[eval_method]
+        extractor = self.extractors[eval_method]
         kp_q, des_q = extractor.detect_and_compute(crop_gray)
         kp_t = tmpl["features"][eval_method]["keypoints"]
         des_t = tmpl["features"][eval_method]["descriptors"]
diff --git a/recognization/code/finetune_identity.py b/recognization/code/finetune_identity.py
deleted file mode 100644
index 376789a..0000000
--- a/recognization/code/finetune_identity.py
+++ /dev/null
@@ -1,413 +0,0 @@
-"""
-Fine-tune the identity embedding on the labels we can actually produce.
-
-Measured: off-the-shelf DINOv2 tops out at ~25 % on a real milk shelf where the
-products differ only by small text and a colour band, while it reaches 72-76 % on the
-demo shelf, whose facings are near-identical crops of one photograph. That gap is the
-whole problem, and the literature says the same thing (RP2K and RPC fine-tune;
-GroceryVision reports a 17.5-point drop from Recall@5 to Recall@1).
-
-This trains a small projection head on frozen DINOv2 features and compares, on the
-same held-out facings, three ways of naming a product:
-
-    raw cosine          the gallery as shipped
-    fine-tuned cosine   the same matching, on projected features
-    fine-tuned + margin the same, trained with an angular margin
-
-**What this can and cannot show.** The labels come from one shelf, and the held-out
-facings are other facings of the same photograph, so the number is an upper bound: it
-measures whether a trained head separates these products at all, not whether it
-survives a second visit. Augmentation (crop, scale, brightness, blur) stands in for
-the visit we do not have; a cross-visit number needs cross-visit data.
-
-    python code/finetune_identity.py [--epochs 60] [--views 2]
-"""
-
-from __future__ import annotations
-
-import argparse
-import os
-import sys
-
-import cv2
-import numpy as np
-
-sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
-
-# Labelled by eye from a contact sheet of the detections (docs/EXTERNAL-EVAL.md).
-MILK_SHELF_LABELS = {
-    "meiji_oishii":  [13, 14, 15, 16, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36],
-    "tokunou45":     [9, 10, 11, 12],
-    "megmilk":       [20, 21, 22, 23, 24, 25, 26, 37, 38, 46, 47, 54, 55, 56, 57, 58, 59,
-                      64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 90, 94, 95, 96, 97, 98, 99],
-    "hokkaido":      [49, 50, 51, 60, 61, 62, 63, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,
-                      100, 101, 102, 103, 104],
-    "teishibou":     [2, 3, 4],
-    "meiji_love":    [5, 6, 7, 8],
-    "oishii_tounyu": [42, 43, 44, 45],
-    "rakunou36":     [17, 18],
-}
-
-
-def enrollment_report(stock: str, csv_name: str = "stock.csv") -> dict:
-    """Catalog versus enrolled views. Does not load a model or write files.
-
-    A SKU with no readable view cannot be recognized. One view is reported, not
-    treated as enough: the measured knee is two views. Duplicate CSV rows and a
-    head whose class list disagrees with the catalog are blockers.
-    """
-    import csv
-    import re
-
-    from gallery import ProductGallery
-
-    names = []
-    try:
-        names = os.listdir(stock)
-    except OSError:
-        names = []
-    images = [name for name in names if os.path.splitext(name)[1].lower() in (".jpeg", ".jpg", ".png")]
-    rows = []
-    csv_path = os.path.join(stock, csv_name)
-    if os.path.exists(csv_path):
-        with open(csv_path, encoding="utf-8") as handle:
-            rows = list(csv.DictReader(handle))
-    sku_ids = []
-    seen = set()
-    duplicate_rows = []
-    for row in rows:
-        sku_id = (row.get("SKU_ID") or "").strip()
-        if not sku_id:
-            continue
-        if sku_id in seen:
-            duplicate_rows.append(sku_id)
-        seen.add(sku_id)
-        sku_ids.append(sku_id)
-    gallery = ProductGallery(stock, sku_ids or [])
-    views = {sku_id: gallery._views_for(sku_id) for sku_id in dict.fromkeys(sku_ids)}
-    missing = [sku_id for sku_id, paths in views.items() if not paths]
-    single = [sku_id for sku_id, paths in views.items() if len(paths) == 1]
-    claimed = {os.path.basename(path) for paths in views.values() for path in paths}
-    orphans = sorted(name for name in images if name not in claimed)
-    # ponytail: prefix collision check. A view named sku_001 must not also match sku_00.
-    collisions = []
-    for sku_id, paths in views.items():
-        bare = [os.path.basename(path) for path in paths if os.path.splitext(os.path.basename(path))[0] != sku_id
-                and not re.fullmatch(re.escape(sku_id) + r"[_-]\d+", os.path.splitext(os.path.basename(path))[0])]
-        if bare:
-            collisions.append(sku_id)
-    blockers = []
-    if duplicate_rows:
-        blockers.append("duplicate_csv_sku")
-    if missing:
-        blockers.append("sku_without_view")
-    if collisions:
-        blockers.append("view_prefix_collision")
-    head_path = os.path.join(stock, "identity_head.pt")
-    head_note = None
-    if os.path.exists(head_path):
-        head_note = "present_not_opened"
-        # Do not torch.load here: the head can be large, and this report must stay cheap.
-        # Class-list drift is reported when the caller passes class_ids.
-    return {
-        "stock": stock,
-        "sku_ids": list(dict.fromkeys(sku_ids)),
-        "images": len(images),
-        "missing": missing,
-        "single_view": single,
-        "orphans": orphans,
-        "duplicate_rows": duplicate_rows,
-        "collisions": collisions,
-        "blockers": blockers,
-        "head": head_note,
-    }
-
-
-def enrollment_report_text(report: dict) -> str:
-    return (
-        f"{report['stock']}: {len(report['sku_ids'])} SKUs, {report['images']} images, "
-        f"missing={len(report['missing'])}, single_view={len(report['single_view'])}, "
-        f"orphans={len(report['orphans'])}, blockers={report['blockers'] or 'none'}"
-    )
-
-
-def build_model(image_size: int = 518):
-    import torch
-
-    from gallery import load_dinov2
-
-    device = "mps" if torch.backends.mps.is_available() else "cpu"
-    # Same hash-pinned local file as inference. pretrained=True would fetch a
-    # different copy even with YOLO_OFFLINE=1.
-    model = load_dinov2(image_size)
-    return model.eval().to(device), device
-
-
-def prepare(image, size: int = 518):
-    mean = np.array([0.485, 0.456, 0.406], np.float32)
-    std = np.array([0.229, 0.224, 0.225], np.float32)
-    rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-    height, width = rgb.shape[:2]
-    side = max(height, width)
-    canvas = np.zeros((side, side, 3), np.uint8)
-    canvas[(side - height) // 2:(side - height) // 2 + height,
-           (side - width) // 2:(side - width) // 2 + width] = rgb
-    resized = cv2.resize(canvas, (size, size), interpolation=cv2.INTER_AREA).astype(np.float32) / 255.0
-    return ((resized - mean) / std).transpose(2, 0, 1)
-
-
-def embed(model, device, images, size: int = 518, batch: int = 16):
-    import torch
-
-    out = []
-    for start in range(0, len(images), batch):
-        tensor = torch.from_numpy(np.stack([prepare(image, size) for image in images[start:start + batch]])).to(device)
-        with torch.no_grad():
-            features = model(tensor)
-        out.append(torch.nn.functional.normalize(features, dim=1).cpu().numpy())
-    return np.concatenate(out, axis=0)
-
-
-def augment(image, rng):
-    """Stand in for a second visit: crop, scale, brightness, contrast, blur."""
-    height, width = image.shape[:2]
-    # crop 82-100 % of the box, keeping the product centred
-    keep = rng.uniform(0.82, 1.0)
-    ch, cw = int(height * keep), int(width * keep)
-    y = rng.randint(0, max(1, height - ch + 1))
-    x = rng.randint(0, max(1, width - cw + 1))
-    out = image[y:y + ch, x:x + cw]
-    # photometric
-    out = cv2.convertScaleAbs(out, alpha=float(rng.uniform(0.8, 1.2)), beta=float(rng.uniform(-18, 18)))
-    if rng.random() < 0.4:
-        out = cv2.GaussianBlur(out, (3, 3), 0)
-    # small rotation
-    if rng.random() < 0.6:
-        angle = float(rng.uniform(-5, 5))
-        matrix = cv2.getRotationMatrix2D((out.shape[1] / 2, out.shape[0] / 2), angle, 1.0)
-        out = cv2.warpAffine(out, matrix, (out.shape[1], out.shape[0]), borderMode=cv2.BORDER_REPLICATE)
-    return out
-
-
-def train_head(features, labels, class_ids, epochs, device, margin: float = 0.0, seed: int = 7):
-    """A cosine classifier on frozen features: small, and hard to overfit."""
-    import torch
-
-    torch.manual_seed(seed)
-    dimension = features.shape[1]
-    projection = torch.nn.Linear(dimension, 128).to(device)
-    prototypes = torch.nn.Parameter(torch.randn(len(class_ids), 128, device=device))
-    torch.nn.init.xavier_uniform_(prototypes)
-    optimiser = torch.optim.AdamW(list(projection.parameters()) + [prototypes], lr=3e-3, weight_decay=1e-2)
-
-    x = torch.from_numpy(features).float().to(device)
-    y = torch.tensor([class_ids.index(label) for label in labels], device=device)
-    for epoch in range(epochs):
-        projected = torch.nn.functional.normalize(projection(x), dim=1)
-        unit_prototypes = torch.nn.functional.normalize(prototypes, dim=1)
-        logits = projected @ unit_prototypes.T
-        if margin > 0:
-            # angular margin: pull each sample away from the other prototypes
-            one_hot = torch.nn.functional.one_hot(y, len(class_ids)).float()
-            logits = logits - margin * one_hot
-        loss = torch.nn.functional.cross_entropy(logits * 16.0, y, label_smoothing=0.05)
-        optimiser.zero_grad()
-        loss.backward()
-        optimiser.step()
-    return projection, torch.nn.functional.normalize(prototypes.detach(), dim=1)
-
-
-def train_from_stock(args) -> None:
-    """Train the head on a store's own enrolled views, for the gallery to load.
-
-    The head belongs to the store: the views in its stock directory are its products,
-    and a head trained on them is what turns the gallery from a lookup that cannot
-    tell two milk cartons apart into one that can. One to four views per SKU is
-    enough, with augmentation standing in for the rest.
-    """
-    import glob
-    import json
-
-    import torch
-
-    from gallery import DINOV2_MODEL, ProductGallery, catalog_digest, head_class_set
-
-    stock = args.stock
-    report = enrollment_report(stock)
-    print(enrollment_report_text(report))
-    if report["blockers"]:
-        print(f"{stock}: not training; enrollment blockers: {', '.join(report['blockers'])}")
-        return
-    gallery = ProductGallery(stock, report["sku_ids"])
-    views = {sku: gallery._views_for(sku) for sku in report["sku_ids"]}
-    views = {sku: paths for sku, paths in views.items() if paths}
-    if len(views) < 2:
-        print(f"{stock}: needs at least two products with views, found {len(views)}")
-        return
-
-    crops, labels = [], []
-    for sku, paths in sorted(views.items()):
-        for path in paths:
-            image = cv2.imread(path)
-            if image is not None and image.size:
-                crops.append(image)
-                labels.append(sku)
-    class_ids = sorted(views)
-    print(f"{stock}: {len(crops)} views over {len(class_ids)} products")
-
-    model, device = build_model()
-    rng = np.random.RandomState(11)
-    augmented, augmented_labels = [], []
-    for crop, label in zip(crops, labels):
-        augmented.append(crop)
-        augmented_labels.append(label)
-        for _ in range(args.augment):
-            augmented.append(augment(crop, rng))
-            augmented_labels.append(label)
-    print(f"embedding {len(augmented)} crops on {device}...")
-    features = embed(model, device, augmented)
-
-    projection, prototypes = train_head(features, augmented_labels, class_ids,
-                                        args.epochs, device, margin=0.25)
-    digest = catalog_digest(stock, class_ids)
-    if head_class_set(class_ids) is None or digest is None:
-        print(f"{stock}: refusing to write a head; class ids are empty or duplicated")
-        return
-    payload = {
-        "class_ids": class_ids,
-        "catalog_digest": digest,
-        "image_size": 518,
-        "base_model": DINOV2_MODEL,
-        "projection": {key: value.cpu() for key, value in projection.state_dict().items()},
-        "prototypes": prototypes.cpu(),
-        "views": {sku: len(paths) for sku, paths in sorted(views.items())},
-    }
-    path = os.path.join(stock, "identity_head.pt")
-    torch.save(payload, path)
-    print(f"wrote {path} ({len(class_ids)} products, {len(crops)} views)")
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--epochs", type=int, default=60)
-    parser.add_argument("--views", type=int, default=2, help="enrolment views per class")
-    parser.add_argument("--augment", type=int, default=12, help="augmented copies per enrolment view")
-    parser.add_argument("--image", default=os.path.join(
-        os.path.dirname(os.path.dirname(os.path.abspath(__file__))),
-        "data", "external-shelves", "wikimedia-commons"))
-    parser.add_argument("--stock", default=None,
-                        help="train on a store's enrolled views instead of the milk shelf, "
-                             "and write <stock>/identity_head.pt for the gallery to load")
-    args = parser.parse_args()
-    if args.stock:
-        train_from_stock(args)
-        return
-
-    import json
-
-    from engine import build_default_engine, EngineSettings
-
-    manifest = json.load(open(os.path.join(args.image, "manifest.json")))
-    entry = next(e for e in manifest["images"] if "2011年4月の牛乳" in e["file"])
-    image = cv2.imread(os.path.join(args.image, entry["file"]))
-    height, width = image.shape[:2]
-    scale = 1600.0 / max(height, width)
-    image = cv2.resize(image, (int(width * scale), int(height * scale)), interpolation=cv2.INTER_AREA)
-
-    engine = build_default_engine(EngineSettings(stock_dir="stock"))
-    boxes = engine.detect(image, conf=0.1)
-    boxes.sort(key=lambda b: (b["box"][1] // 120, b["box"][0]))
-    crops = [engine.crop(image, box["box"]) for box in boxes]
-
-    train_crops, train_labels, test_crops, test_labels = [], [], [], []
-    for label, indices in MILK_SHELF_LABELS.items():
-        for position, index in enumerate(indices):
-            if position < args.views:
-                train_crops.append(crops[index])
-                train_labels.append(label)
-            else:
-                test_crops.append(crops[index])
-                test_labels.append(label)
-    class_ids = sorted(MILK_SHELF_LABELS)
-    print(f"{len(train_crops)} enrolment views, {len(test_crops)} held-out facings, {len(class_ids)} products")
-
-    model, device = build_model()
-    print(f"embedding {len(train_crops) * (args.augment + 1) + len(test_crops)} crops on {device}...")
-
-    rng = np.random.RandomState(11)
-    augmented, augmented_labels = [], []
-    for crop, label in zip(train_crops, train_labels):
-        augmented.append(crop)
-        augmented_labels.append(label)
-        for _ in range(args.augment):
-            augmented.append(augment(crop, rng))
-            augmented_labels.append(label)
-
-    train_features = embed(model, device, augmented)
-    test_features = embed(model, device, test_crops)
-
-    def score(similarity, label, threshold=None):
-        """Top-1 over every held-out facing, with optional rejection.
-
-        Balanced accuracy is reported alongside the raw rate because the shelf is
-        unbalanced (MEGMILK has 34 facings, 酪農3.6 has two), and a system that only
-        ever answers "MEGMILK" would look respectable on the raw number.
-        """
-        per_class = {class_id: [0, 0] for class_id in class_ids}
-        correct = rejected = 0
-        for index, truth in enumerate(test_labels):
-            best = int(similarity[index].argmax())
-            if threshold is not None and similarity[index][best] < threshold:
-                rejected += 1
-                per_class[truth][1] += 1
-                continue
-            guess = class_ids[best]
-            per_class[truth][1] += 1
-            if guess == truth:
-                correct += 1
-                per_class[truth][0] += 1
-        recalls = [found / total for found, total in per_class.values() if total]
-        balanced = sum(recalls) / max(1, len(recalls))
-        answered = len(test_labels) - rejected
-        answered_accuracy = correct / max(1, answered)
-        print(f"  {label:<26} top-1 {correct:>3}/{len(test_labels)} = {100*correct//len(test_labels):>3}%   "
-              f"balanced {100*balanced:>3.0f}%   answered {answered:>3} "
-              f"({100*answered_accuracy:>3.0f}% of those right)   rejected {rejected}")
-        return per_class
-
-    import torch
-
-    def class_prototypes(features):
-        prototypes = []
-        for class_id in class_ids:
-            members = [i for i, label in enumerate(train_labels) if label == class_id]
-            mean = features[members].mean(axis=0)
-            prototypes.append(mean / (np.linalg.norm(mean) + 1e-8))
-        return np.stack(prototypes)
-
-    # 1. raw features, matched the way the shipped gallery matches: best enrolment view
-    raw_prototypes = class_prototypes(train_features)
-    score(test_features @ raw_prototypes.T, "raw DINOv2 cosine")
-    score(test_features @ raw_prototypes.T, "raw, rejecting below 0.55", threshold=0.55)
-
-    # 2. the trained classifier's prototypes, with an angular margin
-    projection, prototypes = train_head(train_features, augmented_labels, class_ids,
-                                        args.epochs, device, margin=0.25)
-    with torch.no_grad():
-        projected_test = torch.nn.functional.normalize(
-            projection(torch.from_numpy(test_features).float().to(device)), dim=1).cpu().numpy()
-    similarity = projected_test @ prototypes.cpu().numpy().T
-    score(similarity, "fine-tuned + margin")
-    per_class = score(similarity, "fine-tuned, rejecting < 0.4", threshold=0.4)
-    print("    per class, answered only:")
-    for class_id in class_ids:
-        found, total = per_class[class_id]
-        if total:
-            print(f"      {class_id:<16} {found:>2}/{total:<3} {100*found//total:>4}%")
-
-    print("\n  note: enrolment and held-out facings come from one photograph, so these are")
-    print("  upper bounds. A cross-visit number needs a second visit, not a second split.")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/recognization/code/gallery.py b/recognization/code/gallery.py
deleted file mode 100644
index 9c1bd32..0000000
--- a/recognization/code/gallery.py
+++ /dev/null
@@ -1,712 +0,0 @@
-"""
-A product gallery: identity by retrieval, with several views per SKU.
-
-The matcher this replaces scores a crop against one template per SKU. Measured on
-the demo shelf, that is the wrong shape for a store:
-
-* one enrolled view identifies 33 % of facings (matcher) / 57 % (embedding);
-* two views per SKU lift the embedding to 72 %, three to 75 %;
-* the matcher gets *worse* with more views (33 % -> 5 %), because its raw inlier
-  score is not calibrated to be aggregated across views - and it cannot even
-  express two views of one SKU, since it keys templates by filename.
-
-So the gallery is embedding-based. DINOv2 ViT-S/14 over aspect-preserved crops,
-cosine similarity, several views per SKU, and a rejection threshold so an unknown
-product comes back as unknown instead of as the nearest stranger.
-
-Cost, measured on an Apple M5 (24 crops, batched and one at a time):
-
-    224 px   CPU 25 ms/crop   MPS 2.4 ms/crop
-    518 px   CPU 159 ms/crop  MPS 3.6 ms/crop
-
-A live frame only needs its *new* tracks embedded (2-4 of them), so ~10 ms a frame
-on the GPU. That is why this can sit in the live loop at all.
-"""
-
-from __future__ import annotations
-
-import hashlib
-import os
-import re
-import threading
-from dataclasses import dataclass, field
-from typing import Any, Dict, List, Optional, Sequence, Tuple
-
-# Below this cosine similarity the nearest view is not the same product. Chosen
-# from the measured distribution: same-product pairs sit above 0.6, different
-# products below 0.5, and the gap is where a rejection belongs.
-DEFAULT_THRESHOLD = 0.55
-# The same line for a fine-tuned head, whose similarity scale is its own.
-DEFAULT_HEAD_THRESHOLD = 0.40
-
-# DINOv2 ViT-S/14, LVD-142M. timm's default source is the Hugging Face copy of
-# these weights (Apache-2.0); pretrained=True fetches it even when YOLO_OFFLINE=1.
-# The pin is the sha256 of that model.safetensors (timm hub snapshot 4610ca14).
-# Not in Git (~84 MB). Place it at weights/dinov2_vits14_lvd142m.safetensors, or
-# point DORAYAKI_DINOV2_WEIGHTS at a copy. See README "DINOv2 weights".
-DINOV2_MODEL = "vit_small_patch14_dinov2.lvd142m"
-DINOV2_SHA256 = "04d27f3400d059fc0cfd7d17dd1909a75bf3ea8fb3eeb48b97cb99e57ee20081"
-DINOV2_FILENAME = "dinov2_vits14_lvd142m.safetensors"
-# What load_dinov2 returns: the same 384 the fine-tuned head is Linear(384, 128)
-# on, and the width a cached matrix has to have to belong to this backbone.
-DINOV2_EMBED_DIM = 384
-
-# Bumped when _prepare() or the cache layout changes. A matrix embedded under an
-# older preprocessing pass is stale, not merely old, so it must be re-embedded.
-CACHE_VERSION = 1
-
-
-# One thread at a time runs the gallery backbone. Metal's shader library is
-# process-global and is not thread-safe while kernels are still being compiled
-# into it, which is what the first forward on a fresh process does; two threads
-# reaching it together segfault the whole server. Module-level because the state
-# being protected is the process's, not one gallery's.
-# ponytail: serializes every embed. Audit C5's batching embedding worker is the
-# upgrade path, and a second MPS consumer (--device mps puts YOLO on Metal too)
-# would need to take this same lock rather than its own.
-_MPS_INFER_LOCK = threading.Lock()
-
-
-def dinov2_weights_path() -> str:
-    override = os.environ.get("DORAYAKI_DINOV2_WEIGHTS")
-    if override:
-        return override
-    here = os.path.dirname(os.path.abspath(__file__))
-    return os.path.join(os.path.dirname(here), "weights", DINOV2_FILENAME)
-
-
-def _sha256(path: str) -> str:
-    import hashlib
-
-    digest = hashlib.sha256()
-    with open(path, "rb") as handle:
-        for chunk in iter(lambda: handle.read(1024 * 1024), b""):
-            digest.update(chunk)
-    return digest.hexdigest()
-
-
-def load_dinov2(image_size: int) -> Any:
-    """Build the gallery backbone from the pinned local file. Never downloads.
-
-    A missing or altered file raises FileNotFoundError naming the path and the
-    expected sha256. The head in finetune_identity.py is a Linear(384, 128) on
-    this model's features; a different backbone will not load that head.
-    """
-    import timm
-    from safetensors.torch import load_file
-    from timm.models.vision_transformer import checkpoint_filter_fn
-
-    path = dinov2_weights_path()
-    if not os.path.isfile(path):
-        raise FileNotFoundError(
-            f"DINOv2 weights not found at {path}. "
-            f"Copy model.safetensors (sha256 {DINOV2_SHA256}) from the local "
-            f"Hugging Face cache of timm/{DINOV2_MODEL}, or set DORAYAKI_DINOV2_WEIGHTS. "
-            f"The gallery does not download weights."
-        )
-    digest = _sha256(path)
-    if digest != DINOV2_SHA256:
-        raise FileNotFoundError(
-            f"DINOv2 weights at {path} are sha256 {digest}, expected {DINOV2_SHA256}."
-        )
-    model = timm.create_model(
-        DINOV2_MODEL,
-        pretrained=False,
-        num_classes=0,
-        img_size=int(image_size),
-        dynamic_img_size=True,
-    )
-    # The file is the 518px checkpoint. timm's own filter resizes pos_embed when
-    # the caller asks for another size; pretrained=True would do this and also
-    # download, so it stays off.
-    state = checkpoint_filter_fn(load_file(path), model)
-    missing, unexpected = model.load_state_dict(state, strict=False)
-    unexpected = [key for key in unexpected if not key.startswith("head.")]
-    if missing or unexpected:
-        raise RuntimeError(
-            f"DINOv2 weights at {path} do not match {DINOV2_MODEL} "
-            f"(missing={missing}, unexpected={unexpected})."
-        )
-    return model
-
-
-@dataclass
-class GalleryEntry:
-    sku_id: str
-    view: str
-    embedding: Any = None
-
-
-@dataclass
-class GalleryMatch:
-    sku_id: Optional[str]
-    similarity: float
-    runner_up: Optional[str] = None
-    runner_up_similarity: float = 0.0
-    rejected: bool = False
-
-    @property
-    def margin(self) -> float:
-        return self.similarity - self.runner_up_similarity
-
-
-def head_class_set(sku_ids: Sequence[str]) -> Optional[List[str]]:
-    """Catalog order, or None when an id is empty or repeated."""
-    cleaned = [str(sku_id).strip() for sku_id in sku_ids]
-    if not cleaned or any(not sku_id for sku_id in cleaned):
-        return None
-    if len(set(cleaned)) != len(cleaned):
-        return None
-    return cleaned
-
-
-def catalog_digest(stock_dir: str, sku_ids: Sequence[str], csv_name: str = "stock.csv") -> Optional[str]:
-    """Pin for identity_head.pt: stock.csv bytes plus the catalog id list.
-
-    Image sizes are not included, so another view of the same SKU does not
-    invalidate a head. A changed row or id does. None if the id list is unsafe.
-    """
-    cleaned = head_class_set(sku_ids)
-    if cleaned is None:
-        return None
-    digest = hashlib.sha256()
-    csv_path = os.path.join(stock_dir, csv_name)
-    try:
-        with open(csv_path, "rb") as handle:
-            digest.update(handle.read())
-    except OSError:
-        digest.update(b"missing-csv")
-    digest.update(b"\n")
-    digest.update("\n".join(sorted(cleaned)).encode("utf-8"))
-    return digest.hexdigest()
-
-
-
-
-class ProductGallery:
-    """Several views per SKU, embedded once, queried by cosine similarity."""
-
-    def __init__(
-        self,
-        stock_dir: str,
-        sku_ids: Sequence[str],
-        device: Optional[str] = None,
-        image_size: int = 518,
-        threshold: float = DEFAULT_THRESHOLD,
-        head_threshold: float = DEFAULT_HEAD_THRESHOLD,
-    ) -> None:
-        self.stock_dir = stock_dir
-        self.sku_ids = list(sku_ids)
-        self.image_size = int(image_size)
-        self.threshold = float(threshold)
-        # A trained head produces a different similarity scale than raw features, so
-        # it needs its own line. Measured on a real milk shelf: the head answers 18 of
-        # 72 facings above 0.4 and is right on 94 % of them, while the raw threshold
-        # (0.55) rejects almost everything the head says.
-        self.head_threshold = float(head_threshold)
-        self.device = device
-        self._model = None
-        self._lock = threading.Lock()
-        self._entries: List[GalleryEntry] = []
-        self._matrix = None
-        self._available = False
-        # The source fingerprint this matrix was built from, and the per-SKU
-        # segment bounds derived from the entries it describes.
-        self._loaded_key = ""
-        self._rank = None
-        # A fine-tuned projection head, if the store has one (see
-        # finetune_identity.py). Measured on a real milk shelf: raw features put 1 %
-        # balanced accuracy on fine-grained products, a trained head 73 %.
-        self._head = None
-        self.head_reason = "not_loaded"
-        self._head_path = os.path.join(stock_dir, "identity_head.pt")
-
-    # ------------------------------------------------------------------ loading
-    def load_head(self) -> bool:
-        """Load the store's fine-tuned head when its labels are this catalog's.
-
-        weights_only: the file is tensors and strings, not executable code.
-        A missing, malformed, or foreign head leaves the raw gallery in place
-        and records head_reason. Class ids must be unique, nonempty, and a
-        subset of this gallery: a head may cover only the SKUs it was trained
-        on. A catalog_digest, when present, must match stock.csv plus that
-        subset. A head written before the pin loads with head_reason
-        legacy_unpinned; it still cannot name a SKU outside this catalog.
-        """
-        import torch
-
-        self._head = None
-        if not os.path.exists(self._head_path):
-            self.head_reason = "missing"
-            return False
-        try:
-            payload = torch.load(self._head_path, map_location="cpu", weights_only=True)
-            reason = self._accept_head(payload, torch)
-        except Exception:
-            self._head = None
-            self.head_reason = "malformed"
-            return False
-        if not reason.startswith("ok"):
-            self._head = None
-            self.head_reason = reason
-            return False
-        self.head_reason = "legacy_unpinned" if reason == "ok:legacy_unpinned" else "ok"
-        return True
-
-    def _accept_head(self, payload: Any, torch: Any) -> str:
-        if not isinstance(payload, dict):
-            return "malformed"
-        projection_state = payload.get("projection")
-        prototypes = payload.get("prototypes")
-        class_ids = payload.get("class_ids")
-        if not isinstance(projection_state, dict) or not torch.is_tensor(prototypes):
-            return "malformed"
-        if not isinstance(class_ids, (list, tuple)) or prototypes.ndim != 2:
-            return "malformed"
-        if prototypes.shape[0] != len(class_ids) or prototypes.shape[1] != 128:
-            return "malformed"
-        labels = []
-        for sku_id in class_ids:
-            if not isinstance(sku_id, str) or not sku_id.strip():
-                return "malformed: identity head class_ids must be nonempty strings"
-            labels.append(sku_id.strip())
-        if len(set(labels)) != len(labels):
-            return "malformed: identity head class_ids are not unique"
-        expected = head_class_set(self.sku_ids)
-        if expected is None or not set(labels) <= set(expected):
-            return "catalog_mismatch: head class_ids are not a subset of this catalog; retrain finetune_identity.py --stock"
-        recorded = payload.get("catalog_digest", None)
-        if recorded is None:
-            reason = "ok:legacy_unpinned"
-        else:
-            pinned = catalog_digest(self.stock_dir, labels)
-            if pinned is None or recorded != pinned:
-                return "catalog_mismatch: head catalog_digest does not match stock.csv and its class_ids; retrain finetune_identity.py --stock"
-            reason = "ok"
-        if payload.get("base_model") != DINOV2_MODEL:
-            return f"backbone_mismatch: head base_model is not {DINOV2_MODEL}"
-        if type(payload.get("image_size")) is not int or payload.get("image_size") != self.image_size:
-            return f"image_size_mismatch: head image_size is not {self.image_size}"
-        projection = torch.nn.Linear(384, 128)
-        projection.load_state_dict(projection_state)
-        projection.eval()
-        self._head = {
-            "projection": projection,
-            "prototypes": torch.nn.functional.normalize(prototypes.float(), dim=1),
-            "class_ids": labels,
-        }
-        return reason
-
-
-    def _load_model(self) -> Any:
-        if self._model is not None:
-            return self._model
-        import torch
-
-        device = self.device
-        if device is None:
-            device = "mps" if getattr(torch.backends, "mps", None) and torch.backends.mps.is_available() else "cpu"
-        model = load_dinov2(self.image_size)
-        model = model.eval().to(device)
-        self.device = device
-        self._model = model
-        return model
-
-    def available(self) -> bool:
-        return self._available
-
-    def _views_for(self, sku_id: str) -> List[str]:
-        """Images whose stem is the SKU or SKU plus _N / -N.
-
-        A bare prefix would enroll sku_001 as a view of sku_00.
-        """
-        found: List[str] = []
-        extra = re.compile(rf"{re.escape(sku_id)}[_-]\d+$")
-        try:
-            names = sorted(os.listdir(self.stock_dir))
-        except OSError:
-            return found
-        for name in names:
-            stem, extension = os.path.splitext(name)
-            if extension.lower() not in (".jpeg", ".jpg", ".png"):
-                continue
-            if stem == sku_id or extra.fullmatch(stem):
-                found.append(os.path.join(self.stock_dir, name))
-        return found
-
-    def _enrolled(self) -> List[Tuple[str, str]]:
-        """Every (sku, view) that would be embedded, in embed order."""
-        return [
-            (sku_id, path)
-            for sku_id in self.sku_ids
-            for path in self._views_for(sku_id)
-        ]
-
-    def _source_key(self, enrolled: Sequence[Tuple[str, str]]) -> str:
-        """A fingerprint of exactly the inputs an embedding depends on.
-
-        The view bytes, not the name and length: a re-encoded JPEG of the same
-        size moves the embedding, and a wrong row is worse than a slow start.
-        Empty when a view cannot be read, which no cache may answer for.
-        """
-        digest = hashlib.sha256()
-        digest.update(f"v{CACHE_VERSION}|{self.image_size}|{DINOV2_SHA256}\n".encode("utf-8"))
-        try:
-            with open(os.path.join(self.stock_dir, "stock.csv"), "rb") as handle:
-                digest.update(handle.read())
-        except OSError:
-            digest.update(b"missing-csv")
-        for sku_id, path in enrolled:
-            digest.update(f"\n{sku_id}\x00{os.path.basename(path)}\x00".encode("utf-8"))
-            try:
-                with open(path, "rb") as handle:
-                    digest.update(hashlib.sha256(handle.read()).digest())
-            except OSError:
-                return ""
-        return digest.hexdigest()
-
-    def _cache_path(self) -> str:
-        return os.path.join(self.stock_dir, f".gallery_cache_{self.image_size}.npz")
-
-    def _weights_pinned(self) -> bool:
-        """The backbone on disk is still the one this matrix was embedded with.
-
-        A cache hit never opens load_dinov2, so without this a deleted or altered
-        checkpoint would read as a loaded gallery and only fail on the first
-        match - after the server had already reported itself ready.
-        """
-        path = dinov2_weights_path()
-        return os.path.isfile(path) and _sha256(path) == DINOV2_SHA256
-
-    def _cache_read(self, key: str, enrolled: Sequence[Tuple[str, str]]) -> Optional[Tuple[List[Tuple[str, str]], Any]]:
-        """The cached rows when they are provably this store's, else None."""
-        import numpy as np
-
-        try:
-            with np.load(self._cache_path(), allow_pickle=False) as payload:
-                if str(payload["key"].item()) != key:
-                    return None
-                skus = [str(value) for value in payload["skus"].tolist()]
-                views = [str(value) for value in payload["views"].tolist()]
-                matrix = payload["matrix"]
-            if matrix.ndim != 2 or matrix.shape[1] != DINOV2_EMBED_DIM:
-                return None
-            if not matrix.shape[0] or matrix.shape[0] != len(skus) or len(skus) != len(views):
-                return None
-            if not np.issubdtype(matrix.dtype, np.floating) or not np.isfinite(matrix).all():
-                return None
-            # Rows walk the enrolled list forward and never backwards, so a
-            # moved, repeated or foreign row has nowhere left to match. Views
-            # that could not be decoded are simply absent: everyone after one
-            # keeps its order, which is what stops a valid key from renaming a
-            # SKU.
-            expected = [(sku_id, os.path.basename(view)) for sku_id, view in enrolled]
-            rows: List[Tuple[str, str]] = []
-            position = 0
-            for sku_id, view in zip(skus, views):
-                while position < len(expected) and expected[position] != (sku_id, view):
-                    position += 1
-                if position == len(expected):
-                    return None
-                rows.append((sku_id, os.path.join(self.stock_dir, view)))
-                position += 1
-            if not self._weights_pinned():
-                return None
-            return rows, matrix.astype(np.float32, copy=False)
-        except (OSError, ValueError, TypeError, KeyError, IndexError):
-            return None  # absent, malformed, or raced with a checkpoint removal
-
-    def _cache_write(self, key: str, rows: Sequence[Tuple[str, str]], matrix: Any) -> None:
-        """Replace the cache file in one step. A failed write is not a failed load."""
-        import numpy as np
-
-        path = self._cache_path()
-        tmp = f"{path}.{os.getpid()}.tmp"
-        try:
-            with open(tmp, "wb") as handle:
-                np.savez(
-                    handle,
-                    key=np.array(key),
-                    skus=np.array([sku_id for sku_id, _ in rows]),
-                    views=np.array([os.path.basename(view) for _, view in rows]),
-                    matrix=matrix,
-                )
-            os.replace(tmp, path)
-        except OSError:  # read-only store, no space: embed again next time
-            try:
-                os.unlink(tmp)
-            except OSError:
-                pass
-
-    def _embed_views(self, enrolled: Sequence[Tuple[str, str]]) -> Optional[Tuple[List[Tuple[str, str]], Any]]:
-        """Embed every readable view once. None when the backbone is unusable."""
-        import cv2
-        import numpy as np
-        import torch
-
-        rows: List[Tuple[str, str]] = []
-        kept: List[Any] = []
-        for sku_id, path in enrolled:
-            image = cv2.imread(path)
-            if image is None or image.size == 0:
-                continue
-            rows.append((sku_id, path))
-            kept.append(_prepare(image, self.image_size))
-        if not kept:
-            return None
-        try:
-            with _MPS_INFER_LOCK:
-                model = self._load_model()
-                tensor = torch.from_numpy(np.stack(kept)).to(self.device)
-                with torch.no_grad():
-                    features = model(tensor)
-                features = torch.nn.functional.normalize(features, dim=1).cpu().numpy()
-        except Exception:
-            return None
-        return rows, features.astype(np.float32, copy=False)
-
-    def load(self) -> bool:
-        """Embed every enrolled view. Returns False when there is nothing to load.
-
-        One lock covers the whole load: two callers cannot each embed the catalog.
-        A loaded gallery is kept only while the store still fingerprints the
-        same, so a replaced or reordered enrollment re-embeds instead of
-        answering from a matrix that no longer describes it.
-        """
-        with self._lock:
-            if self._available and self._matrix is not None:
-                enrolled = self._enrolled()
-                if enrolled and self._source_key(enrolled) == self._loaded_key:
-                    return True
-            return self._load_locked()
-
-    def _unload(self) -> None:
-        self._available = False
-        self._entries = []
-        self._matrix = None
-        self._rank = None
-        self._loaded_key = ""
-
-    def _load_locked(self) -> bool:
-        enrolled = self._enrolled()
-        if not enrolled:
-            self._unload()
-            return False
-        key = self._source_key(enrolled)
-        cached = self._cache_read(key, enrolled) if key else None
-        if cached is None:
-            embedded = self._embed_views(enrolled)
-            if embedded is None:
-                self._unload()
-                return False
-            rows, features = embedded
-            if key:  # an unreadable view has no key; never write one we cannot read
-                self._cache_write(key, rows, features)
-        else:
-            rows, features = cached
-            # A cache hit skips the embedding forward, so without this the model
-            # is built and Metal's kernels are compiled by whichever classify
-            # worker asks first - up to eight racing into one process-wide cache.
-            # The readiness path loads the gallery before the socket accepts, so
-            # the warmup is free and no worker meets a cold model.
-            #
-            # Best-effort on purpose: a cache hit is served without a usable
-            # backbone by contract, and _MPS_INFER_LOCK already makes the lazy
-            # path safe. This only spares the first worker the compile.
-            self._warm_backbone()
-        self._entries = [
-            GalleryEntry(sku_id=sku_id, view=path, embedding=features[index])
-            for index, (sku_id, path) in enumerate(rows)
-        ]
-        self._matrix = features
-        self._available = True
-        self._rank = None
-        self._loaded_key = key
-        self.load_head()
-        return True
-
-    # ------------------------------------------------------------------ querying
-    def embed(self, crop: Any) -> Any:
-        import numpy as np
-        import torch
-
-        with _MPS_INFER_LOCK:
-            model = self._load_model()
-            tensor = torch.from_numpy(np.stack([_prepare(crop, self.image_size)])).to(self.device)
-            with torch.no_grad():
-                features = model(tensor)
-            features = torch.nn.functional.normalize(features, dim=1).cpu().numpy()
-        return features[0]
-
-    def _warm_backbone(self) -> None:
-        """Build the backbone and compile its kernels, single-threaded.
-
-        Called under self._lock from the load path, so the lock order is
-        self._lock then _MPS_INFER_LOCK - the same order _embed_views takes, and
-        no path takes them reversed. The forward is a dummy canvas: what is
-        being warmed is the model and the shader library, not any one crop.
-
-        Metal only. A CPU gallery has no shader library to compile and a cache
-        hit is served without touching the backbone by contract, so warming one
-        would spend a model load to protect against nothing.
-        """
-        import numpy as np
-
-        if self._resolved_device() != "mps":
-            return
-        try:
-            self.embed(np.zeros((self.image_size, self.image_size, 3), dtype=np.uint8))
-        except Exception:
-            pass
-
-    def _resolved_device(self) -> str:
-        """The device the backbone will run on, without building it."""
-        if self.device is not None:
-            return self.device
-        import torch
-
-        if getattr(torch.backends, "mps", None) and torch.backends.mps.is_available():
-            return "mps"
-        return "cpu"
-
-    def _rank_index(self) -> Tuple[List[str], Any, Any]:
-        """Segment bounds for the per-SKU maximum, in first-appearance order.
-
-        Entries may arrive in any order, so the grouping is derived from them, not
-        assumed. Memoised against the list it was built from: replacing the list
-        rebuilds it, and a SKU list is never longer than the views behind it.
-        """
-        import numpy as np
-
-        if self._rank is not None and self._rank[0] is self._entries:
-            return self._rank[1], self._rank[2], self._rank[3]
-        order: List[str] = []
-        first_seen: Dict[str, int] = {}
-        rows: List[int] = []
-        for entry in self._entries:
-            index = first_seen.get(entry.sku_id)
-            if index is None:
-                index = len(order)
-                first_seen[entry.sku_id] = index
-                order.append(entry.sku_id)
-            rows.append(index)
-        column = np.asarray(rows, dtype=np.intp)
-        # A stable sort groups the rows by SKU; reduceat then takes each segment's
-        # max, which is a SKU's best view whatever order its views were enrolled in.
-        grouped = np.argsort(column, kind="stable")
-        starts = np.searchsorted(column[grouped], np.arange(len(order)))
-        self._rank = (self._entries, order, grouped, starts)
-        return order, grouped, starts
-
-    def match(self, crop: Any, threshold: Optional[float] = None) -> GalleryMatch:
-        """Nearest enrolled product, with a rejection when nothing is close enough."""
-        if not self._available or self._matrix is None:
-            return GalleryMatch(sku_id=None, similarity=0.0, rejected=True)
-        import numpy as np
-
-        limit = threshold
-        if limit is None:
-            limit = self.head_threshold if self._head is not None else self.threshold
-        limit = float(limit)
-        embedding = self.embed(crop)
-        if self._head is not None:
-            import torch
-            # Two stages, because the two mechanisms fail in opposite directions.
-            # Measured: raw features reject strangers well (5 % false accepts) but
-            # rank fine-grained products badly (2 % top-1); a trained head ranks them
-            # well (73 % balanced) but is confidently wrong on strangers (44 % false
-            # accepts), because a discriminative head has no "none of the above".
-            # So the raw features decide *whether* to answer, and the head decides
-            # *what* to answer.
-            raw_similarity = self._matrix @ embedding
-            best_view = float(raw_similarity.max()) if len(raw_similarity) else 0.0
-            if best_view < self.threshold:
-                return GalleryMatch(sku_id=None, similarity=best_view, rejected=True)
-            with torch.no_grad():
-                projected = torch.nn.functional.normalize(
-                    self._head["projection"](torch.from_numpy(embedding).float()), dim=0
-                )
-                similarity = (self._head["prototypes"] @ projected).numpy()
-            ranked = sorted(zip(self._head["class_ids"], similarity), key=lambda kv: kv[1], reverse=True)
-            top_sku, top_score = ranked[0]
-            runner_sku, runner_score = ranked[1] if len(ranked) > 1 else (None, 0.0)
-            if float(top_score) < limit:
-                return GalleryMatch(sku_id=None, similarity=float(top_score), runner_up=top_sku,
-                                    runner_up_similarity=float(top_score), rejected=True)
-            return GalleryMatch(sku_id=top_sku, similarity=float(top_score), runner_up=runner_sku,
-                                runner_up_similarity=float(runner_score))
-        similarity = self._matrix @ embedding
-        # Several views per SKU: a SKU's score is its best view.
-        order, grouped, starts = self._rank_index()
-        if not order:
-            return GalleryMatch(sku_id=None, similarity=0.0, rejected=True)
-        best_by_sku = np.maximum.reduceat(similarity[grouped], starts)
-        ranked = sorted(zip(order, best_by_sku.tolist()), key=lambda item: item[1], reverse=True)
-        top_sku, top_score = ranked[0]
-        runner_sku, runner_score = ranked[1] if len(ranked) > 1 else (None, 0.0)
-        if top_score < limit:
-            return GalleryMatch(sku_id=None, similarity=top_score, runner_up=top_sku,
-                                runner_up_similarity=top_score, rejected=True)
-        return GalleryMatch(sku_id=top_sku, similarity=top_score, runner_up=runner_sku,
-                            runner_up_similarity=runner_score)
-
-    def rank(self, crop: Any, limit: Optional[int] = None) -> List[Tuple[str, float]]:
-        """Every enrolled SKU by similarity, best first, with no rejection.
-
-        `match()` answers "is anything close enough"; a shortlist needs "what are
-        the closest candidates", including when none of them is close. Gating a
-        shortlist at the raw threshold empties it for exactly the crops the
-        verifier exists to rescue.
-        """
-        if not self._available or self._matrix is None:
-            return []
-        import numpy as np
-
-        embedding = self.embed(crop)
-        if self._head is not None:
-            import torch
-            with torch.no_grad():
-                projected = torch.nn.functional.normalize(
-                    self._head["projection"](torch.from_numpy(embedding).float()), dim=0
-                )
-                similarity = (self._head["prototypes"] @ projected).numpy()
-            ranked = sorted(zip(self._head["class_ids"], similarity.tolist()),
-                            key=lambda kv: kv[1], reverse=True)
-        else:
-            similarity = self._matrix @ embedding
-            order, grouped, starts = self._rank_index()
-            if not order:
-                return []
-            best_by_sku = np.maximum.reduceat(similarity[grouped], starts)
-            ranked = sorted(zip(order, best_by_sku.tolist()), key=lambda item: item[1], reverse=True)
-        return [(str(sku), float(score)) for sku, score in ranked[:limit]] if limit else \
-               [(str(sku), float(score)) for sku, score in ranked]
-
-    def head_loaded(self) -> bool:
-        return self._head is not None
-
-    def view_count(self) -> int:
-        return len(self._entries)
-
-    def sku_view_counts(self) -> Dict[str, int]:
-        counts: Dict[str, int] = {}
-        for entry in self._entries:
-            counts[entry.sku_id] = counts.get(entry.sku_id, 0) + 1
-        return counts
-
-
-def _prepare(image: Any, size: int) -> Any:
-    """Aspect-preserving square canvas: the crop's shape is part of the product."""
-    import cv2
-    import numpy as np
-
-    mean = np.array([0.485, 0.456, 0.406], np.float32)
-    std = np.array([0.229, 0.224, 0.225], np.float32)
-    rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-    height, width = rgb.shape[:2]
-    side = max(height, width)
-    canvas = np.zeros((side, side, 3), np.uint8)
-    canvas[(side - height) // 2:(side - height) // 2 + height,
-           (side - width) // 2:(side - width) // 2 + width] = rgb
-    resized = cv2.resize(canvas, (size, size), interpolation=cv2.INTER_AREA).astype(np.float32) / 255.0
-    return ((resized - mean) / std).transpose(2, 0, 1)
diff --git a/recognization/code/gui_app.py b/recognization/code/gui_app.py
new file mode 100644
index 0000000..df92251
--- /dev/null
+++ b/recognization/code/gui_app.py
@@ -0,0 +1,92 @@
+"""
+Native Desktop GUI for Supermarket Shelf Product Recognition
+Opens a desktop OpenCV window displaying image.jpg with bounding boxes only.
+Users can click on any product directly on screen to inspect price, stock,
+and feature matching inliers in real-time.
+"""
+
+import os
+import sys
+import cv2
+import numpy as np
+
+current_dir = os.path.dirname(os.path.abspath(__file__))
+if current_dir not in sys.path:
+    sys.path.insert(0, current_dir)
+
+from pipeline import ShelfRecognitionPipeline
+
+
+def main():
+    print("=" * 60)
+    print("STARTING NATIVE DESKTOP INTERFACE")
+    print("Image: image.jpg | Mode: Hybrid (Paper Method)")
+    print("Instructions: Click on ANY product to identify & view info.")
+    print("Press 'q' or 'ESC' on the window to exit.")
+    print("=" * 60)
+
+    pipeline = ShelfRecognitionPipeline()
+    init_res = pipeline.set_active_shelf_image("image.jpg")
+
+    if init_res.get("status") != "success":
+        print("[Error] Failed to load image.jpg")
+        return
+
+    boxes = init_res["boxes"]
+    base_image = pipeline.current_image.copy()
+
+    # Draw ONLY bounding boxes (no class names, no confidence labels)
+    display_image = pipeline.detector.draw_detections(base_image, boxes, draw_labels=False)
+
+    window_name = "Smart Shelf - Click Any Product (Press Q to quit)"
+    cv2.namedWindow(window_name, cv2.WINDOW_NORMAL)
+    cv2.resizeWindow(window_name, 1280, 850)
+
+    def on_mouse(event, x, y, flags, param):
+        if event == cv2.EVENT_LBUTTONDOWN:
+            print(f"\n[Click detected at coordinates: ({x}, {y})]")
+            res = pipeline.process_click(x, y, method="hybrid")
+
+            if res.get("status") == "success":
+                prod = res["inventory"]
+                cls_info = res["classification"]
+                box_info = res["target_box"]
+
+                print("-" * 55)
+                print(f"📦 Product Identified : {prod['product_name']}")
+                print(f"🏷️  SKU ID             : {prod['sku_id']}")
+                print(f"💰 Price              : ¥{prod['price']}")
+                print(f"📊 In Stock (On-Shelf): {prod['number_on']} units")
+                print(f"🚚 Received / Sold    : {prod['number_in']} in / {prod['number_out']} out")
+                print(f"📅 Expiration Date    : {prod['expiration_date']}")
+                print(f"🎯 Match Confidence   : {cls_info['confidence'] * 100:.1f}% (Score: {cls_info['score']})")
+                print("-" * 55)
+
+                # Update main window with selected box highlight (in red)
+                selected_display = pipeline.detector.draw_detections(
+                    base_image, boxes, selected_box_id=box_info["id"], draw_labels=False
+                )
+                cv2.imshow(window_name, selected_display)
+
+                # Show visual match comparison window if available
+                if res.get("match_visualization") is not None:
+                    match_win = "Feature Matcher (Shelf Crop vs Stock Template)"
+                    cv2.namedWindow(match_win, cv2.WINDOW_NORMAL)
+                    cv2.resizeWindow(match_win, 800, 400)
+                    cv2.imshow(match_win, res["match_visualization"])
+            else:
+                print(f"[Info] {res.get('message', 'No product found at coordinates.')}")
+
+    cv2.setMouseCallback(window_name, on_mouse)
+
+    while True:
+        cv2.imshow(window_name, display_image)
+        key = cv2.waitKey(20) & 0xFF
+        if key in [ord('q'), ord('Q'), 27]:
+            break
+
+    cv2.destroyAllWindows()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/recognization/code/identity.py b/recognization/code/identity.py
deleted file mode 100644
index 9b82499..0000000
--- a/recognization/code/identity.py
+++ /dev/null
@@ -1,318 +0,0 @@
-"""Identity ranking for catalog scores.
-
-Separates raw matcher scores from accept/unknown/ambiguous/reject so a
-zero-score crop cannot become a SKU. Three score scales are recognized:
-
-- `inlier`: Van's composite, kept as the selectable baseline lane (E-1).
-- `local`: `identity_lanes.LocalLane`'s margin-over-runner-up, in inlier units.
-- `cosine`: the embedding gallery, fenced off and not in the live path.
-
-The `local` line is measured on the corrected image6 labels: 2.0 keeps 95% of
-shown labels correct at 59% coverage with the text lane, 1.0 keeps 87% at 66%.
-It is tuned on one photo, so it is a direction, not a calibration; E-4 replaces
-it once Tier-2 held-out labels exist.
-"""
-
-from __future__ import annotations
-
-from typing import Any, Dict, List, Optional, Sequence, Tuple
-
-IDENTITY_ACCEPTED = "accepted"
-IDENTITY_AMBIGUOUS = "ambiguous"
-IDENTITY_UNKNOWN = "unknown"
-IDENTITY_REJECTED = "rejected"
-
-# ponytail: closed-set defaults for the baseline inlier scale. Recalibrate when
-# held-out scores exist.
-DEFAULT_MIN_SCORE = 1.0
-DEFAULT_MIN_MARGIN = 0.15
-DEFAULT_MIN_CONFIDENCE = 0.55
-
-# `local` scale (identity_lanes.LocalLane). The value is already a margin over
-# the runner-up, so the relative-margin test does not apply to it: a single
-# absolute line is the whole gate. Kept equal to identity_lanes.LOCAL_ACCEPT,
-# which is what the engine's DecisionPolicy uses; this copy exists for the
-# evaluator's `--no-text` path, and two different lines would score the same
-# crops two ways.
-LOCAL_ACCEPT = 4.0
-
-
-def relative_confidence(top_score: float, runner_up_score: float) -> float:
-    """Existing matcher formula: top / (top + 0.5 * runner)."""
-    return round(min(1.0, top_score / max(1.0, (top_score + runner_up_score * 0.5))), 4)
-
-
-# Cosine scale (gallery). Same-product pairs were measured above 0.6 and
-# different products below 0.5. These are not a new calibration.
-COSINE_ACCEPT = 0.60
-COSINE_AMBIGUOUS = 0.50
-COSINE_MARGIN = 0.04
-VOTE_CAP = 3
-
-
-def _finite(value: Any) -> Optional[float]:
-    try:
-        number = float(value)
-    except (TypeError, ValueError):
-        return None
-    if number != number or number in (float("inf"), float("-inf")):
-        return None
-    return number
-
-
-def aggregate_votes(
-    observations: Sequence[Dict[str, float]],
-    *,
-    cap: int = VOTE_CAP,
-) -> Dict[str, float]:
-    """Mean of the newest observations, at most `cap`.
-
-    Older frames are dropped, not averaged in. An empty observation still
-    counts, so a later miss can outvote an earlier weak hit. Inlier and
-    cosine scores must not be mixed in one list.
-    """
-    window = list(observations)[-max(1, int(cap)):]
-    if not window:
-        return {}
-    totals: Dict[str, float] = {}
-    for scores in window:
-        for sku_id, score in (scores or {}).items():
-            number = _finite(score)
-            if number is None:
-                continue
-            totals[str(sku_id)] = totals.get(str(sku_id), 0.0) + number
-    count = len(window)
-    return {sku_id: total / count for sku_id, total in totals.items()}
-
-
-def _cosine_decision(
-    sorted_candidates: List[Tuple[str, float]],
-    *,
-    method: str,
-    details: Optional[Dict[str, Any]],
-    accept: float,
-    ambiguous: float,
-    margin_min: float,
-) -> Dict[str, Any]:
-    top_sku, top_score = sorted_candidates[0]
-    runner_score = sorted_candidates[1][1] if len(sorted_candidates) > 1 else 0.0
-    margin = top_score - runner_score
-    if top_score >= accept and margin >= margin_min:
-        level = IDENTITY_ACCEPTED
-    elif top_score >= ambiguous:
-        level = IDENTITY_AMBIGUOUS
-    else:
-        level = IDENTITY_REJECTED
-    return {
-        "status": level,
-        "method": method,
-        "best_sku": top_sku if level == IDENTITY_ACCEPTED else None,
-        "provisional_sku": top_sku,
-        "score": float(top_score),
-        "confidence": float(top_score),
-        "identity_level": level,
-        "ranked_candidates": [
-            {"sku_id": sku, "score": float(value)} for sku, value in sorted_candidates[:3]
-        ],
-        "details": details or {},
-        "message": None,
-    }
-
-
-def decision_for_match(
-    match: Any,
-    *,
-    method: str = "gallery",
-    margin: float = COSINE_MARGIN,
-) -> Dict[str, Any]:
-    """Rank one gallery match. Rejection is the gallery's, not a second threshold.
-
-    `match()` already applied the raw 0.55 line or the head's 0.40 line. Re-ranking
-    those scores against COSINE_ACCEPT (0.60) would refuse a head match the gallery
-    accepted. The only extra gate here is the published cosine margin.
-    """
-    similarity = _finite(getattr(match, "similarity", 0.0)) or 0.0
-    if match is None or getattr(match, "rejected", False) or not getattr(match, "sku_id", None):
-        return {
-            "status": IDENTITY_REJECTED,
-            "method": method,
-            "best_sku": None,
-            "score": similarity,
-            "confidence": similarity,
-            "identity_level": IDENTITY_REJECTED,
-            "ranked_candidates": [],
-            "details": {},
-            "message": "no enrolled product is close enough",
-        }
-    runner = getattr(match, "runner_up", None)
-    runner_score = _finite(getattr(match, "runner_up_similarity", 0.0)) or 0.0
-    gap = similarity - runner_score
-    close = bool(runner) and gap < margin
-    level = IDENTITY_AMBIGUOUS if close else IDENTITY_ACCEPTED
-    ranked = [{"sku_id": str(match.sku_id), "score": similarity}]
-    if runner:
-        ranked.append({"sku_id": str(runner), "score": runner_score})
-    return {
-        "status": level,
-        "method": method,
-        "best_sku": None if close else str(match.sku_id),
-        "score": similarity,
-        "confidence": similarity,
-        "identity_level": level,
-        "ranked_candidates": ranked,
-        "details": {},
-        "message": None,
-    }
-
-
-def decide_identity(
-    scores: Dict[str, float],
-    *,
-    min_score: float = DEFAULT_MIN_SCORE,
-    min_margin: float = DEFAULT_MIN_MARGIN,
-    min_confidence: float = DEFAULT_MIN_CONFIDENCE,
-    details: Optional[Dict[str, Any]] = None,
-    method: str = "hybrid",
-    scale: str = "inlier",
-) -> Dict[str, Any]:
-    """Rank catalog scores into accepted / ambiguous / unknown / rejected.
-
-    Zero or missing evidence is rejected. A weak but unique top score is
-    unknown. Two close scores are ambiguous. Only a unique, above-threshold
-    score is accepted.
-    """
-    if scale == "cosine":
-        if min_score == DEFAULT_MIN_SCORE:
-            min_score = COSINE_ACCEPT
-        if min_margin == DEFAULT_MIN_MARGIN:
-            min_margin = COSINE_MARGIN
-        if min_confidence == DEFAULT_MIN_CONFIDENCE:
-            min_confidence = COSINE_AMBIGUOUS
-    elif scale == "local" and min_score == DEFAULT_MIN_SCORE:
-        min_score = LOCAL_ACCEPT
-    cleaned: Dict[str, float] = {}
-    for sku_id, score in (scores or {}).items():
-        number = _finite(score)
-        if number is None:
-            continue
-        cleaned[str(sku_id)] = number
-    if not cleaned:
-        return {
-            "status": IDENTITY_REJECTED,
-            "method": method,
-            "best_sku": None,
-            "provisional_sku": None,
-            "score": 0.0,
-            "confidence": 0.0,
-            "identity_level": IDENTITY_REJECTED,
-            "ranked_candidates": [],
-            "details": {},
-            "message": "No templates available",
-        }
-
-    sorted_candidates: List[Tuple[str, float]] = sorted(
-        cleaned.items(), key=lambda item: item[1], reverse=True
-    )
-    if scale == "cosine":
-        return _cosine_decision(
-            sorted_candidates,
-            method=method,
-            details=details,
-            accept=min_score,
-            ambiguous=min_confidence,
-            margin_min=min_margin,
-        )
-    top_sku, top_score = sorted_candidates[0]
-    runner_up_sku, runner_up_score = (
-        sorted_candidates[1] if len(sorted_candidates) > 1 else (None, 0.0)
-    )
-    confidence = relative_confidence(top_score, runner_up_score)
-    ranked = [{"sku_id": s, "score": round(sc, 2)} for s, sc in sorted_candidates[:5]]
-    top_details = (details or {}).get(top_sku, {})
-
-    if scale == "local":
-        # The score is already a margin over the runner-up, so the relative
-        # margin test below would apply the same correction twice. One absolute
-        # line is the whole gate; a candidate under it is unknown, not ambiguous,
-        # because the lane has already said which one it prefers.
-        if top_score <= 0.0:
-            level, message, best = IDENTITY_REJECTED, "No matching evidence", None
-        elif top_score < min_score:
-            level, message, best = IDENTITY_UNKNOWN, "Top score below accept threshold", None
-        else:
-            level, message, best = IDENTITY_ACCEPTED, "Accepted unique catalog match", top_sku
-        return {
-            "status": "success" if level == IDENTITY_ACCEPTED else level,
-            "method": method,
-            "best_sku": best,
-            "provisional_sku": top_sku if top_score > 0.0 else None,
-            "score": round(top_score, 2),
-            "confidence": round(top_score, 4),
-            "identity_level": level,
-            "ranked_candidates": ranked,
-            "details": top_details,
-            "message": message,
-        }
-
-    if scale == "van":
-        # Van's contract: the top SKU, always answered. No gate, no abstention -
-        # this is the baseline the decision is measured against, and it exists
-        # so the stream comparison is reproducible rather than asserted.
-        if top_score <= 0.0:
-            level, message, best = IDENTITY_REJECTED, "No matching evidence", None
-        else:
-            level, message, best = IDENTITY_ACCEPTED, "Always-argmax baseline", top_sku
-        return {
-            "status": "success" if level == IDENTITY_ACCEPTED else level,
-            "method": method,
-            "best_sku": best,
-            "provisional_sku": top_sku if top_score > 0.0 else None,
-            "score": round(top_score, 2),
-            "confidence": confidence,
-            "identity_level": level,
-            "ranked_candidates": ranked,
-            "details": top_details,
-            "message": message,
-        }
-
-    if top_score <= 0.0:
-        level = IDENTITY_REJECTED
-        message = "No matching evidence"
-        best = None
-    elif (
-        runner_up_sku is not None
-        and top_score > 0.0
-        and (top_score - runner_up_score) < min_margin * max(top_score, 1.0)
-    ):
-        level = IDENTITY_AMBIGUOUS
-        message = f"Close scores: {top_sku} vs {runner_up_sku}"
-        best = None
-    elif top_score < min_score or confidence < min_confidence:
-        level = IDENTITY_UNKNOWN
-        message = "Top score below accept threshold"
-        best = None
-    else:
-        level = IDENTITY_ACCEPTED
-        message = "Accepted unique catalog match"
-        best = top_sku
-
-    return {
-        "status": "success" if level == IDENTITY_ACCEPTED else level,
-        "method": method,
-        "best_sku": best,
-        "provisional_sku": top_sku if top_score > 0.0 else None,
-        "score": round(top_score, 2),
-        "confidence": confidence,
-        "identity_level": level,
-        "ranked_candidates": ranked,
-        "details": top_details,
-        "message": message,
-    }
-
-
-def identity_from_ranked(
-    ranked_candidates: Sequence[Dict[str, Any]],
-    **kwargs: Any,
-) -> Dict[str, Any]:
-    scores = {str(row["sku_id"]): float(row["score"]) for row in ranked_candidates}
-    return decide_identity(scores, **kwargs)
diff --git a/recognization/code/identity_lanes.py b/recognization/code/identity_lanes.py
deleted file mode 100644
index 54512c7..0000000
--- a/recognization/code/identity_lanes.py
+++ /dev/null
@@ -1,712 +0,0 @@
-"""Identity lanes and the decision that fuses them.
-
-Two lanes answer "which catalog row is this crop", and one policy turns their
-evidence into accepted / ambiguous / unknown / rejected:
-
-- `LocalLane` reads printed geometry (RootSIFT over a reference pyramid,
-  similarity-constrained RANSAC, a hue check). It is the only primitive that
-  ranks fine-grained siblings correctly: 40/42 enrolled facings first at full
-  resolution, against 32/42 for Van's composite.
-- `TextLane` reads the package text and compares it to each catalog row's
-  printed name. It is the only measured mechanism that rejects a near-unknown
-  sibling, and it can name catalog rows that have no enrolled image at all.
-
-Why a policy and not a threshold: the matcher's raw score is not comparable
-across references of different resolution, and a single unverified match used
-to clear the old floor. Measured on the corrected image6 labels, the old gate
-accepts 22-28 of 48 unknowns; the local gate alone reaches 79% precision at 43%
-coverage, and adding the text lane reaches 95% precision at 59% coverage.
-
-`ponytail:` the policy is a hand-set pair of lines tuned on one photo, not a
-calibrated model. E-4 replaces it once Tier-2 held-out labels exist.
-"""
-
-from __future__ import annotations
-
-import math
-import os
-import re
-import sys
-from dataclasses import dataclass
-from typing import Any, Dict, List, Optional, Sequence, Set, Tuple
-
-current_dir = os.path.dirname(os.path.abspath(__file__))
-if current_dir not in sys.path:
-    sys.path.insert(0, current_dir)
-
-import cv2  # noqa: E402
-import numpy as np  # noqa: E402
-
-from rebuild_catalog import bigrams, normalize_text, signature_bigrams, sku_of  # noqa: E402
-
-# The local lane's accept line, in the units of `confidence()` below. Measured
-# on the corrected image6 labels: 2.0 keeps 95% of shown labels correct at 59%
-# coverage, 1.0 keeps 87% at 66%. The stricter line is the default because a
-# wrong label on a shelf is worse than a missing one.
-#
-# 4.0 since 2026-09-29: the line was tuned on image6 alone, where every crop is
-# a facing of a product the shelf actually holds, so that set could not price
-# it. Re-measured with `code/sweep_accept_lines.py` on 4580 crops from 64
-# unrelated Wikimedia shelf photos - 1280-long-side frames at
-# `LiveSettings.conf`, the configuration the phone runs - against
-# `TEXT_MIN_SHARED = 4`: 2.0 accepts 60 of them (1.31%) and 4.0 accepts 3
-# (0.07%), against G3's 1%. On image6 at full resolution 4.0 shows 44 labels of
-# which 42 are right - 95.5% precision, and 65.6% of the 64 facings that hold a
-# catalog row - and it is the highest-coverage cell in the sweep that passes
-# both G2 and G3.
-LOCAL_ACCEPT = 4.0
-# The text lane's own accept line: a bigram overlap this high, with this much
-# margin over the runner-up, names the row on its own. Measured: the text lane
-# picks the right SKU on 41-42 of the 55 facings where it reads anything.
-TEXT_ACCEPT = 0.5
-TEXT_MARGIN = 0.15
-# How many of the read's bigrams the winning row must actually contain before
-# that read may name a product. A floor on the amount of shared evidence, not
-# on the ratio: a two-pair read the row happens to contain scores 1.0, and
-# "サソース" named sku_l08 that way.
-#
-# It gates the text lane's *answer* and nothing else. Zeroing the row's score
-# would be a different policy under the same name: it reorders the ranking, so
-# a zeroed top row promotes a runner-up and the margin gate ends up comparing a
-# different pair than the one it was measured on. It would also gate the veto,
-# which is backwards - the veto only ever refuses a local accept, so a floor
-# under it would let a two-bigram read rescue a match the local lane earned.
-# `code/sweep_accept_lines.py` measures the line by calling the policy below
-# with this value, so the sweep and the shipped lane cannot disagree about what
-# it means.
-#
-# The ratio stays the ranking's own gate, so a long read of shared brand copy
-# still scores low and is still refused by the margin.
-TEXT_MIN_SHARED = 4
-# Reference pyramid heights. A ~1000 px packshot against a 110-250 px crop is a
-# 4-8x ratio, and ORB's 8x1.2 pyramid tops out at 3.6x, which is why ORB and
-# BRISK find zero inliers at live resolution. Scaling each reference to the
-# crop's own size removes the ratio instead of widening the pyramid.
-REFERENCE_HEIGHTS = (200, 400)
-# Lowe's ratio test.
-RATIO_THRESH = 0.75
-# RANSAC reprojection tolerance, as a fraction of the reference's long side.
-REPROJ_FRACTION = 0.02
-# Hue histogram: chromatic pixels only, so a white background cannot vote.
-HUE_BINS = 18
-HUE_SATURATION_MIN = 60
-HUE_VALUE_MIN = 50
-# Quantity tokens: a number with its unit, in either script. Cut whole, because
-# the unit's own letters ("g", "ml", "%") are what survived a digits-only rule.
-_QUANTITY = re.compile(r"\d+(?:\.\d+)?\s*(?:KG|G|ML|L|%|個|本|枚|入|袋|缶|パック)")
-# A package's own size, in mass units. Only mass can contradict a package size:
-# a percentage ("カロリー 50% カット") is a claim about the contents, not the
-# package, and counting it made every low-calorie crop look like a 50 g jar.
-_MASS = re.compile(r"(\d+(?:\.\d+)?)\s*(KG|G)", re.IGNORECASE)
-# The range a retail package of this class falls in. Below it are fragments of a
-# longer number ("20" read out of "200g"); above it are two numbers run together
-# ("5210" out of "210g"). Both are OCR noise, not a different package, and
-# either would veto a correct answer if taken at face value.
-PACKAGE_MASS_RANGE = (100.0, 2000.0)
-# How many surviving bigrams a read needs before it may name a product.
-IDENTITY_BIGRAM_FLOOR = 3
-
-
-@dataclass
-class Evidence:
-    """What one reference view says about one crop."""
-
-    inliers: int = 0
-    cover: float = 0.0   # inlier hull area / reference area
-    qcover: float = 0.0  # inlier hull area / crop area
-    color: float = 0.0   # hue histogram intersection
-
-
-def _hue_histogram(image: Any) -> np.ndarray:
-    """Hue distribution over the chromatic pixels, L1-normalized."""
-    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
-    chromatic = ((hsv[..., 1] > HUE_SATURATION_MIN) & (hsv[..., 2] > HUE_VALUE_MIN))
-    hist = cv2.calcHist([hsv], [0], chromatic.astype(np.uint8), [HUE_BINS], [0, 180]).ravel()
-    return hist / (hist.sum() + 1e-6)
-
-
-def _root_sift(descriptors: Optional[np.ndarray]) -> Optional[np.ndarray]:
-    """RootSIFT: L1-normalize then square-root. Cheap, and it beats raw SIFT."""
-    if descriptors is None:
-        return None
-    normalized = descriptors / (np.abs(descriptors).sum(1, keepdims=True) + 1e-7)
-    return np.sqrt(normalized).astype(np.float32)
-
-
-class LocalLane:
-    """RootSIFT over a reference pyramid, similarity-RANSAC, hue re-rank.
-
-    Scores every enrolled reference view and reports the best view per SKU, so
-    a multi-view catalog cannot make one product compete with itself.
-    """
-
-    name = "local"
-
-    def __init__(
-        self,
-        stock_dir: str,
-        *,
-        heights: Sequence[int] = REFERENCE_HEIGHTS,
-        nfeatures: int = 2000,
-    ) -> None:
-        self.stock_dir = stock_dir
-        self.sift = cv2.SIFT_create(nfeatures=nfeatures)
-        self.matcher = cv2.BFMatcher(cv2.NORM_L2)
-        self.views: List[Dict[str, Any]] = []
-        self.sku_ids: List[str] = []
-        self._load(heights)
-
-    def _load(self, heights: Sequence[int]) -> None:
-        if not os.path.exists(self.stock_dir):
-            return
-        names = sorted(
-            name for name in os.listdir(self.stock_dir)
-            if name.lower().endswith((".jpg", ".jpeg", ".png"))
-        )
-        for name in names:
-            image = cv2.imread(os.path.join(self.stock_dir, name))
-            if image is None:
-                continue
-            sku = sku_of(name)
-            if sku not in self.sku_ids:
-                self.sku_ids.append(sku)
-            for height in heights:
-                if height > image.shape[0]:
-                    # Never upscale a reference. A 305 px view blown up to 400
-                    # is a blur, and a blur matches everything: the l09 shelf
-                    # crop won 31 of 54 crops on the shelf it was cut from, at
-                    # a median margin of 4.9, because its upscaled level had no
-                    # high-frequency detail to disagree with. A view is used at
-                    # the levels it actually has.
-                    continue
-                scaled = cv2.resize(
-                    image,
-                    (max(1, int(image.shape[1] * height / image.shape[0])), int(height)),
-                    interpolation=cv2.INTER_AREA,
-                )
-                gray = cv2.cvtColor(scaled, cv2.COLOR_BGR2GRAY)
-                keypoints, descriptors = self.sift.detectAndCompute(gray, None)
-                self.views.append(
-                    {
-                        "sku": sku,
-                        "keypoints": keypoints,
-                        "descriptors": _root_sift(descriptors),
-                        "shape": gray.shape,
-                        "hist": _hue_histogram(scaled),
-                    }
-                )
-
-    def view_count(self) -> int:
-        return len(self.views)
-
-    def evidence(self, crop: Any) -> Dict[str, Evidence]:
-        """Best evidence per canonical SKU. Empty when the crop has no features."""
-        if crop is None or getattr(crop, "size", 0) == 0:
-            return {}
-        gray = cv2.cvtColor(crop, cv2.COLOR_BGR2GRAY)
-        keypoints, descriptors = self.sift.detectAndCompute(gray, None)
-        descriptors = _root_sift(descriptors)
-        if descriptors is None or len(descriptors) < 2:
-            return {}
-        query_hist = _hue_histogram(crop)
-        crop_area = float(gray.shape[0] * gray.shape[1])
-
-        best: Dict[str, Evidence] = {}
-        for view in self.views:
-            reference = view["descriptors"]
-            if reference is None or len(reference) < 2:
-                continue
-            pairs = self.matcher.knnMatch(descriptors, reference, k=2)
-            good = [
-                first for first, second in (pair for pair in pairs if len(pair) == 2)
-                if first.distance < RATIO_THRESH * second.distance
-            ]
-            if len(good) < 4:
-                continue
-            source = np.float32([keypoints[m.queryIdx].pt for m in good])
-            target = np.float32([view["keypoints"][m.trainIdx].pt for m in good])
-            _, mask = cv2.estimateAffinePartial2D(
-                source, target, method=cv2.RANSAC,
-                ransacReprojThreshold=REPROJ_FRACTION * max(view["shape"]),
-            )
-            if mask is None:
-                continue
-            inliers = mask.ravel().astype(bool)
-            count = int(inliers.sum())
-            if count < 3:
-                continue
-            reference_area = float(view["shape"][0] * view["shape"][1])
-            evidence = Evidence(
-                inliers=count,
-                cover=float(cv2.contourArea(cv2.convexHull(target[inliers])) / reference_area),
-                qcover=float(cv2.contourArea(cv2.convexHull(source[inliers])) / crop_area),
-                color=float(np.minimum(query_hist, view["hist"]).sum()),
-            )
-            current = best.get(view["sku"])
-            if current is None or evidence.inliers > current.inliers:
-                best[view["sku"]] = evidence
-        return best
-
-    @staticmethod
-    def rank_key(evidence: Evidence) -> float:
-        """Order candidates. The hue term is what separates colour-coded variants
-        (pink ゼロ from red ハーフ), which grayscale SIFT cannot see at all."""
-        return evidence.inliers * (0.25 + evidence.color)
-
-    @staticmethod
-    def confidence(evidence: Evidence, runner_up: Evidence) -> float:
-        """How much this crop's best candidate stands out, in inlier units.
-
-        The runner-up is subtracted in inlier space, so a sibling that matches
-        the same artwork cannot be separated by a high score alone; the hue
-        agreement then scales the result, because a match that disagrees on
-        colour is not the same package.
-        """
-        margin = 1.0 - runner_up.inliers / max(evidence.inliers, 1)
-        return evidence.inliers * margin * evidence.color
-
-    def rank(self, crop: Any) -> List[Tuple[str, float]]:
-        """Candidates best-first, each with the confidence the gate reads.
-
-        Order and value are different quantities on purpose: the order comes
-        from `rank_key` (inliers weighted by hue), the value from `confidence`
-        (the margin over the runner-up). Sorting by the value would reorder the
-        candidates, which is how a lane that ranks 40/42 correctly ends up
-        gating on the wrong one.
-        """
-        evidence = self.evidence(crop)
-        if not evidence:
-            return []
-        ranked = sorted(evidence.items(), key=lambda kv: self.rank_key(kv[1]), reverse=True)
-        return [
-            (
-                sku,
-                self.confidence(
-                    item,
-                    ranked[index + 1][1] if index + 1 < len(ranked) else Evidence(),
-                ),
-            )
-            for index, (sku, item) in enumerate(ranked)
-        ]
-
-    def scores(self, crop: Any) -> Dict[str, float]:
-        """`rank()` as a dict, in rank order. Convenience for callers that only
-        need the values; the policy uses `rank()` so the order survives."""
-        return {sku: value for sku, value in self.rank(crop)}
-
-
-# Text normalization and bigrams come from `rebuild_catalog`, which uses them
-# to match a catalog name against a packshot's OCR. Two copies of the same
-# folding rules drift: the lane's copy folded ユ to itself while the catalog's
-# did not, so the same read scored differently depending on which one ran.
-
-
-def identity_bigrams(read: str) -> set:
-    """Bigrams carrying a non-digit character, after quantity tokens are cut.
-
-    A crop reading "400g" matches every 400g product's size line, so size and
-    number tokens must not name a product on their own. Cutting the whole
-    quantity token - not just the digits - is what makes that true: "400g 50%"
-    leaves nothing behind, where counting non-digit bigrams alone left "0G" and
-    "0%" and let the read through. They still count for the veto, where the
-    local lane has already supplied the evidence and the text only has to
-    contradict it.
-    """
-    return {gram for gram in bigrams(_QUANTITY.sub(" ", normalize_text(read))) if not gram.isdigit()}
-
-
-def carries_identity(read: str) -> bool:
-    """Whether a read holds enough name text to answer for a product.
-
-    Two bigrams is not enough: "カロリ" is two, and it is a descriptor that any
-    low-calorie package prints, not a name. Three is the floor - four
-    characters of surviving text - which keeps "コクウマ" (from "コクうま 65%")
-    and refuses "カロリ", "ハーフ" and every pure quantity string. The veto is
-    unaffected: there the local lane has already supplied the evidence and the
-    """
-    return len(identity_bigrams(read)) >= IDENTITY_BIGRAM_FLOOR
-
-
-def text_similarity(
-    read: str,
-    signature: str,
-    weights: Optional[Dict[str, float]] = None,
-    vocabulary: Optional[Set[str]] = None,
-) -> float:
-    """Share of the read text's bigrams that the signature explains.
-
-    Asymmetric on purpose: the crop shows a few words of a package, the
-    signature holds the whole name, so the question is how much of what was
-    read the catalog row accounts for.
-
-    `weights` is the inverse document frequency of each bigram over the
-    catalog's signatures. Without it, brand copy that every sibling prints
-    ("ピュアセレクト", "マヨネーズ", "国産新鮮たまご") counts as much as the words
-    that tell the products apart ("コクうま", "カロリーカット", "65"), so a row
-    whose signature happens to carry more shared copy outranks the row the
-    crop actually shows. Measured on image6: every l10 facing reads
-    "ピュア セレクト マヨネーズ" with no "コクうま" and no "65", yet l09 tied or
-    won on shared brand text alone.
-
-    `vocabulary` is every bigram any signature can produce. A read's pairs
-    that no catalog name contains are OCR noise - a misread character, a
-    date, or a pair that spans two words the package prints separately
-    ("セレクト マヨネーズ" yields "トマ"). Counting them in the denominator
-    charges the row for text it could never have explained, which is how a
-    correct read of l10 fell to 0.46 while a garbage read of l11 stayed at
-    0.24: the noise is proportional to how much was read, so the rows with
-    the most real text to match were punished hardest. Bounded this way the
-    same reads score 0.86 and 1.0 against their true rows, and the garbage
-    read ties across three rows and is refused by the margin.
-
-    `vocabulary` defaults to the weight table's own keys, which are by
-    construction the bigrams some signature produces. A second copy of that set
-    on the lane is a second thing to keep in step, and it is the drift the
-    module already paid for once with the folding rules.
-    """
-    read_grams = bigrams(read)
-    if vocabulary is None and weights is not None:
-        vocabulary = set(weights)
-    if vocabulary is not None:
-        read_grams &= vocabulary
-    if not read_grams:
-        return 0.0
-    shared = read_grams & signature_bigrams(signature)
-    if not shared:
-        return 0.0
-    if weights is None:
-        return len(shared) / max(1, len(read_grams))
-    total = sum(weights.get(gram, 1.0) for gram in read_grams)
-    if total <= 0:
-        return 0.0
-    return sum(weights.get(gram, 1.0) for gram in shared) / total
-
-
-def bigram_weights(signatures: Sequence[str]) -> Dict[str, float]:
-    """Inverse document frequency of each bigram over the catalog signatures.
-
-    A bigram every row prints carries no information about which row this is,
-    so it is worth ~0; a bigram one row prints is worth the most. Smoothed and
-    floored at 0.05 so a shared word still counts a little and a read made
-    entirely of shared copy scores low rather than dividing by zero.
-    """
-    counts: Dict[str, int] = {}
-    for signature in signatures:
-        for gram in set(signature_bigrams(signature)):
-            counts[gram] = counts.get(gram, 0) + 1
-    total = max(1, len(signatures))
-    return {
-        gram: max(0.05, math.log((total + 1) / (count + 1)))
-        for gram, count in counts.items()
-    }
-
-
-class TextLane:
-    """Package text against each catalog row's printed name.
-
-    The signature is the row's name, so it is built from the catalog and never
-    from the evaluation photo. Rows with no image still get a signature, which
-    is how the lane can name a SKU no matcher can see.
-    """
-
-    name = "text"
-
-    def __init__(
-        self,
-        signatures: Dict[str, str],
-        ocr_engine: Any = None,
-        lock: Any = None,
-    ) -> None:
-        self.signatures = {str(sku): text for sku, text in signatures.items()}
-        # Bigram weights over the catalog's own names, so the lane can tell
-        # copy every sibling prints from the words that name one product.
-        self.weights = bigram_weights(list(self.signatures.values()))
-        self.ocr_engine = ocr_engine
-        # The engine's OCR lock. Vision tolerates concurrency but the CPU does
-        # not, and this lane runs one OCR per crop on the classify pool, so it
-        # shares the same lock as tag reading instead of adding a second one.
-        self.lock = lock
-
-    @classmethod
-    def from_catalog(
-        cls,
-        catalog_rows: Sequence[Dict[str, Any]],
-        stock_dir: str,
-        ocr_engine: Any = None,
-        lock: Any = None,
-    ) -> "TextLane":
-        signatures: Dict[str, str] = {}
-        for row in catalog_rows:
-            sku = str(row.get("sku_id") or "")
-            if not sku:
-                continue
-            signatures[sku] = str(row.get("product_name") or "")
-        return cls(signatures, ocr_engine=ocr_engine, lock=lock)
-
-    def read(self, crop: Any) -> str:
-        """The text on one crop, upsampled when the crop is small.
-
-        Below ~200 px tall the recognizer misses the small variant words that
-        are the whole point of this lane, so the crop is doubled first.
-        """
-        if self.ocr_engine is None or crop is None or getattr(crop, "size", 0) == 0:
-            return ""
-        if not getattr(self.ocr_engine, "available", lambda: False)():
-            return ""
-        image = crop
-        if image.shape[0] < 200:
-            image = cv2.resize(image, None, fx=2.0, fy=2.0, interpolation=cv2.INTER_CUBIC)
-        try:
-            if self.lock is not None:
-                with self.lock:
-                    result = self.ocr_engine.read(image)
-            else:
-                result = self.ocr_engine.read(image)
-        except Exception:
-            return ""
-        return " ".join(line.text for line in result.lines)
-
-    def scores(self, crop: Any) -> Dict[str, float]:
-        """Bigram similarity per catalog row. Empty when nothing was read."""
-        return self.scores_from_text(self.read(crop))
-
-    def scores_from_text(self, read: str) -> Dict[str, float]:
-        """Bigram similarity per catalog row, against the row's name.
-
-        The name only. Scoring against the packshot's own OCR as well is how
-        l09 used to win l10's facings: l09's packshot prints "マヨネーズ",
-        which is l10's actual name, so a row with a photographed packshot
-        could explain a sibling's text better than the sibling's own name
-        could. Every row in the catalog has a name, so the packshot adds
-        nothing but that confusion.
-        """
-        if len(normalize_text(read)) < 4:
-            return {}
-        return {
-            sku: text_similarity(read, signature, self.weights)
-            for sku, signature in self.signatures.items()
-        }
-
-
-def _ranked(scores: Any) -> List[Tuple[str, float]]:
-    """Candidates best-first.
-
-    A lane hands over an already-ordered sequence and it is used as given: the
-    order is the lane's ranking, and re-sorting by the confidence value would
-    silently replace it. A plain dict is sorted by value, which is the only
-    order a dict has.
-    """
-    if scores is None:
-        return []
-    if isinstance(scores, dict):
-        items = scores.items()
-        return sorted(
-            ((str(sku), float(value)) for sku, value in items),
-            key=lambda kv: kv[1],
-            reverse=True,
-        )
-    return [(str(sku), float(value)) for sku, value in scores]
-
-
-class DecisionPolicy:
-    """Turn two lanes' evidence into one identity decision.
-
-    The local lane answers first, because it is the only lane that ranks
-    siblings correctly. The text lane then does one of two jobs: it vetoes a
-    local answer whose package text names a different catalog row, and it
-    answers on its own when the local lane has nothing. Both jobs are measured:
-    the veto takes precision from 62% to 82% for one lost correct label, and
-    the lane adds ten correct labels at live resolution, including SKUs with no
-    enrolled image.
-    """
-
-    def __init__(
-        self,
-        local_accept: float = LOCAL_ACCEPT,
-        text_accept: float = TEXT_ACCEPT,
-        text_margin: float = TEXT_MARGIN,
-        text_min_shared: int = TEXT_MIN_SHARED,
-        signatures: Optional[Dict[str, str]] = None,
-        method: str = "rootsift",
-    ) -> None:
-        self.local_accept = float(local_accept)
-        self.text_accept = float(text_accept)
-        self.text_margin = float(text_margin)
-        self.text_min_shared = int(text_min_shared)
-        # The catalog names, so the evidence floor can ask how many of the
-        # read's pairs the winning row actually contains. Without them the
-        # floor cannot be applied and the ratio stands alone, which is the
-        # behaviour a caller that has no catalog is asking for.
-        self.signatures = {str(sku): str(text) for sku, text in (signatures or {}).items()}
-        self.method = method
-
-    def _shared_evidence(self, read: str, sku: str) -> int:
-        """How many of the read's bigrams the row's name contains."""
-        signature = self.signatures.get(sku)
-        if signature is None:
-            return self.text_min_shared
-        return len(bigrams(read) & signature_bigrams(signature))
-
-    def _contradicts_size(self, read: str, sku: str) -> bool:
-        """Does the read print a package size the row's name does not have?
-
-        The near-unknown case the text ranking cannot catch. A crop of a
-        Kewpie 450 g bag reads "マヨネーズ 450g", and every mayonnaise row in
-        the catalog explains "マヨネーズ" equally well, so the ranking ties at
-        1.0 and the strict-greater veto above never fires - the local lane's
-        answer stands. The size is the part that differs, and it is printed
-        plainly, so it is the part worth checking.
-
-        Only a mass in `PACKAGE_MASS_RANGE` counts. Outside it the read is
-        noise, not a package: "20" is a fragment of "200g" and "5210" is two
-        numbers run together, and vetoing on either would cost correct
-        answers. Measured on image6, this vetoes 3 false accepts at full
-        resolution and 1 at live, and no correct answer at either.
-        """
-        signature = self.signatures.get(sku)
-        if signature is None:
-            return False
-        read_masses = {
-            float(number) for number, _unit in _MASS.findall(normalize_text(read))
-        }
-        plausible = {
-            mass for mass in read_masses
-            if PACKAGE_MASS_RANGE[0] <= mass <= PACKAGE_MASS_RANGE[1]
-        }
-        if not plausible:
-            return False
-        name_masses = {
-            float(number) for number, _unit in _MASS.findall(normalize_text(signature))
-        }
-        return not (read_masses & name_masses)
-
-
-    def decide(
-        self,
-        local_rank: Any,
-        text_rank: Any = None,
-        *,
-        details: Optional[Dict[str, Any]] = None,
-        text_read: Optional[str] = None,
-    ) -> Dict[str, Any]:
-        """One decision, in the shape `decide_identity` already returns.
-
-        `text_read` is the raw OCR text behind `text_rank`. It gates the
-        fill-in job only: a read that is nothing but a size or a number cannot
-        name a product, but it can still veto one. Omitting it means no
-        fill-in - a caller that has a ranking and no read has not shown the
-        text can name anything, so the safe reading is the default one.
-        """
-        from identity import (
-            IDENTITY_ACCEPTED,
-            IDENTITY_AMBIGUOUS,
-            IDENTITY_REJECTED,
-            IDENTITY_UNKNOWN,
-        )
-
-        local = _ranked(local_rank)
-        text = _ranked(text_rank)
-        text_top = text[0] if text else None
-        text_runner = text[1][1] if len(text) > 1 else 0.0
-        text_names = (
-            text_top[0]
-            if text_top
-            and text_top[1] >= self.text_accept
-            and (text_top[1] - text_runner) >= self.text_margin
-            and carries_identity(text_read or "")
-            and self._shared_evidence(text_read or "", text_top[0]) >= self.text_min_shared
-            # A row the read contradicts on size cannot be named by that read
-            # either. Without this the veto above only moves the answer: the
-            # local lane's row is blocked and the text lane then names the same
-            # row, because "マヨネーズ" is what both of them matched.
-            and not self._contradicts_size(text_read or "", text_top[0])
-            else None
-        )
-
-        answer: Optional[str] = None
-        source: Optional[str] = None
-        if local and local[0][1] >= self.local_accept:
-            candidate = local[0][0]
-            # A local match whose package text names a different row is the
-            # near-unknown case no threshold can reject: the sibling shares the
-            # artwork, so the geometry is real and only the words differ.
-            if text_top and text_top[0] != candidate and text_top[1] > local_text_floor(text, candidate):
-                answer = None
-            elif self._contradicts_size(text_read or "", candidate):
-                answer = None
-            else:
-                answer, source = candidate, "local"
-        if answer is None and text_names:
-            answer, source = text_names, "text"
-
-        ranked = [
-            {"sku_id": sku, "score": round(value, 2)} for sku, value in local[:5]
-        ]
-        if not ranked and text:
-            ranked = [{"sku_id": sku, "score": round(value, 3)} for sku, value in text[:5]]
-
-        if answer is not None:
-            level = IDENTITY_ACCEPTED
-            message = "Accepted unique catalog match"
-        elif local and local[0][1] > 0.0:
-            level = IDENTITY_AMBIGUOUS if len(local) > 1 else IDENTITY_UNKNOWN
-            message = "No candidate cleared the accept line"
-        elif text:
-            level = IDENTITY_UNKNOWN
-            message = "Text read, but it does not name one catalog row"
-        else:
-            level = IDENTITY_REJECTED
-            message = "No matching evidence"
-
-        # Coverage-first: the row the evidence supports well enough to *show*,
-        # even when it does not clear the accept line. `None` means the crop
-        # carried no usable evidence at all, which is the only case that still
-        # ends blank - a size-only read has no identity text and stays None.
-        if answer is not None:
-            provisional = answer
-        elif local and local[0][1] > 0.0:
-            # A vetoed local answer is still the best geometric guess, unless
-            # the text named a different row - then that row is the better one.
-            vetoed = bool(
-                text_top
-                and text_top[0] != local[0][0]
-                and text_top[1] > local_text_floor(text, local[0][0])
-            )
-            provisional = text_top[0] if vetoed else local[0][0]
-        elif (
-            text
-            and carries_identity(text_read or "")
-            and self._shared_evidence(text_read or "", text[0][0]) >= self.text_min_shared
-        ):
-            provisional = text[0][0]
-        else:
-            provisional = None
-
-        top_score = local[0][1] if local else (text_top[1] if text_top else 0.0)
-        return {
-            "status": "success" if level == IDENTITY_ACCEPTED else level,
-            "method": self.method,
-            "best_sku": answer,
-            "provisional_sku": provisional,
-            "score": round(float(top_score), 2),
-            "confidence": round(float(top_score), 4),
-            "identity_level": level,
-            "ranked_candidates": ranked,
-            "details": (details or {}).get(answer, {}) if answer else {},
-            "message": message,
-            "source": source,
-        }
-
-
-def local_text_floor(text: List[Tuple[str, float]], candidate: str) -> float:
-    """The text score the local candidate itself explains.
-
-    A veto needs the text to *prefer* another row, not merely to mention it: a
-    crop that reads "マヨネーズ" scores every mayonnaise row, and the local
-    candidate is usually one of them. Only a row the text explains better than
-    the local answer's own row is a contradiction.
-    """
-    for sku, value in text:
-        if sku == candidate:
-            return value
-    return 0.0
diff --git a/recognization/code/live.py b/recognization/code/live.py
deleted file mode 100644
index c8ba065..0000000
--- a/recognization/code/live.py
+++ /dev/null
@@ -1,1755 +0,0 @@
-"""
-Live recognition session: one per streaming client.
-
-Per frame it runs detection and tracking, then schedules classification of each
-physical product ONCE in a background pool and remembers the result on the
-track. Labels therefore fill in as fast as the hardware allows while detection
-keeps the frame rate.
-
-Pure Python: no FastAPI imports, no globals. Everything a session owns is behind
-one lock, and the heavy per-frame work (decode/detect/track) runs outside that
-lock so classification callbacks can land concurrently.
-"""
-
-from __future__ import annotations
-
-import asyncio
-import collections
-import os
-import statistics
-import sys
-import logging
-import threading
-import time
-from concurrent.futures import Future, ThreadPoolExecutor
-from dataclasses import dataclass, field
-LOGGER = logging.getLogger("dorayaki.live")
-
-from typing import Any, Dict, List, Optional, Sequence, Set, Tuple
-
-current_dir = os.path.dirname(os.path.abspath(__file__))
-if current_dir not in sys.path:
-    sys.path.insert(0, current_dir)
-
-from engine import (  # noqa: E402
-    STATE_DETECTED,
-    STATE_IDENTIFYING,
-    STATE_NEEDS_CONFIRMATION,
-    STATE_RECOGNIZED,
-    STATE_UNRECOGNIZED,
-    RecognitionEngine,
-)
-from identity_lanes import carries_identity  # noqa: E402
-from tracking import SessionTracker  # noqa: E402
-
-EMPTY_SLOT_HISTORY = 5
-EMPTY_SLOT_MIN_HITS = 3
-EMPTY_SLOT_IOU = 0.3
-# Label memory: a recognized product keeps its label for any track that covers
-# the same place soon after, so panning does not re-classify one product many
-# times just because the tracker re-issued its id.
-LABEL_MEMORY_IOU = 0.55
-LABEL_MEMORY_FRAMES = 45
-LABEL_MEMORY_MAX = 200
-STATS_WINDOW = 30
-# A recognized label is only trustworthy while the box still covers the product
-# it was measured on. Below this IoU against the box at label time, the track has
-# slid onto a different product (typical when the camera pans) and must be
-# re-classified rather than keep a stale label. 0.5 means more than half the box
-# still overlaps the labelled product; a neighbour-slide crosses it.
-DRIFT_IOU = 0.5
-STATE_UNCONFIRMED = "unconfirmed"
-# Which evidence produced the newest observation on a track. A label is only ever
-# shown for an accepted match, so this says how that match was measured.
-SOURCE_FRAME = "frame"
-SOURCE_CROP = "crop"
-SOURCE_INHERITED = "inherited"
-# A crop the client returns is base64 inside a text control message, so it is
-# bounded on its own rather than riding the binary frame path.
-MAX_CROP_BYTES = 1024 * 1024
-# Frame faults a full-resolution crop cannot undo. Blur and glare are optical
-# faults of the whole capture, so the phone would photograph the same bad
-# scene. "too_far" is exempt on purpose: a distant shelf is the one frame a
-# native-resolution crop exists to decide.
-CROP_BLIND_QUALITY = ("blur", "glare")
-
-
-@dataclass
-class LiveSettings:
-    method: str = "hybrid"
-    conf: float = 0.1
-    empty_slots: bool = True
-    tags: bool = True
-    # The text lane: OCR every classified crop and let the reading veto or name
-    # a row. It is the slowest lane (5-25 ms per crop, serialized behind one
-    # lock), so a client may turn it off and run on geometry alone.
-    ocr: bool = True
-    min_hits: int = 2
-    min_crop_px: int = 40
-    max_attempts: int = 2
-    retry_gap: int = 8
-    # A product drifting out of the frame is not worth a retry: the budget belongs
-    # to what is still in front of the camera.
-    retry_edge_ratio: float = 0.06
-    area_growth: float = 1.3
-    max_inflight_per_session: int = 16
-    edge_margin_ratio: float = 0.015
-    glare_fraction: float = 0.02
-    glare_pixel: int = 245
-    too_far_height_ratio: float = 0.08
-    # Full-resolution crop lane. Off until a client advertises it, so a
-    # protocol-v1 client is never asked for a crop and its traffic is unchanged.
-    crops: bool = False
-    max_crop_bytes: int = 128 * 1024
-    # A crop answered this many processed frames late describes a product that
-    # may have moved on, so it is dropped instead of relabelling the track.
-    crop_ttl_frames: int = 6
-    # Requests outstanding at once. Bounds what one phone can owe us.
-    max_crop_inflight: int = 4
-    # Ask for a native-resolution crop while the box is still this small. At the
-    # camera's own resolution it may still be large enough to classify.
-    crop_below_px: int = 64
-
-
-@dataclass
-class TrackState:
-    track_id: int
-    first_seq: int
-    last_seq: int
-    hits: int = 1
-    box: List[int] = field(default_factory=list)
-    raw_box: List[int] = field(default_factory=list)
-    confidence: float = 0.0
-    state: str = STATE_DETECTED
-    attempts: int = 0
-    in_flight: int = 0
-    last_attempt_seq: int = 0
-    last_attempt_area: int = 0
-    score_sums: Dict[str, float] = field(default_factory=dict)
-    score_n: int = 0
-    observations: List[Dict[str, float]] = field(default_factory=list)
-    candidates: List[Dict[str, Any]] = field(default_factory=list)
-    identity: Optional[Dict[str, Any]] = None
-    confirmed: bool = False
-    label_box: List[int] = field(default_factory=list)
-    drift_frames: int = 0
-    # Bumped every time the tracker starts this id over. A classification that
-    # finishes after the id was recycled belongs to the old object and must not
-    # label the new one.
-    object_generation: int = 0
-    # How many times a settled label was dropped and re-verified (drift or id
-    # recycle). A label change without a relabel would be a defect; the counter
-    # makes that auditable from the result payload.
-    relabels: int = 0
-    # Monotonic: how many times this id was started over (absent then re-detected,
-    # or pruned then re-created). Restarting resets the label, so it is always a
-    # re-verification rather than an unexplained label change.
-    restarts: int = 0
-    # How many full-resolution crops have been asked for on this object. It is
-    # the budget the frame path spends on attempts, so a phone that never
-    # answers cannot hold a small facing in the lane for ever. A recycled id
-    # gets a fresh TrackState, so a new object starts with a fresh budget.
-    crop_attempts: int = 0
-    # Which evidence produced the newest observation: frame or crop.
-    last_source: str = SOURCE_FRAME
-    # The newest text reading for this object, cached so OCR runs once per
-    # track rather than once per attempt. Vision takes a crop from 5 ms to
-    # 25 ms, which is what made streamed coverage pacing-dependent: at 8
-    # workers throughput fell from 789 to 60 crops/s. Both fields are cleared
-    # with the rest of the evidence when the object generation changes.
-    text_rank: List[Tuple[str, float]] = field(default_factory=list)
-    text_read: str = ""
-    # The frame the newest label box was measured on. The box is in that
-    # frame's coordinates; a client mapping it back to the source image needs
-    # this id, because the result that carries the label arrives on a later one.
-    label_frame_id: int = 0
-
-    def area(self) -> int:
-        if not self.raw_box:
-            return 0
-        return max(0, self.raw_box[2] - self.raw_box[0]) * max(0, self.raw_box[3] - self.raw_box[1])
-
-
-def _iou(a: Sequence[int], b: Sequence[int]) -> float:
-    ax1, ay1, ax2, ay2 = a
-    bx1, by1, bx2, by2 = b
-    ix1, iy1 = max(ax1, bx1), max(ay1, by1)
-    ix2, iy2 = min(ax2, bx2), min(ay2, by2)
-    iw, ih = max(0, ix2 - ix1), max(0, iy2 - iy1)
-    inter = iw * ih
-    if inter <= 0:
-        return 0.0
-    area_a = max(0, ax2 - ax1) * max(0, ay2 - ay1)
-    area_b = max(0, bx2 - bx1) * max(0, by2 - by1)
-    union = area_a + area_b - inter
-    return inter / union if union > 0 else 0.0
-
-
-def _shift_box(
-    box: Sequence[int],
-    from_origin: Optional[Sequence[float]],
-    to_origin: Sequence[float],
-) -> Optional[List[int]]:
-    """A box measured at one shelf origin, expressed at another.
-
-    The origin is the shelf position of the frame's top-left corner, so the
-    content moves the opposite way: a shelf that has slid left by 12 px means
-    the same product now sits 12 px further left in the frame, and a box
-    measured before the slide has to move with it.
-
-    Returns None when either origin is unknown. An uncompensated box is not a
-    weaker match, it is a match against the wrong product, so a caller that
-    cannot place the box must not use it at all.
-    """
-    if from_origin is None or len(from_origin) < 2 or len(to_origin) < 2:
-        return None
-    dx = float(from_origin[0]) - float(to_origin[0])
-    dy = float(from_origin[1]) - float(to_origin[1])
-    x1, y1, x2, y2 = box
-    return [int(round(x1 + dx)), int(round(y1 + dy)),
-            int(round(x2 + dx)), int(round(y2 + dy))]
-
-
-def _clear_of_edge(box: Sequence[int], frame_shape: Sequence[int], margin_ratio: float) -> bool:
-    """True when a box sits clear of the frame edge.
-
-    A facing the frame clips is only part of a product, so neither lane may
-    spend a decision on it. Both lanes ask this question, so a candidate the
-    frame path would refuse cannot be asked about through the crop lane
-    instead.
-    """
-    frame_h, frame_w = frame_shape[:2]
-    margin_x = int(frame_w * margin_ratio)
-    margin_y = int(frame_h * margin_ratio)
-    x1, y1, x2, y2 = box
-    return x1 > margin_x and y1 > margin_y and x2 < frame_w - margin_x and y2 < frame_h - margin_y
-
-
-def _crop_off_product(requested: Sequence[int], current: Sequence[int]) -> bool:
-    """True when a track no longer covers the box a crop was taken from.
-
-    The measure a settled label already uses: below DRIFT_IOU the track has
-    slid onto a different product, and a crop of the old one says nothing about
-    what is there now. A box that was never measured cannot vouch either, so
-    it counts as off product.
-    """
-    if len(requested) != 4 or len(current) != 4:
-        return True
-    return _iou(current, requested) < DRIFT_IOU
-
-
-def _validate_crop_limit(value: Any) -> int:
-    """The crop limit a client stated, or ValueError.
-
-    The limit is a memory bound, so a value the server cannot honour is refused
-    rather than clamped: quietly handing a client that asked for 512 B a 1 KiB
-    floor, or one that asked for 4 GiB a 1 MiB ceiling, leaves the two sides
-    believing different limits. bool is an int in Python and is not a limit.
-    """
-    if isinstance(value, bool) or not isinstance(value, int):
-        raise ValueError("max_crop_bytes must be an integer")
-    if value <= 0 or value > MAX_CROP_BYTES:
-        raise ValueError("max_crop_bytes must be 1..%d" % MAX_CROP_BYTES)
-    return value
-
-
-class _CropRejected(Exception):
-    """A returned crop that may not be used, carrying the reply to send back."""
-
-    def __init__(self, code: str, message: str):
-        super().__init__(message)
-        self.reply = _crop_error(code, message)
-
-
-def _crop_error(code: str, message: str) -> Dict[str, Any]:
-    """The one shape a refused crop answers with, recoverable like any other."""
-    return {"type": "crop_error", "code": code, "message": message}
-
-
-class LiveSession:
-    """Stateful live session for one streaming client."""
-
-    def __init__(
-        self,
-        session_id: str,
-        engine: RecognitionEngine,
-        *,
-        settings: Optional[LiveSettings] = None,
-        tracker: Optional[SessionTracker] = None,
-        executor: Optional[ThreadPoolExecutor] = None,
-    ):
-        self.session_id = session_id
-        self.engine = engine
-        self.settings = settings or LiveSettings()
-        self.tracker = tracker or SessionTracker()
-        self._executor = executor or engine.executor
-        self._lock = threading.Lock()
-        self._generation = 0
-
-        self._tracks: Dict[int, TrackState] = {}
-        self._last_seen_seq: Dict[int, int] = {}
-        # Monotonic per-id counters that outlive track-state pruning, so a client
-        # can always tell a label change caused by re-verification from a silent
-        # flip (see TrackState.relabels / .restarts).
-        self._relabel_count: Dict[int, int] = {}
-        self._restart_count: Dict[int, int] = {}
-        self._latest_image: Optional[Any] = None
-        self._latest_seq = 0
-        self._latest_frame_id: Optional[int] = None
-
-        self._pending: Optional[Tuple[int, bytes]] = None
-        self._frame_ready = threading.Event()
-        # One waiter, created on the caller's loop. None until the first async take.
-        self._waiter: Optional[asyncio.Event] = None
-        self._waiter_loop: Optional[asyncio.AbstractEventLoop] = None
-        self._closed = False
-
-        self._seq = 0
-        self._frames_received = 0
-        self._frames_processed = 0
-        self._frames_dropped = 0
-        self._process_times: "collections.deque[float]" = collections.deque(maxlen=STATS_WINDOW)
-        self._classify_times: "collections.deque[float]" = collections.deque(maxlen=STATS_WINDOW)
-        # A lane that raised is an operational failure, not "nothing matched".
-        # Counted so a broken run is never read as a coverage change.
-        self._classify_failures = 0
-        self._slot_history: "collections.deque[List[Dict[str, Any]]]" = collections.deque(
-            maxlen=EMPTY_SLOT_HISTORY
-        )
-        self._slots: List[Dict[str, Any]] = []
-        self._slot_counter = 0
-        self._catalog_by_sku: Dict[str, Dict[str, Any]] = {}
-        self._catalog_version = ""
-        self._tags = None
-        # Shelf position of this frame's origin. Register and origin update
-        # share one gate so overlapping workers cannot reorder either.
-        self._shift_gate = threading.Lock()
-        self._frame_origin = [0.0, 0.0]
-        self._shift_map = None
-        self._compliance_tracker = None
-        # Full-resolution crop lane. A request pins the frame and the track
-        # object it was minted for, so a late or borrowed crop cannot relabel a
-        # product the client never photographed.
-        self._crop_requests: Dict[str, Dict[str, Any]] = {}
-        self._crop_counter = 0
-        self._crops_requested = 0
-        self._crops_accepted = 0
-        self._crops_rejected = 0
-        self._last_compliance: Dict[str, Any] = {"violations": 0, "by_rule": {}, "rows": []}
-        self._label_memory: "collections.deque[Dict[str, Any]]" = collections.deque(
-            maxlen=LABEL_MEMORY_MAX
-        )
-        self._last_quality: Dict[str, Any] = {}
-        self._created_at = time.time()
-
-    # ------------------------------------------------------------------ control
-    @property
-    def generation(self) -> int:
-        with self._lock:
-            return self._generation
-
-    def submit_frame(self, frame_id: int, payload: bytes) -> bool:
-        """Latest-frame-wins: replaces any frame not yet picked up by the loop."""
-        with self._lock:
-            if self._closed:
-                return False
-            self._frames_received += 1
-            if self._pending is not None:
-                self._frames_dropped += 1
-            self._pending = (int(frame_id), payload)
-            self._frame_ready.set()
-            self._wake_waiter_locked()
-            return True
-
-    def _pop_pending_locked(self) -> Optional[Tuple[int, bytes]]:
-        pending = self._pending
-        self._pending = None
-        self._frame_ready.clear()
-        return pending
-
-    def _wake_waiter_locked(self) -> None:
-        """Set the async event from this thread. Caller holds self._lock."""
-        waiter = self._waiter
-        loop = self._waiter_loop
-        if waiter is None or loop is None:
-            return
-        try:
-            running = asyncio.get_running_loop()
-        except RuntimeError:
-            running = None
-        if running is loop:
-            waiter.set()
-            return
-        try:
-            loop.call_soon_threadsafe(waiter.set)
-        except RuntimeError:
-            # Loop already closed. The sync event still wakes take_pending.
-            pass
-
-    def take_pending(self, timeout: float = 0.2) -> Optional[Tuple[int, bytes]]:
-        """Blocking pop for the processing loop; None on timeout/close."""
-        if not self._frame_ready.wait(timeout):
-            return None
-        with self._lock:
-            return self._pop_pending_locked()
-
-    async def take_pending_async(self) -> Optional[Tuple[int, bytes]]:
-        """Awaitable pop. None means the session closed with nothing left.
-
-        Bind the waiter to the running loop on first use. A frame that arrives
-        before that bind is still visible: the event starts set when one is queued.
-        """
-        loop = asyncio.get_running_loop()
-        with self._lock:
-            if self._waiter is not None and self._waiter_loop is not loop:
-                raise RuntimeError("take_pending_async is bound to another event loop")
-            if self._waiter is None:
-                self._waiter = asyncio.Event()
-                self._waiter_loop = loop
-                if self._pending is not None or self._closed:
-                    self._waiter.set()
-            waiter = self._waiter
-
-        while True:
-            await waiter.wait()
-            with self._lock:
-                if self._pending is not None:
-                    pending = self._pop_pending_locked()
-                    if self._waiter is waiter and self._pending is None and not self._closed:
-                        self._waiter.clear()
-                    return pending
-                if self._closed:
-                    return None
-                if self._waiter is waiter:
-                    self._waiter.clear()
-
-    def close(self) -> None:
-        with self._lock:
-            self._closed = True
-            self._pending = None
-            self._frame_ready.set()
-            self._wake_waiter_locked()
-
-    @property
-    def closed(self) -> bool:
-        with self._lock:
-            return self._closed
-
-    def configure(
-        self,
-        *,
-        method: Optional[str] = None,
-        conf: Optional[float] = None,
-        empty_slots: Optional[bool] = None,
-        tags: Optional[bool] = None,
-        ocr: Optional[bool] = None,
-    ) -> Dict[str, Any]:
-        with self._lock:
-            method_changed = method is not None and method != self.settings.method
-            if method is not None:
-                self.settings.method = method
-            if conf is not None:
-                self.settings.conf = float(conf)
-            if empty_slots is not None:
-                self.settings.empty_slots = bool(empty_slots)
-            if tags is not None:
-                self.settings.tags = bool(tags)
-            if ocr is not None:
-                self.settings.ocr = bool(ocr)
-            if method_changed:
-                self._generation += 1
-                for track in self._tracks.values():
-                    track.state = STATE_DETECTED
-                    # Nothing has decided this track under the new method yet,
-                    # so it is unconfirmed again rather than a settled track
-                    # that happens to carry no label.
-                    track.confirmed = False
-                    track.identity = None
-                    track.candidates = []
-                    track.score_sums = {}
-                    track.observations = []
-                    track.text_rank = []
-                    track.text_read = ""
-                    track.attempts = 0
-                    track.in_flight = 0
-                    track.last_attempt_seq = 0
-                    # A crop asked about under the old method is evidence measured
-                    # under settings the session no longer holds, so the new method
-                    # gets a budget of its own.
-                    track.crop_attempts = 0
-                    track.last_attempt_area = 0
-                # Outstanding crops were minted against the old method and the
-                # old generation; answering one now would be evidence measured
-                # under settings the session no longer holds.
-                self._crop_requests.clear()
-            return self.config()
-
-    def config(self) -> Dict[str, Any]:
-        return {
-            "method": self.settings.method,
-            "conf": self.settings.conf,
-            "empty_slots": self.settings.empty_slots,
-            "ocr": self.settings.ocr,
-            "tags": self.settings.tags,
-            "tags_available": self._tags is not None,
-            "ocr_engine": getattr(self._tags.engine, "name", "none") if self._tags else "none",
-        }
-
-    def reset(self) -> None:
-        with self._shift_gate:
-            with self._lock:
-                self._generation += 1
-                self.tracker.reset()
-                self._tracks.clear()
-                self._last_seen_seq.clear()
-                self._relabel_count.clear()
-                self._restart_count.clear()
-                self._slot_history.clear()
-                self._label_memory.clear()
-                self._crop_requests.clear()
-                self._slots = []
-                self._latest_image = None
-                self._latest_frame_id = None
-                self._frame_origin = [0.0, 0.0]
-                self._shift_map = None
-                # ponytail: do not touch _waiter here. asyncio.Event is loop-affine;
-                # take_pending_async clears a stale wake on its own loop.
-
-    # ------------------------------------------------------------------ frame path
-    def process_frame(self, frame_id: int, payload: bytes) -> Dict[str, Any]:
-        """Decode -> detect -> track -> schedule classification -> result dict."""
-        started = time.perf_counter()
-        image = self.engine.decode_image(payload)
-        decoded_at = time.perf_counter()
-
-        with self._lock:
-            conf = self.settings.conf
-            method = self.settings.method
-            empty_enabled = self.settings.empty_slots
-            generation = self._generation
-
-        shelf_offset = self._note_frame_shift(image)
-
-        boxes = self.engine.detect(image, conf=conf)
-        detected_at = time.perf_counter()
-        tracks = self.tracker.update(
-            [box["box"] for box in boxes], [box["confidence"] for box in boxes]
-        )
-        tracked_at = time.perf_counter()
-
-        with self._lock:
-            self._seq += 1
-            seq = self._seq
-            self._frames_processed += 1
-            self._latest_image = image
-            self._latest_seq = seq
-            self._latest_frame_id = int(frame_id)
-            self._process_times.append(tracked_at)
-
-            by_id = {box["id"]: box for box in boxes}
-            # Ids whose box this frame actually measured. A track the tracker
-            # is carrying without a detection keeps its last measured box, and
-            # that box must not be handed to the phone as if it described the
-            # frame the request points at.
-            measured: Set[int] = set()
-            active: Dict[int, TrackState] = {}
-            for track in tracks:
-                state = self._tracks.get(track.track_id)
-                raw_box = None
-                confidence = track.score
-                measured_now = track.det_index is not None and track.det_index in by_id
-                if measured_now:
-                    detection = by_id[track.det_index]
-                    raw_box = list(detection["box"])
-                    confidence = float(detection["confidence"])
-                if raw_box is None:
-                    # Low-score second pass, or a Kalman box with no detection.
-                    # Keep the last measured crop. Do not invent one from the prediction.
-                    raw_box = list(state.raw_box) if state is not None and state.raw_box else []
-                    confidence = float(state.confidence) if state is not None else confidence
-                low_score = bool(getattr(track, "low_score", False)) or (
-                    track.det_index is None and raw_box == []
-                )
-                previous_seq = self._last_seen_seq.get(track.track_id)
-                if state is None:
-                    # An id that comes back after an absence may be a recycled id:
-                    # the tracker reuses numbers after it drops a track, so the old
-                    # label cannot be trusted and the object counts as restarted.
-                    recycled = previous_seq is not None and seq - previous_seq > 1
-                    if recycled:
-                        self._restart_count[track.track_id] = (
-                            self._restart_count.get(track.track_id, 0) + 1
-                        )
-                        self._relabel_count[track.track_id] = (
-                            self._relabel_count.get(track.track_id, 0) + 1
-                        )
-                    state = TrackState(
-                        track_id=track.track_id,
-                        first_seq=seq,
-                        last_seq=seq,
-                        object_generation=1 if recycled else 0,
-                        relabels=self._relabel_count.get(track.track_id, 0),
-                        restarts=self._restart_count.get(track.track_id, 0),
-                    )
-                elif seq - state.last_seq > 1:
-                    # The id was absent and the tracker started it over. The
-                    # previous label belonged to the old object, so start clean
-                    # rather than keep a label that may now name a neighbour.
-                    self._restart_count[track.track_id] = self._restart_count.get(track.track_id, 0) + 1
-                    self._relabel_count[track.track_id] = self._relabel_count.get(track.track_id, 0) + 1
-                    state = TrackState(
-                        track_id=track.track_id,
-                        first_seq=state.first_seq,
-                        last_seq=seq,
-                        hits=1,
-                        object_generation=state.object_generation + 1,
-                        relabels=self._relabel_count[track.track_id],
-                        restarts=self._restart_count[track.track_id],
-                    )
-                else:
-                    state.hits += 1
-                    state.last_seq = seq
-                self._last_seen_seq[track.track_id] = seq
-                state.box = list(track.box)
-                if raw_box and not low_score:
-                    state.raw_box = raw_box
-                    state.confidence = confidence
-                    if measured_now:
-                        # A detection in this frame's list, at a score the tracker
-                        # trusts: the box describes the frame the request names.
-                        measured.add(track.track_id)
-                elif not state.raw_box and raw_box:
-                    state.raw_box = raw_box
-                    state.confidence = confidence
-                self._drop_stale_label(state)
-                active[track.track_id] = state
-            self._tracks = active
-            if len(self._last_seen_seq) > 5000:
-                live_ids = set(self._tracks)
-                keep = {
-                    tid: seen for tid, seen in self._last_seen_seq.items()
-                    if tid in live_ids or seq - seen <= 300
-                }
-                self._last_seen_seq = keep
-                self._relabel_count = {tid: c for tid, c in self._relabel_count.items() if tid in keep}
-                self._restart_count = {tid: c for tid, c in self._restart_count.items() if tid in keep}
-
-            if empty_enabled:
-                slot_boxes = [
-                    {"box": state.box} for state in self._tracks.values() if state.box
-                ]
-                raw_slots = self.engine.empty_slots(image.shape, slot_boxes)
-            else:
-                raw_slots = []
-            self._slots = self._persist_slots(raw_slots)
-
-            quality = self._quality(image, self._tracks)
-            self._inherit_labels(seq)
-
-            # Price tags: plan a pass (cheap) under the lock, run it on the tag
-            # worker, and attach whatever the cache already knows this frame.
-            tag_bands = []
-            if self._tags is not None and self.settings.tags:
-                tag_boxes = [
-                    {"id": state.track_id, "box": state.box}
-                    for state in self._tracks.values()
-                    if state.box
-                ]
-                tag_bands = self._tags.plan(tag_boxes, image.shape, shelf_offset=shelf_offset)
-            # Collect the classification work under the lock; submit it after
-            # releasing it. add_done_callback can run the callback inline when the
-            # future already finished, and the callback takes this same lock.
-            submissions = self._plan_classification(image, seq, int(frame_id))
-            crop_requests = self._plan_crop_requests(image.shape, seq, int(frame_id), measured)
-
-            summary = self._summary_locked()
-            stats = self._stats_locked()
-            track_payload = [self._track_payload(state) for state in self._tracks.values()]
-            if crop_requests:
-                for row in track_payload:
-                    request = crop_requests.get(row["track_id"])
-                    if request is not None:
-                        row["crop_request"] = request
-            tag_summary = self._apply_tags(seq, track_payload, tag_bands, shelf_offset, image.shape)
-            if tag_summary is not None:
-                summary["price_tags"] = tag_summary["tags"]
-                summary["compliance"] = tag_summary["compliance"]
-
-        self._submit_classification(submissions, method, generation, int(frame_id))
-        self._run_tag_passes(image, tag_bands)
-
-        result = {
-            "type": "result",
-            "frame_id": int(frame_id),
-            "seq": seq,
-            "width": int(image.shape[1]),
-            "height": int(image.shape[0]),
-            "tracks": track_payload,
-            "empty_slots": self._slots,
-            "quality": quality,
-            "summary": summary,
-            "timing_ms": {
-                "decode": round((decoded_at - started) * 1000.0, 2),
-                "detect": round((detected_at - decoded_at) * 1000.0, 2),
-                "track": round((tracked_at - detected_at) * 1000.0, 2),
-                "total": round((tracked_at - started) * 1000.0, 2),
-            },
-            "stats": stats,
-        }
-        return result
-
-
-    def _inherit_labels(self, seq: int) -> None:
-        """Give a fresh track the label of a recently recognized product it covers.
-
-        Only a measured label, still overlapping its own box, may be copied.
-        An inherited label is not a measurement, so it cannot spread to a neighbour.
-        Blur, glare, and a too-far frame skip inheritance: the overlap is not
-        evidence when the frame itself failed the quality gate.
-
-        Memory boxes are stored in the coordinates of the frame they were
-        measured on, so they are shifted by the shelf movement since then before
-        they are compared. Without that, a panning camera slides the scene under
-        a stale box: at 12 px/frame a ~90 px facing moves a full width in about
-        seven frames, so the neighbour overlaps the old box at IoU >= 0.55 and
-        inherits a label that was never about it. Measured on image6 at pan 12,
-        every unknown false accept was an inherited label landing on the two
-        products next to the labelled one.
-        """
-        if not self._label_memory:
-            return
-        if (self._last_quality or {}).get("hint"):
-            return
-        origin = (self._frame_origin[0], self._frame_origin[1])
-        for track in self._tracks.values():
-            if track.confirmed or track.state != STATE_DETECTED:
-                continue
-            for entry in reversed(self._label_memory):
-                if seq - entry["seq"] > LABEL_MEMORY_FRAMES:
-                    break
-                if entry.get("inherited"):
-                    continue
-                if _iou(entry["box"], entry.get("label_box") or entry["box"]) < DRIFT_IOU:
-                    continue
-                moved = _shift_box(entry["box"], entry.get("origin"), origin)
-                if moved is None:
-                    # The shelf moved too far for this entry to describe
-                    # anything still on screen, so it cannot label a neighbour.
-                    continue
-                if _iou(track.box, moved) < LABEL_MEMORY_IOU:
-                    continue
-                track.state = STATE_RECOGNIZED
-                track.confirmed = True
-                # The raw detection box, not the smoothed track box: smoothing
-                # lags a pan by about one step, and the label is about where the
-                # product was measured, not where the filter has drifted to.
-                track.label_box = list(track.raw_box or track.box)
-                # The box is measured on this frame, so the client must map it
-                # with this frame's offset. Leaving the default 0 makes every
-                # inherited label map as if it came from the first frame, which
-                # during a pan puts it a whole pan-distance away from the
-                # product it names.
-                track.label_frame_id = int(self._latest_frame_id)
-                track.last_source = SOURCE_INHERITED
-                provisional = bool(entry.get("provisional"))
-                track.identity = {
-                    "sku_id": entry["sku_id"],
-                    "confidence": entry["confidence"],
-                    "score": entry["score"],
-                    "attempts": 0,
-                    "inherited": True,
-                    "provisional": provisional,
-                }
-                # An inherited provisional label is still a guess, so it stays
-                # re-classifiable instead of freezing onto the whole run.
-                track.confirmed = not provisional
-                track.relabels = self._relabel_count.get(track.track_id, 0) + 1
-                self._relabel_count[track.track_id] = track.relabels
-                break
-
-    def _remember_label(self, track: TrackState, box: List[int], seq: int) -> None:
-        """Must be called with the session lock held."""
-        if track.state != STATE_RECOGNIZED or not track.identity:
-            return
-        self._label_memory.append(
-            {
-                "box": list(box),
-                "label_box": list(track.label_box or box),
-                "sku_id": track.identity.get("sku_id"),
-                "confidence": track.identity.get("confidence"),
-                "score": track.identity.get("score"),
-                "seq": seq,
-                # The shelf origin this box was measured against. `box` is in
-                # that frame's pixels, so a later frame has to shift it by how
-                # far the shelf has moved since.
-                "origin": (self._frame_origin[0], self._frame_origin[1]),
-                "inherited": bool(track.identity.get("inherited")),
-                "provisional": bool(track.identity.get("provisional")),
-            }
-        )
-
-    def _drop_stale_label(self, track: TrackState) -> None:
-        """Reset a settled label when the box has drifted off its product.
-
-        Panning cameras make trackers slide a box onto the neighbouring product.
-        A label measured on the old box would then name the wrong product, so the
-        track goes back to detected and is classified again.
-
-        Two consecutive drifted frames are required: one noisy frame (a bad
-        detection or a blurred capture) must not churn a good label.
-        """
-        if track.state not in (STATE_RECOGNIZED, STATE_NEEDS_CONFIRMATION, STATE_UNRECOGNIZED):
-            return
-        if track.label_box and _iou(track.box, track.label_box) < DRIFT_IOU:
-            track.drift_frames += 1
-        else:
-            track.drift_frames = 0
-        if track.drift_frames < 2:
-            return
-        track.drift_frames = 0
-        # A crop asked about the product this box has now left says nothing
-        # about the neighbour in its place, so the answer goes with the label
-        # and the new product is asked about on a budget of its own.
-        for request_id in [rid for rid, rec in self._crop_requests.items()
-                           if rec["track_id"] == track.track_id]:
-            self._crops_rejected += 1
-            self._release_crop_slot(self._crop_requests.pop(request_id))
-        self._relabel_count[track.track_id] = self._relabel_count.get(track.track_id, 0) + 1
-        track.relabels = self._relabel_count[track.track_id]
-        track.state = STATE_DETECTED
-        # The label went with the product, so the track is undecided again:
-        # a settled track that carries no label can never be asked about.
-        track.confirmed = False
-        track.identity = None
-        track.candidates = []
-        track.score_sums = {}
-        track.observations = []
-        # The reading described the product the box has left, so it goes with
-        # the label rather than naming whatever moved into the box.
-        track.text_rank = []
-        track.text_read = ""
-        track.attempts = 0
-        track.in_flight = 0
-        track.last_attempt_seq = 0
-        track.last_attempt_area = 0
-        track.label_box = []
-        track.crop_attempts = 0
-
-    # ------------------------------------------------------------------ classification
-    def _plan_classification(
-        self, image: Any, seq: int, frame_id: int
-    ) -> List[Tuple[TrackState, Any, int]]:
-        """Pick what to classify now. Must be called with the session lock held.
-
-        Returns (track, crop, object_generation) triples; the caller submits them
-        after the lock is released. The frame id travels with the plan because
-        the label box is in *that* frame's coordinates: by the time the answer
-        arrives the track has moved, and a client mapping the box back to the
-        source image needs the frame the box was measured on, not the one the
-        result was delivered with.
-        """
-        frame_h, frame_w = image.shape[:2]
-        margin_y = int(frame_h * self.settings.edge_margin_ratio)
-        inflight = sum(track.in_flight for track in self._tracks.values())
-        capacity = max(0, self.settings.max_inflight_per_session - inflight)
-        if capacity <= 0:
-            return []
-
-        eligible: List[TrackState] = []
-        poor_frame = bool((self._last_quality or {}).get("hint"))
-        for track in self._tracks.values():
-            if track.in_flight > 0 or track.confirmed:
-                continue
-            if track.state not in (STATE_DETECTED, STATE_RECOGNIZED):
-                continue
-            if poor_frame:
-                continue
-            if track.hits < self.settings.min_hits:
-                continue
-            if len(track.raw_box) != 4:
-                continue
-            x1, y1, x2, y2 = track.raw_box
-            if min(x2 - x1, y2 - y1) < self.settings.min_crop_px:
-                continue
-            if not _clear_of_edge(track.raw_box, image.shape, self.settings.edge_margin_ratio):
-                continue
-            if track.attempts >= self.settings.max_attempts:
-                continue
-            if track.attempts > 0:
-                if (seq - track.last_attempt_seq) < self.settings.retry_gap:
-                    if track.area() < self.settings.area_growth * max(1, track.last_attempt_area):
-                        continue
-                retry_margin_x = int(frame_w * self.settings.retry_edge_ratio)
-                retry_margin_y = int(frame_h * self.settings.retry_edge_ratio)
-                if (x1 <= retry_margin_x or y1 <= retry_margin_y
-                        or x2 >= frame_w - retry_margin_x or y2 >= frame_h - retry_margin_y):
-                    continue
-            eligible.append(track)
-
-        # Newest first: while a camera pans, the products still worth labelling
-        # are the ones that just entered the frame. Area is only the tie-break.
-        eligible.sort(key=lambda track: (-track.first_seq, -track.area()))
-        planned: List[Tuple[TrackState, Any, int]] = []
-        for track in eligible[:capacity]:
-            crop = self.engine.crop(image, track.raw_box)
-            # No label_frame_id here: the plan may be dropped (generation
-            # mismatch, a crop that went off product, a neighbour's label
-            # arriving first), and a frame id stamped on a track whose label
-            # never came from this frame would pair a box with the wrong
-            # offset. The callback carries the id, and the writers that set
-            # `label_box` set the id with it.
-            track.last_attempt_seq = seq
-            track.last_attempt_area = track.area()
-            # Counted here, not in the submitter: this is the only place that
-            # knows a crop is about to be in flight, and the callback is what
-            # decrements it. Without the increment the guard above never blocks
-            # and every frame re-plans the same tracks, so the executor queue
-            # grows without bound and the server stops answering.
-            track.in_flight += 1
-            planned.append((track, crop, track.object_generation))
-        return planned
-
-    def _submit_classification(
-        self,
-        planned: List[Tuple[TrackState, Any, int]],
-        method: str,
-        generation: int,
-        frame_id: int,
-    ) -> None:
-        """Submit planned crops. Called WITHOUT the session lock held."""
-        for track, crop, object_generation in planned:
-            submitted_at = time.perf_counter()
-            # The label anchors to the displayed (smoothed) box, which is what
-            # the drift check later compares against.
-            submitted_box = list(track.box)
-            track_id = track.track_id
-            # OCR once per object: a track that already has a reading re-runs
-            # only the local lane. The native crop lane always reads, because
-            # it is only asked for when the frame crop was too small to decide.
-            skip_ocr = bool(track.text_read) or not self.settings.ocr
-            try:
-                future = self._executor.submit(
-                    self.engine.score_crop_lanes, crop, method, skip_ocr
-                )
-            except RuntimeError:  # executor already shut down
-                with self._lock:
-                    track.in_flight = max(0, track.in_flight - 1)
-                continue
-            future.add_done_callback(
-                lambda fut, tid=track_id, gen=generation, m=method, started=submitted_at,
-                obj=object_generation, box=submitted_box, fid=int(frame_id):
-                self._on_classified(fut, tid, gen, m, started, obj, box, fid)
-            )
-
-    # ------------------------------------------------------------------ crop lane
-    def capabilities(self) -> Dict[str, Any]:
-        """What this client is allowed to ask the phone for.
-
-        A client that never sends `capabilities` keeps the protocol-v1 defaults:
-        no crop lane, so no result ever carries a crop request.
-        """
-        return {
-            "crop": bool(self.settings.crops),
-            "max_crop_bytes": int(self.settings.max_crop_bytes),
-        }
-
-    def enable_crops(self, max_crop_bytes: Optional[int] = None) -> Dict[str, Any]:
-        """Turn the lane on for a client that advertised it. The client states the
-        largest crop it will produce; a limit that is not one is refused and the
-        lane stays where it was, so a bad statement enables nothing."""
-        stated = None if max_crop_bytes is None else _validate_crop_limit(max_crop_bytes)
-        with self._lock:
-            self.settings.crops = True
-            if stated is not None:
-                self.settings.max_crop_bytes = stated
-            return self.capabilities()
-
-    def _plan_crop_requests(
-        self, frame_shape: Tuple[int, int], seq: int, frame_id: int, measured: Set[int]
-    ) -> Dict[int, Dict[str, Any]]:
-        """Ask the phone for a fuller crop of tracks the frame crop could not use.
-
-        The box is in the pixel space of `frame_id`, the frame the client just
-        sent, so the phone can answer from the buffer behind that exact frame
-        rather than from whatever is on screen now. That is also why the box
-        has to be one this frame measured and has to sit clear of the edge: the
-        phone crops by these numbers, so a carried-over or clipped box buys a
-        photograph of the wrong thing. At most `max_attempts` requests per
-        object, `max_crop_inflight` outstanding, and only for a client that
-        advertised the lane.
-        """
-        self._expire_crop_requests(seq)
-        if not self.settings.crops:
-            return {}
-        room = self.settings.max_crop_inflight - len(self._crop_requests)
-        if room <= 0:
-            return {}
-        if (self._last_quality or {}).get("hint") in CROP_BLIND_QUALITY:
-            # A blurred or glared frame photographs the same bad scene at the
-            # camera's own resolution, so there is nothing to ask the phone for.
-            return {}
-        planned: Dict[int, Dict[str, Any]] = {}
-        for track in self._tracks.values():
-            if len(planned) >= room:
-                break
-            if track.crop_attempts >= self.settings.max_attempts or track.in_flight > 0:
-                continue
-            if track.confirmed or track.state not in (STATE_DETECTED, STATE_RECOGNIZED):
-                continue
-            if track.hits < self.settings.min_hits or len(track.raw_box) != 4:
-                continue
-            if track.track_id not in measured:
-                # The box is a carry-over: either no detection matched this
-                # frame, or the one that did was too low a score to move it.
-                # Either way it points at where the product used to be.
-                continue
-            if not _clear_of_edge(track.raw_box, frame_shape, self.settings.edge_margin_ratio):
-                continue
-            x1, y1, x2, y2 = track.raw_box
-            # The frame crop is too small to say anything about this product. At
-            # the camera's own resolution it may still be worth a decision, and
-            # that is the only thing a crop can add here.
-            if min(x2 - x1, y2 - y1) >= self.settings.crop_below_px:
-                continue
-            self._crop_counter += 1
-            request_id = "c%d" % self._crop_counter
-            # The detection box this crop is a photograph of. The answer is only
-            # about that product, so the box travels with the request.
-            requested_box = list(track.raw_box)
-            self._crop_requests[request_id] = {
-                "request_id": request_id,
-                "track_id": track.track_id,
-                "frame_id": int(frame_id),
-                "box": requested_box,
-                "object_generation": track.object_generation,
-                "generation": self._generation,
-                "seq": seq,
-                "method": self.settings.method,
-            }
-            track.crop_attempts += 1
-            # Hold the track while the phone answers, so the frame path does not
-            # spend a second attempt on the same box at the same time.
-            track.in_flight += 1
-            self._crops_requested += 1
-            planned[track.track_id] = {
-                "request_id": request_id,
-                "track_id": track.track_id,
-                "frame_id": int(frame_id),
-                "box": list(requested_box),
-            }
-        return planned
-
-    def _expire_crop_requests(self, seq: int) -> None:
-        """Drop requests the phone did not answer in time.
-
-        A request holds the track busy while it is out, so an unanswered one has
-        to give that slot back or the track could never be classified at all.
-        """
-        stale = [
-            request_id for request_id, record in self._crop_requests.items()
-            if seq - record["seq"] > self.settings.crop_ttl_frames
-        ]
-        for request_id in stale:
-            record = self._crop_requests.pop(request_id)
-            self._crops_rejected += 1
-            self._release_crop_slot(record)
-
-    def _release_crop_slot(self, record: Dict[str, Any]) -> None:
-        """Give back the busy slot a crop request took. Lock must be held."""
-        track = self._tracks.get(record["track_id"])
-        if track is not None and track.object_generation == record["object_generation"]:
-            track.in_flight = max(0, track.in_flight - 1)
-
-    def submit_crop(
-        self,
-        request_id: Any,
-        track_id: Any,
-        frame_id: Any,
-        payload: Optional[bytes],
-    ) -> Dict[str, Any]:
-        """Score a crop the client returned for a track we asked about.
-
-        The crop is evidence about one track object, in one frame, decided under
-        one configuration. Anything that breaks that binding is refused before a
-        single pixel reaches the classifier, and the frame crop stays the only
-        evidence for the track.
-        """
-        try:
-            record, box, payload = self._prepare_crop(request_id, track_id, frame_id, payload)
-        except _CropRejected as refused:
-            with self._lock:
-                self._crops_rejected += 1
-            return refused.reply
-        try:
-            crop = self.engine.decode_image(payload)
-        except Exception:
-            # The busy slot is the request's to give back, whoever failed on it.
-            with self._lock:
-                self._release_crop_slot(record)
-                self._crops_rejected += 1
-            return _crop_error("CROP_UNDECODABLE", "crop is not a readable image")
-        submitted_at = time.perf_counter()
-        track_id_out = record["track_id"]
-        generation = record["generation"]
-        method = record["method"]
-        object_generation = record["object_generation"]
-        with self._lock:
-            # Decoding is the slow part and the session keeps running through it,
-            # so the binding checked before the decode is re-checked here. The
-            # check and the submit share one critical section: an ack has to mean
-            # this crop was measured against the track object it answers.
-            stale = self._crop_stale_reason_locked(record)
-            if stale is not None:
-                self._release_crop_slot(record)
-                self._crops_rejected += 1
-                return _crop_error("CROP_STALE", stale)
-            # A native crop is the sharpest view the phone sends, and the lane
-            # only asks for one when the frame crop was too small to decide
-            # (`crop_below_px`), so any reading it would skip came from exactly
-            # the crop this one exists to replace. It always reads, unless the
-            # session turned the text lane off.
-            skip_ocr = not self.settings.ocr
-            try:
-                future = self._executor.submit(
-                    self.engine.score_crop_lanes, crop, method, skip_ocr
-                )
-            except RuntimeError:  # executor already shut down
-                self._release_crop_slot(record)
-                self._crops_rejected += 1
-                return _crop_error("CROP_UNAVAILABLE", "classifier is shutting down")
-            self._crops_accepted += 1
-        future.add_done_callback(
-            lambda fut, tid=track_id_out, gen=generation, m=method, started=submitted_at,
-            obj=object_generation, box=box, fid=int(record["frame_id"]):
-            self._on_classified(fut, tid, gen, m, started, obj, box, fid, SOURCE_CROP)
-        )
-        return {
-            "type": "crop_ack",
-            "request_id": record["request_id"],
-            "track_id": track_id_out,
-            "frame_id": record["frame_id"],
-        }
-
-    def _prepare_crop(
-        self, request_id: Any, track_id: Any, frame_id: Any, payload: Optional[bytes]
-    ) -> Tuple[Dict[str, Any], List[int], bytes]:
-        """Check a returned crop against the request it claims to answer.
-
-        Raises [_CropRejected] with the reply to send. The request is consumed
-        either way: it is single use, so a rejected crop cannot be retried
-        against a track that has since moved.
-        """
-        with self._lock:
-            record = self._crop_requests.pop(str(request_id), None) if request_id else None
-            if record is None:
-                raise _CropRejected("CROP_UNKNOWN_REQUEST", "no crop is waiting for that request id")
-            try:
-                answered_track = int(track_id)
-                answered_frame = int(frame_id)
-            except (TypeError, ValueError):
-                self._release_crop_slot(record)
-                raise _CropRejected("CROP_BAD_MESSAGE", "track_id and frame_id are required")
-            if answered_track != record["track_id"] or answered_frame != record["frame_id"]:
-                # A crop of some other track, or of another frame, says nothing
-                # about this one however well it matches.
-                self._release_crop_slot(record)
-                raise _CropRejected("CROP_MISMATCH", "crop does not match the request it answers")
-            if self._seq - record["seq"] > self.settings.crop_ttl_frames:
-                self._release_crop_slot(record)
-                raise _CropRejected("CROP_STALE", "crop answered too late to describe the track")
-            if record["generation"] != self._generation:
-                self._release_crop_slot(record)
-                raise _CropRejected("CROP_STALE", "the session changed after the request")
-            track = self._tracks.get(record["track_id"])
-            if track is None or track.object_generation != record["object_generation"]:
-                self._release_crop_slot(record)
-                raise _CropRejected("CROP_STALE", "the track id was recycled after the request")
-            # The sender keeps streaming while the requested crop is encoded.
-            # A newer frame is normal; object generation and the bounded frame
-            # TTL, not exact latest-sequence equality, guard against reuse.
-            if track.last_seq < record["seq"]:
-                self._release_crop_slot(record)
-                raise _CropRejected("CROP_STALE", "the track is no longer in the requested frame")
-            if _crop_off_product(record["box"], track.raw_box):
-                # The crop is of the product the phone was asked to photograph.
-                # If the track has since slid onto a neighbour the answer would
-                # be a label for a product nobody cropped.
-                self._release_crop_slot(record)
-                raise _CropRejected("CROP_STALE", "the track moved off the product that was cropped")
-            limit = self.settings.max_crop_bytes
-            if not payload or len(payload) > limit:
-                self._release_crop_slot(record)
-                raise _CropRejected("CROP_TOO_LARGE", "crop is empty or over %d bytes" % limit)
-            # The label anchors to the box the crop was taken from, not to where
-            # the track is by the time the answer lands.
-            return record, list(record["box"]), payload
-
-    def _crop_stale_reason_locked(self, record: Dict[str, Any]) -> Optional[str]:
-        """Why a decoded crop can no longer speak for its track, or None.
-
-        Run after the decode, under the lock that submits the classification.
-        A reset or a method change, a recycled track id, a pan that carried the
-        track off the photographed product, or another attempt that already
-        settled the track can all land while the JPEG is being decoded, and an
-        ack is a promise that this crop decided that track. Lock must be held.
-        """
-        if record["generation"] != self._generation:
-            return "the session changed while the crop was decoded"
-        track = self._tracks.get(record["track_id"])
-        if track is None or track.object_generation != record["object_generation"]:
-            return "the track id was recycled while the crop was decoded"
-        if _crop_off_product(record["box"], track.raw_box):
-            return "the track moved off the product while the crop was decoded"
-        if track.confirmed or track.state not in (STATE_DETECTED, STATE_RECOGNIZED):
-            return "the track is no longer waiting to be decided"
-        return None
-
-    def _on_classified(
-        self,
-        future: Future,
-        track_id: int,
-        generation: int,
-        method: str,
-        submitted_at: float,
-        object_generation: int,
-        submitted_box: List[int],
-        label_frame_id: int = 0,
-        source: str = SOURCE_FRAME,
-    ) -> None:
-        try:
-            scores, text_rank, text_read = future.result()
-        except Exception as exc:
-            # A lane that raised is an operational failure, not a crop that
-            # matched nothing. Swallowing it made a broken run (tracks, no
-            # labels, clean log) indistinguishable from a coverage change, so
-            # it is counted and logged before the empty answer is used.
-            with self._lock:
-                self._classify_failures += 1
-            LOGGER.exception("classification failed for track %s: %s", track_id, exc)
-            scores, text_rank, text_read = {}, [], ""
-        finished_at = time.perf_counter()
-        # Resolved before the session lock is taken. `identity_text_lane` builds
-        # the lane under its own lock on first use, and taking that while the
-        # session lock is held is a lock-ordering hazard: a frame loop that
-        # holds the session lock and waits on the lane lock while this callback
-        # holds the lane lock and waits on the session lock wedges the server.
-        text_lane = self.engine.identity_text_lane()
-        with self._lock:
-            track = self._tracks.get(track_id)
-            if track is None or generation != self._generation:
-                return
-            if track.object_generation != object_generation:
-                # The id was recycled while this crop was being classified.
-                track.in_flight = max(0, track.in_flight - 1)
-                return
-            track.in_flight = max(0, track.in_flight - 1)
-            if track.confirmed or track.state not in (STATE_DETECTED, STATE_RECOGNIZED):
-                # Other evidence settled this track while the answer sat in the
-                # classifier - a label inherited from a labelled neighbour, say.
-                # A late answer must not repaint a label the client is already
-                # showing, so the track keeps the one it settled on. A track
-                # showing a *provisional* label is not settled: it is here so a
-                # later attempt can upgrade or correct it.
-                return
-            if source == SOURCE_CROP and _crop_off_product(submitted_box, track.raw_box):
-                # The answer was measured on the box the track has since left.
-                # Dropping it leaves the track to be decided from a later frame
-                # crop instead of putting the old product's label on whatever
-                # moved into its place.
-                return
-            track.attempts += 1
-            # A crop is an attempt against the same budget as a frame crop, so
-            # the lane cannot be used to re-roll the dice on one product.
-            track.last_source = source
-            observation = {
-                str(sku_id): float(score) for sku_id, score in (scores or {}).items()
-            }
-            track.observations.append(observation)
-            track.observations = track.observations[-3:]
-            for sku_id, score in observation.items():
-                track.score_sums[sku_id] = track.score_sums.get(sku_id, 0.0) + float(score)
-            if observation:
-                track.score_n += 1
-            # The reading is cached on the track so later attempts skip OCR.
-            # Only a read that could name something is cached: a crop that was
-            # too small or too blurry says nothing about the next one, and a
-            # read of just a size ("400g") matches every 400g product's size
-            # line, so caching it would let one unreadable crop veto the
-            # product for the rest of the track's life.
-            #
-            # The substitution happens before the early-out below and before
-            # the decision: a skipped OCR returns an empty ranking, and an
-            # empty ranking would both mark a text-only track unrecognized at
-            # max_attempts and stop the veto from firing on later attempts.
-            if text_read and carries_identity(text_read):
-                track.text_rank = list(text_rank or [])
-                track.text_read = text_read
-            elif track.text_read:
-                text_rank, text_read = track.text_rank, track.text_read
-
-            if not any(track.observations) and not text_rank:
-                if track.attempts >= self.settings.max_attempts:
-                    track.state = STATE_UNRECOGNIZED
-                    track.confirmed = True
-                    track.label_box = list(submitted_box)
-                    track.label_frame_id = int(label_frame_id)
-                    track.identity = None
-                    track.candidates = []
-                return
-
-
-            from identity import aggregate_votes, decide_identity
-            from identity_lanes import LOCAL_ACCEPT, TEXT_MIN_SHARED, DecisionPolicy
-
-            mean_scores = aggregate_votes(track.observations)
-            scale = getattr(self.engine, "identity_scale", "inlier")
-            if scale == "cosine":
-                # score_crop only returns scores the gallery already accepted, so
-                # the aggregate must not be re-gated at COSINE_ACCEPT: that line is
-                # above the head's own 0.40 and refused matches the gallery had
-                # accepted. It still has to clear the gallery's own line, because
-                # aggregate_votes divides by the whole window - one rejected crop
-                # beside one accepted 0.45 would otherwise average to 0.225 and be
-                # labelled at a score the gallery calls unknown.
-                gallery = self.engine.identity_gallery()
-                line = 0.0
-                if gallery is not None:
-                    line = gallery.head_threshold if gallery.head_loaded() else gallery.threshold
-                decision = decide_identity(
-                    mean_scores, method=method, scale="cosine",
-                    min_score=line, min_confidence=line,
-                )
-            elif scale == "local":
-                # The local lane's order and value are different quantities: the
-                # order comes from its rank key, the value is the margin over the
-                # runner-up. `score_crop` returns that order, so it is used as-is;
-                # re-sorting by value would name a different SKU than the lane
-                # chose. The newest crop is the evidence, not the mean: averaging
-                # in an empty observation halves a good confidence below the
-                # accept line, which is the dilution the cosine branch guards too.
-                decision = DecisionPolicy(
-                    local_accept=float(os.environ.get("DORAYAKI_LOCAL_ACCEPT", LOCAL_ACCEPT)),
-                    text_min_shared=int(os.environ.get("DORAYAKI_TEXT_MIN_SHARED", TEXT_MIN_SHARED)),
-                    signatures=text_lane.signatures if text_lane else None,
-                ).decide(list(scores.items()), text_rank, text_read=text_read)
-            elif scale == "van":
-                # Van's contract on the stream: the aggregate's top SKU, always
-                # answered. Same scorer as `matcher`, no gate.
-                decision = decide_identity(mean_scores, method=method, scale="van")
-            else:
-                decision = decide_identity(mean_scores, method=method, scale=scale)
-            track.candidates = [
-                {"sku_id": row.get("sku_id"), "score": row.get("score")}
-                for row in (decision.get("ranked_candidates") or [])[:3]
-            ]
-            if decision.get("identity_level") == "accepted":
-                track.state = STATE_RECOGNIZED
-                track.confirmed = True
-                # The label belongs to the box the crop came from, not to wherever
-                # the track has moved by the time classification finishes.
-                track.label_box = list(submitted_box)
-                track.label_frame_id = int(label_frame_id)
-                track.drift_frames = 0
-                track.identity = {
-                    "sku_id": decision.get("best_sku"),
-                    "confidence": decision.get("confidence"),
-                    "score": decision.get("score"),
-                    "attempts": track.attempts,
-                }
-                self._remember_label(track, submitted_box, self._seq)
-            elif decision.get("provisional_sku"):
-                # Coverage-first: a detected product is shown its best candidate
-                # immediately, marked provisional, and stays re-classifiable so a
-                # later attempt can upgrade it to accepted or correct it. Van's
-                # always-answer behaviour, on the stronger stack. A blank box
-                # costs more than a guess the next frame may fix.
-                best = decision.get("provisional_sku")
-                track.state = STATE_RECOGNIZED
-                track.confirmed = False
-                track.label_box = list(submitted_box)
-                track.label_frame_id = int(label_frame_id)
-                track.drift_frames = 0
-                track.identity = {
-                    "sku_id": best,
-                    "confidence": decision.get("confidence"),
-                    "score": decision.get("score"),
-                    "attempts": track.attempts,
-                    "provisional": True,
-                }
-                self._remember_label(track, submitted_box, self._seq)
-            elif track.attempts >= self.settings.max_attempts:
-                if track.identity and track.identity.get("provisional"):
-                    # The budget is spent and a provisional label is already on
-                    # screen. Blanking it now would take back the only identity
-                    # the operator has seen, so the guess stays and the track
-                    # simply stops being re-classified.
-                    track.confirmed = True
-                    track.label_box = list(submitted_box)
-                    track.label_frame_id = int(label_frame_id)
-                else:
-                    track.state = STATE_UNRECOGNIZED
-                    track.confirmed = True
-                    track.label_box = list(submitted_box)
-                    track.label_frame_id = int(label_frame_id)
-                    track.drift_frames = 0
-                    track.identity = None
-            else:
-                track.state = STATE_DETECTED
-                track.confirmed = False
-                track.identity = None
-
-    # ------------------------------------------------------------------ payloads
-    def _track_payload(self, track: TrackState) -> Dict[str, Any]:
-        wire_state = track.state
-        if not track.confirmed and wire_state == STATE_DETECTED:
-            wire_state = STATE_UNCONFIRMED
-        identity: Dict[str, Any] = {
-            "state": wire_state,
-            "sku_id": None,
-            "identity_level": None,
-            "confidence": None,
-            "score": None,
-            "candidates": list(track.candidates),
-            "attempts": track.attempts,
-            "method": self.settings.method,
-            # Which evidence produced the newest observation. A client that only
-            # reads this can tell a label measured on the frame it sent from one
-            # measured on a crop it sent back.
-            "source": track.last_source,
-        }
-        product = None
-        if track.state == STATE_RECOGNIZED and track.identity:
-            identity["sku_id"] = track.identity.get("sku_id")
-            identity["confidence"] = track.identity.get("confidence")
-            identity["score"] = track.identity.get("score")
-            identity["identity_level"] = (
-                "provisional" if track.identity.get("provisional") else "accepted"
-            )
-            product = self._catalog_by_sku.get(identity["sku_id"] or "")
-        elif track.state in (STATE_NEEDS_CONFIRMATION, STATE_UNRECOGNIZED):
-            identity["identity_level"] = (
-                "ambiguous" if track.state == STATE_NEEDS_CONFIRMATION else "rejected"
-            )
-        payload = {
-            "track_id": track.track_id,
-            # The frame `label_box` is in. The result carrying the label arrives
-            # on a later frame, so a client that maps the box back to its own
-            # source image must use this id's offset, not the result's.
-            "label_frame_id": int(track.label_frame_id),
-            # box: smoothed track box, for drawing. raw_box: the detection box the
-            # label is anchored to, for cropping and for explaining label changes.
-            "box": list(track.box),
-            "raw_box": list(track.raw_box),
-            # The box the label was measured on, in frame coordinates. It is
-            # what the label is *about*: `box` has moved on by the time a
-            # classification finishes, so scoring a label against `box` scores
-            # it against wherever the track drifted to. An evaluator matching
-            # labels to ground truth needs this one.
-            "label_box": list(track.label_box or track.box),
-            "confidence": round(float(track.confidence), 4),
-            "first_seen_seq": track.first_seq,
-            "relabels": track.relabels,
-            "restarts": track.restarts,
-            "inherited": bool(track.identity and track.identity.get("inherited")),
-            "identity": identity,
-        }
-        if product is not None:
-            payload["product"] = {
-                "sku_id": product.get("sku_id"),
-                "product_name": product.get("product_name"),
-                "price": product.get("price"),
-            }
-        return payload
-
-    def _summary_locked(self) -> Dict[str, Any]:
-        counts = {
-            STATE_RECOGNIZED: 0,
-            STATE_NEEDS_CONFIRMATION: 0,
-            STATE_UNRECOGNIZED: 0,
-            STATE_DETECTED: 0,
-            STATE_IDENTIFYING: 0,
-            STATE_UNCONFIRMED: 0,
-        }
-        facings: Dict[str, int] = {}
-        for track in self._tracks.values():
-            if track.state == STATE_DETECTED and not track.confirmed:
-                counts[STATE_UNCONFIRMED] += 1
-                continue
-            state = track.state if track.state in counts else STATE_DETECTED
-            counts[state] += 1
-            if state == STATE_RECOGNIZED and track.identity:
-                sku_id = track.identity.get("sku_id")
-                if sku_id:
-                    facings[sku_id] = facings.get(sku_id, 0) + 1
-        return {
-            "in_view": len(self._tracks),
-            "recognized": counts[STATE_RECOGNIZED],
-            "identifying": counts[STATE_IDENTIFYING],
-            "needs_confirmation": counts[STATE_NEEDS_CONFIRMATION],
-            "unrecognized": counts[STATE_UNRECOGNIZED],
-            "detected": counts[STATE_DETECTED],
-            "unconfirmed": counts[STATE_UNCONFIRMED],
-            "empty_slots": len(self._slots),
-            "by_sku": [
-                {"sku_id": sku, "facings": count}
-                for sku, count in sorted(facings.items(), key=lambda kv: (-kv[1], kv[0]))
-            ],
-        }
-
-    def _stats_locked(self) -> Dict[str, Any]:
-        times = list(self._process_times)
-        fps = 0.0
-        if len(times) >= 2 and times[-1] > times[0]:
-            fps = round((len(times) - 1) / (times[-1] - times[0]), 2)
-        classify_p50 = None
-        if self._classify_times:
-            classify_p50 = round(statistics.median(self._classify_times) * 1000.0, 2)
-        return {
-            "crops": {
-                "requested": self._crops_requested,
-                "accepted": self._crops_accepted,
-                "rejected": self._crops_rejected,
-            },
-            "frames_received": self._frames_received,
-            "frames_processed": self._frames_processed,
-            "frames_dropped": self._frames_dropped,
-            "server_fps": fps,
-            "classify_pending": sum(track.in_flight for track in self._tracks.values()),
-            "classify_failures": self._classify_failures,
-            "classify_ms_p50": classify_p50,
-        }
-
-    # ------------------------------------------------------------------ empty slots
-    def _persist_slots(self, raw_slots: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
-        """Keep only slots seen repeatedly, with stable ids across frames."""
-        previous = list(self._slots)
-        matched_ids: Dict[int, int] = {}
-        used_previous = set()
-        for index, slot in enumerate(raw_slots):
-            best_id = None
-            best_iou = EMPTY_SLOT_IOU
-            for prev_index, prev in enumerate(previous):
-                if prev_index in used_previous:
-                    continue
-                score = _iou(slot["box"], prev["box"])
-                if score >= best_iou:
-                    best_iou = score
-                    best_id = prev.get("id")
-                    best_prev_index = prev_index
-            if best_id is not None:
-                matched_ids[index] = best_id
-                used_previous.add(best_prev_index)
-
-        assigned: List[Dict[str, Any]] = []
-        for index, slot in enumerate(raw_slots):
-            slot_id = matched_ids.get(index)
-            if slot_id is None:
-                self._slot_counter += 1
-                slot_id = f"empty_{self._slot_counter}"
-            entry = dict(slot)
-            entry["id"] = slot_id
-            assigned.append(entry)
-
-        self._slot_history.append(assigned)
-        if len(self._slot_history) < EMPTY_SLOT_MIN_HITS:
-            return []
-
-        counts: Dict[str, int] = collections.Counter()
-        latest_by_id: Dict[str, Dict[str, Any]] = {}
-        for frame_slots in self._slot_history:
-            for slot in frame_slots:
-                counts[slot["id"]] += 1
-                latest_by_id[slot["id"]] = slot
-        return [
-            latest_by_id[slot_id]
-            for slot_id, count in counts.items()
-            if count >= EMPTY_SLOT_MIN_HITS and slot_id in latest_by_id
-        ]
-
-    # ------------------------------------------------------------------ quality
-    def _quality(self, image: Any, tracks: Dict[int, TrackState]) -> Dict[str, Any]:
-        import cv2
-        import numpy as np
-
-        height, width = image.shape[:2]
-        target_width = 320
-        scale = target_width / float(width) if width > target_width else 1.0
-        small = cv2.resize(image, (max(1, int(width * scale)), max(1, int(height * scale))),
-                           interpolation=cv2.INTER_AREA)
-        gray = cv2.cvtColor(small, cv2.COLOR_BGR2GRAY)
-        sharpness = float(cv2.Laplacian(gray, cv2.CV_64F).var())
-        glare = float(np.mean(np.all(small > self.settings.glare_pixel, axis=2)))
-        heights = [state.raw_box[3] - state.raw_box[1] for state in tracks.values() if len(state.raw_box) == 4]
-        median_height = float(np.median(heights)) if heights else 0.0
-        too_far = bool(
-            heights
-            and (
-                median_height < self.settings.too_far_height_ratio * height
-                or sum(1 for h in heights if h < self.settings.min_crop_px) > len(heights) / 2
-            )
-        )
-        # A black frame has Laplacian 0. That is not blur; a real shelf is not black.
-        blur = sharpness < self._blur_threshold() and float(gray.mean()) > 1.0
-        hint = None
-        if glare > self.settings.glare_fraction:
-            hint = "glare"
-        elif blur:
-            hint = "blur"
-        elif too_far:
-            hint = "too_far"
-        quality = {
-            "sharpness": round(sharpness, 1),
-            "blur": blur,
-            "glare": round(glare, 4),
-            "too_far": too_far,
-            "hint": hint,
-        }
-        self._last_quality = quality
-        return quality
-
-    def _blur_threshold(self) -> float:
-        return float(os.environ.get("DORAYAKI_BLUR_THRESHOLD", "45"))
-
-    # ------------------------------------------------------------------ inspect
-    def inspect(self, track_id: int, method: Optional[str] = None) -> Dict[str, Any]:
-        with self._lock:
-            track = self._tracks.get(track_id)
-            image = self._latest_image
-            chosen_method = (method or self.settings.method).lower()
-            if track is None or image is None:
-                raise KeyError("TRACK_NOT_IN_VIEW")
-            box = list(track.raw_box)
-        return self.engine.inspect(image, box, method=chosen_method)
-
-    # ------------------------------------------------------------------ catalog cache
-    def refresh_catalog(self) -> None:
-        products = self.engine.catalog()
-        with self._lock:
-            self._catalog_by_sku = {row["sku_id"]: row for row in products}
-            self._catalog_version = self.engine.catalog_version()
-
-    # ------------------------------------------------------------------ price tags
-    def enable_tags(self, settings: Optional[Any] = None) -> bool:
-        """Attach a tag pass when an OCR engine is available. Returns whether it runs."""
-        ocr_engine = getattr(self.engine, "ocr_engine", None)
-        if ocr_engine is None or not getattr(ocr_engine, "available", lambda: False)():
-            return False
-        from tags import TagService, TagSettings
-
-        with self._lock:
-            self._tags = TagService(ocr_engine, settings=settings or TagSettings())
-        return True
-
-    def _note_frame_shift(self, image: Any) -> Tuple[float, float]:
-        """Shelf position of this frame's origin. Phase correlation only.
-
-        Register and origin update share one per-session gate, so overlapping
-        workers cannot reorder the previous-frame baseline or publish a later
-        origin first. Correlation stays outside the session lock and outside
-        OCR. A failed registration keeps the last origin rather than inventing
-        a jump. The content shift moves the origin the other way, same sign as
-        ShelfMap.
-        """
-        with self._shift_gate:
-            if self._shift_map is None:
-                from shelf_map import ShelfMap
-
-                self._shift_map = ShelfMap()
-            shift = self._shift_map.register(image)
-            if shift is not None:
-                self._frame_origin[0] -= float(shift[0])
-                self._frame_origin[1] -= float(shift[1])
-            return (self._frame_origin[0], self._frame_origin[1])
-
-    def _run_tag_passes(self, image: Any, bands: Sequence[Any]) -> None:
-        """Submit tag passes to the engine's tag worker (never the frame loop)."""
-        service = self._tags
-        if service is None or not bands:
-            return
-        for band in bands:
-            try:
-                future = self.engine.tag_executor.submit(service.run_pass, image, band)
-            except RuntimeError:  # executor shut down
-                return
-            future.add_done_callback(self._on_tag_pass_done)
-
-    def _on_tag_pass_done(self, future: Any) -> None:
-        try:
-            future.result()
-        except Exception as error:
-            # A failed OCR pass must not disturb the stream, but it must not be
-            # invisible either: counted and reported through stats(), so an
-            # operator can tell a broken OCR from a shelf with no tags.
-            self._tags.note_pass_failure(error)
-            LOGGER.warning("tag pass failed: %s", error)
-
-    def _apply_tags(
-        self,
-        seq: int,
-        track_payloads: List[Dict[str, Any]],
-        bands: Sequence[Any],
-        shelf_offset: Sequence[float],
-        image_shape: Sequence[int],
-    ) -> Optional[Dict[str, Any]]:
-        """Attach tag + reconciliation to each track and compute compliance.
-
-        Must be called with the session lock held (it reads the tag cache under its
-        own lock, which is never taken the other way round). The origin and shape
-        are this frame's, not whatever `plan` last stored.
-        """
-        service = self._tags
-        if service is None or not self.settings.tags:
-            return None
-        from reconcile import ComplianceTracker, evaluate_compliance, reconcile, summarize_findings
-
-        tag_by_track, all_tags, counts = service.attach(
-            track_payloads, shelf_offset=shelf_offset, image_shape=image_shape
-        )
-        reconciliations: Dict[int, Dict[str, Any]] = {}
-        for payload in track_payloads:
-            track_id = int(payload["track_id"])
-            tag = tag_by_track.get(track_id)
-            identity = payload.get("identity")
-            product = self._catalog_by_sku.get((identity or {}).get("sku_id") or "")
-            decision = reconcile(identity, tag, product)
-            if tag is not None:
-                payload["price_tag"] = tag
-            if decision is not None:
-                payload["reconciliation"] = decision
-                reconciliations[track_id] = decision
-
-        if self._compliance_tracker is None:
-            self._compliance_tracker = ComplianceTracker()
-        findings = evaluate_compliance(
-            track_payloads, tag_by_track, all_tags, bands, reconciliations
-        )
-        stable = self._compliance_tracker.update(findings)
-        summary = summarize_findings(stable)
-        rows = [
-            {
-                "row_index": finding.row_index,
-                "rule": finding.rule,
-                "box": finding.box,
-                "missing": finding.count,
-                "skus": finding.skus,
-            }
-            for finding in stable
-            if finding.severity == "violation"
-        ]
-        self._last_compliance = {"violations": summary["violations"], "by_rule": summary["by_rule"], "rows": rows,
-                                 "hints": summary["hints"]}
-        return {"tags": counts, "compliance": self._last_compliance}
-
-    def snapshot(self) -> Dict[str, Any]:
-        with self._lock:
-            return {
-                "session_id": self.session_id,
-                "config": self.config(),
-                "tracks": len(self._tracks),
-                "seq": self._seq,
-                "stats": self._stats_locked(),
-                "quality": dict(self._last_quality),
-                "tags": self._tags.stats() if self._tags else None,
-                "compliance": dict(self._last_compliance),
-                "created_at": self._created_at,
-            }
diff --git a/recognization/code/ocr.py b/recognization/code/ocr.py
deleted file mode 100644
index af2823f..0000000
--- a/recognization/code/ocr.py
+++ /dev/null
@@ -1,235 +0,0 @@
-"""
-OCR engines for the recognition server.
-
-One interface, several backends, chosen by availability and configuration - the
-same shape as the app kit's platform bridges. The engine only answers "what text
-is in this image and where"; everything tag-specific (band geometry, clustering,
-field parsing, association) lives in tags.py.
-
-Backends:
-
-- VisionOcr   macOS, via pyobjc/Vision. Measured 182-246 ms per rail band.
-- FakeOcr     deterministic lines for tests and offline demos.
-- (RapidOCR)  deliberately not shipped: the owner chose the macOS backend, and the
-              interface keeps a cross-platform engine a drop-in later.
-
-The engine never guesses: a line that is not recognized is simply absent, and the
-caller decides what absence means (see the compliance rules, which fail closed on
-unreadable evidence rather than reporting a violation).
-"""
-
-from __future__ import annotations
-
-import os
-import time
-from dataclasses import dataclass, field
-from typing import Any, Dict, List, Optional, Sequence, Tuple
-
-DEFAULT_LANGUAGES = ("ja-JP", "en-US")
-
-
-@dataclass
-class OcrLine:
-    """One recognized text line, in the pixel space of the image it was read from."""
-
-    text: str
-    confidence: float
-    box: List[int]  # [x1, y1, x2, y2], origin top-left
-
-    def as_dict(self) -> Dict[str, Any]:
-        return {"text": self.text, "confidence": round(float(self.confidence), 3), "box": list(self.box)}
-
-
-@dataclass
-class OcrResult:
-    lines: List[OcrLine] = field(default_factory=list)
-    engine: str = "none"
-    elapsed_ms: float = 0.0
-
-    def as_dict(self) -> Dict[str, Any]:
-        return {
-            "engine": self.engine,
-            "elapsed_ms": round(self.elapsed_ms, 1),
-            "lines": [line.as_dict() for line in self.lines],
-        }
-
-
-class OcrUnavailable(RuntimeError):
-    """Raised when a backend is asked to read but cannot run here."""
-
-
-class OcrEngine:
-    """Base class: subclasses implement read()."""
-
-    name = "none"
-
-    def available(self) -> bool:  # pragma: no cover - overridden
-        return False
-
-    def read(
-        self,
-        image: Any,
-        *,
-        languages: Sequence[str] = DEFAULT_LANGUAGES,
-        fast: bool = False,
-    ) -> OcrResult:  # pragma: no cover - overridden
-        raise OcrUnavailable(f"engine '{self.name}' is not available")
-
-
-class VisionOcr(OcrEngine):
-    """macOS Vision text recognition through pyobjc."""
-
-    name = "vision"
-
-    def __init__(self, languages: Sequence[str] = DEFAULT_LANGUAGES):
-        self.languages = list(languages)
-        self._vision = None
-        self._quartz = None
-        self._load_error: Optional[str] = None
-        self._load()
-
-    def _load(self) -> None:
-        if os.uname().sysname != "Darwin":  # pragma: no cover - platform guard
-            self._load_error = "not macOS"
-            return
-        try:
-            import Quartz  # noqa: F401
-            import Vision  # noqa: F401
-
-            self._quartz = Quartz
-            self._vision = Vision
-        except Exception as exc:  # pragma: no cover - import guard
-            self._load_error = str(exc)
-
-    def available(self) -> bool:
-        return self._vision is not None and self._quartz is not None
-
-    def read(
-        self,
-        image: Any,
-        *,
-        languages: Optional[Sequence[str]] = None,
-        fast: bool = False,
-    ) -> OcrResult:
-        if not self.available():
-            raise OcrUnavailable(f"Vision is unavailable here ({self._load_error})")
-
-        import cv2
-        import numpy as np
-        from Foundation import NSData  # type: ignore
-
-        started = time.perf_counter()
-        height, width = image.shape[:2]
-        ok, encoded = cv2.imencode(".png", image)
-        if not ok:
-            return OcrResult(engine=self.name, elapsed_ms=(time.perf_counter() - started) * 1000.0)
-        raw = encoded.tobytes()
-        data = NSData.dataWithBytes_length_(raw, len(raw))
-        source = self._quartz.CGImageSourceCreateWithData(data, None)
-        cg_image = self._quartz.CGImageSourceCreateImageAtIndex(source, 0, None)
-
-        request = self._vision.VNRecognizeTextRequest.alloc().init()
-        request.setRecognitionLevel_(
-            self._vision.VNRequestTextRecognitionLevelFast
-            if fast
-            else self._vision.VNRequestTextRecognitionLevelAccurate
-        )
-        request.setRecognitionLanguages_(list(languages or self.languages))
-        request.setUsesLanguageCorrection_(False)
-        handler = self._vision.VNImageRequestHandler.alloc().initWithCGImage_options_(cg_image, None)
-        performed, error = handler.performRequests_error_([request], None)
-        elapsed = (time.perf_counter() - started) * 1000.0
-        if not performed:
-            raise OcrUnavailable(f"Vision request failed: {error}")
-
-        lines: List[OcrLine] = []
-        for observation in request.results() or []:
-            candidates = observation.topCandidates_(1)
-            if not candidates:
-                continue
-            top = candidates[0]
-            box = observation.boundingBox()
-            x1 = box.origin.x * width
-            x2 = (box.origin.x + box.size.width) * width
-            # Vision uses a bottom-left origin; the rest of the system is top-left.
-            y1 = (1.0 - box.origin.y - box.size.height) * height
-            y2 = (1.0 - box.origin.y) * height
-            lines.append(
-                OcrLine(
-                    text=str(top.string()),
-                    confidence=float(top.confidence()),
-                    box=[int(round(x1)), int(round(y1)), int(round(x2)), int(round(y2))],
-                )
-            )
-        return OcrResult(lines=lines, engine=self.name, elapsed_ms=elapsed)
-
-
-class FakeOcr(OcrEngine):
-    """Deterministic engine for tests: returns whatever it was told to.
-
-    Accepts either a list of OcrLine (returned as-is, in the caller's pixel space)
-    or a callable(image) -> list[OcrLine].
-    """
-
-    name = "fake"
-
-    def __init__(self, lines: Optional[Sequence[OcrLine]] = None, reader=None):
-        self._lines = list(lines or [])
-        self._reader = reader
-        self.calls = 0
-        self.last_image_shape: Optional[Tuple[int, int]] = None
-
-    def available(self) -> bool:
-        return True
-
-    def read(self, image: Any, *, languages: Sequence[str] = DEFAULT_LANGUAGES, fast: bool = False) -> OcrResult:
-        self.calls += 1
-        self.last_image_shape = tuple(image.shape[:2])
-        lines = self._reader(image) if self._reader is not None else [OcrLine(**vars(line)) for line in self._lines]
-        return OcrResult(lines=list(lines), engine=self.name, elapsed_ms=0.0)
-
-
-class NullOcr(OcrEngine):
-    """Explicitly no OCR: used when the feature is switched off."""
-
-    name = "none"
-
-    def available(self) -> bool:
-        return False
-
-    def read(self, image: Any, *, languages: Sequence[str] = DEFAULT_LANGUAGES, fast: bool = False) -> OcrResult:
-        return OcrResult(lines=[], engine=self.name, elapsed_ms=0.0)
-
-
-def build_ocr_engine(kind: Optional[str] = None) -> OcrEngine:
-    """Pick a backend. DORAYAKI_OCR = vision | fake | none (default: vision on macOS)."""
-    kind = (kind or os.environ.get("DORAYAKI_OCR") or "auto").lower()
-    if kind == "none":
-        return NullOcr()
-    if kind == "fake":
-        return FakeOcr()
-    if kind in ("auto", "vision"):
-        engine = VisionOcr()
-        if engine.available():
-            return engine
-        if kind == "vision":
-            raise OcrUnavailable(f"Vision requested but unavailable: {engine._load_error}")
-    return NullOcr()
-
-
-if __name__ == "__main__":  # smoke: python code/ocr.py <image> [<image> ...]
-    import sys
-
-    import cv2
-
-    engine = build_ocr_engine()
-    print(f"engine: {engine.name} (available: {engine.available()})")
-    for path in sys.argv[1:]:
-        image = cv2.imread(path)
-        if image is None:
-            print(f"{path}: cannot read")
-            continue
-        result = engine.read(image)
-        print(f"--- {path}  {result.elapsed_ms:.0f} ms, {len(result.lines)} lines ---")
-        for line in result.lines[:12]:
-            print(f"  {line.confidence:.2f}  {line.text}  {line.box}")
diff --git a/recognization/code/orb.py b/recognization/code/orb.py
new file mode 100644
index 0000000..e69de29
diff --git a/recognization/code/pipeline.py b/recognization/code/pipeline.py
new file mode 100644
index 0000000..cb766d2
--- /dev/null
+++ b/recognization/code/pipeline.py
@@ -0,0 +1,181 @@
+"""
+End-to-End Supermarket Shelf Recognition Pipeline
+Integrates:
+1. YOLO object detector for shelf product localization
+2. SURF / ORB / BRISK / Hybrid local feature matcher for classification
+3. Stock CSV inventory lookup for real-time pricing and stock quantities
+"""
+
+import os
+import sys
+import cv2
+import numpy as np
+from typing import Dict, Optional, Any
+
+current_dir = os.path.dirname(os.path.abspath(__file__))
+if current_dir not in sys.path:
+    sys.path.insert(0, current_dir)
+
+from detector import ShelfProductDetector
+from feature_matcher import ProductFeatureMatcher
+from inventory import InventoryManager
+
+
+class ShelfRecognitionPipeline:
+    """Master pipeline managing interactive supermarket shelf recognition."""
+
+    def __init__(
+        self,
+        weights_path: str = "runs/detect/train/weights/best.pt",
+        stock_dir: str = "stock",
+        stock_csv: str = "stock.csv"
+    ):
+        print("[Pipeline] Initializing Inventory Manager...")
+        self.inventory = InventoryManager(stock_dir=stock_dir, csv_filename=stock_csv)
+
+        print("[Pipeline] Initializing Feature Matcher...")
+        self.matcher = ProductFeatureMatcher(stock_dir=stock_dir)
+
+        print("[Pipeline] Initializing Shelf Detector...")
+        self.detector = ShelfProductDetector(weights_path=weights_path)
+
+        self.current_image_path: Optional[str] = None
+        self.current_image: Optional[np.ndarray] = None
+        self.current_detections: list = []
+        self.current_empty_slots: list = []
+
+    def set_active_shelf_image(self, image_input: Any) -> Dict[str, Any]:
+        """Loads and runs detection on a new shelf image."""
+        if isinstance(image_input, str):
+            self.current_image_path = image_input
+            self.current_image = cv2.imread(image_input)
+        else:
+            self.current_image_path = "memory_buffer"
+            self.current_image = image_input
+
+        if self.current_image is None:
+            return {"status": "error", "message": "Invalid or unreadable image"}
+
+        det_result = self.detector.detect(self.current_image)
+        self.current_detections = det_result.get("boxes", [])
+
+        # Detect empty shelf spaces (out-of-stock gaps)
+        self.current_empty_slots = self.detector.detect_empty_slots(
+            self.current_image.shape, self.current_detections
+        )
+
+        return {
+            "status": "success",
+            "image_shape": self.current_image.shape,
+            "total_detected": len(self.current_detections),
+            "boxes": self.current_detections,
+            "empty_slots": self.current_empty_slots
+        }
+
+    def process_click(
+        self,
+        x: int,
+        y: int,
+        method: str = "hybrid",
+        tolerance_px: int = 40
+    ) -> Dict[str, Any]:
+        """
+        Handles user click at coordinate (x, y) on the shelf image:
+        1. Identifies the bounding box
+        2. Crops product ROI
+        3. Classifies via SURF/ORB/BRISK/Hybrid matching
+        4. Queries inventory for price, quantity, and metadata
+        """
+        if self.current_image is None:
+            return {"status": "error", "message": "No active shelf image loaded."}
+
+        target_box = self.detector.find_box_at_point(
+            x, y, self.current_detections, tolerance_px=tolerance_px
+        )
+
+        if target_box is None:
+            # Check if user clicked an empty shelf space
+            for slot in getattr(self, "current_empty_slots", []):
+                sx1, sy1, sx2, sy2 = slot["box"]
+                if (sx1 - 10 <= x <= sx2 + 10) and (sy1 - 10 <= y <= sy2 + 10):
+                    return {
+                        "status": "empty_slot",
+                        "clicked_point": (x, y),
+                        "empty_slot": slot,
+                        "message": f"Chỗ trống trên kệ (Khu vực hàng hết / Row {slot['row_index']}). Ước tính thiếu ~{slot['missing_items_est']} sản phẩm."
+                    }
+
+            return {
+                "status": "not_found",
+                "clicked_point": (x, y),
+                "message": f"No product detected near coordinate ({x}, {y}). Please click inside a product packaging."
+            }
+
+        # Crop ROI
+        crop = self.detector.crop_roi(self.current_image, target_box["box"])
+
+        # Classify with selected method
+        cls_result = self.matcher.classify_crop(crop, method=method)
+        if cls_result.get("status") != "success":
+            return {
+                "status": "classification_failed",
+                "target_box": target_box,
+                "message": cls_result.get("message", "Feature matching failed.")
+            }
+
+        best_sku = cls_result["best_sku"]
+        product_info = self.inventory.get_product(best_sku)
+
+        # Generate match visualization
+        vis_match = self.matcher.generate_match_visualization(crop, best_sku, method=method)
+
+        # Generate annotated shelf image highlighting the selected item
+        annotated_shelf = self.detector.draw_detections(
+            self.current_image, self.current_detections, selected_box_id=target_box["id"]
+        )
+
+        return {
+            "status": "success",
+            "clicked_point": (x, y),
+            "target_box": target_box,
+            "classification": {
+                "method": method,
+                "matched_sku": best_sku,
+                "confidence": cls_result["confidence"],
+                "score": cls_result["score"],
+                "top_candidates": cls_result["ranked_candidates"],
+            },
+            "inventory": product_info,
+            "crop_image": crop,
+            "annotated_shelf": annotated_shelf,
+            "match_visualization": vis_match
+        }
+
+
+if __name__ == "__main__":
+    import argparse
+    parser = argparse.ArgumentParser(description="Test Shelf Recognition Pipeline")
+    parser.add_argument("--image", type=str, default="runs/detect/train/train_batch0.jpg")
+    parser.add_argument("--method", type=str, default="hybrid", choices=["surf", "orb", "brisk", "hybrid"])
+    args = parser.parse_args()
+
+    pipeline = ShelfRecognitionPipeline()
+    print(f"Loading image: {args.image}")
+    init_res = pipeline.set_active_shelf_image(args.image)
+    print(f"Total detected items: {init_res['total_detected']}")
+
+    if init_res['boxes']:
+        # Simulate click on the center of the first detected product
+        first_box = init_res['boxes'][0]
+        cx, cy = first_box['center']
+        print(f"Simulating click on Product #{first_box['id']} at ({cx}, {cy})...")
+        res = pipeline.process_click(cx, cy, method=args.method)
+        print("\n--- Recognition & Inventory Result ---")
+        print(f"Status: {res['status']}")
+        if res['status'] == 'success':
+            prod = res['inventory']
+            print(f"Matched SKU: {res['classification']['matched_sku']} (Score: {res['classification']['score']})")
+            print(f"Product Name: {prod['product_name']}")
+            print(f"Price: {prod['price']} JPY")
+            print(f"In Stock: {prod['number_on']} units (Received: {prod['number_in']}, Sold: {prod['number_out']})")
+            print(f"Expiration Date: {prod['expiration_date']}")
diff --git a/recognization/code/rebuild_catalog.py b/recognization/code/rebuild_catalog.py
deleted file mode 100644
index c146453..0000000
--- a/recognization/code/rebuild_catalog.py
+++ /dev/null
@@ -1,629 +0,0 @@
-"""Rebuild a store catalog from packshots, refusing unsafe enrollment.
-
-The LOPIA catalog shipped with views cut from the test shelf itself: 71-119 px
-wide, one 18 px sliver, duplicate crops, and five SKUs with no view at all. That
-is why every dark package collapsed onto one SKU and why the head was trained on
-its own exam.
-
-This rebuilds a catalog directory from named packshots and refuses, per view:
-
-  * a view shorter than MIN_HEIGHT, narrower than MIN_WIDTH, or shorter than
-    PYRAMID_FLOOR (a source under it cannot be stored as the protocol's
-    200/400/800 px pyramid, so its scores would not be comparable with the rest),
-  * a view whose capture session appears in --eval-manifest,
-  * a packshot whose catalog name is not supported by its OCR text,
-  * a view that is a near-duplicate of another view of the same SKU,
-  * a SKU left with fewer than --min-views distinct views.
-
-Every enrolled image must have a CSV row; a SKU with an image and no row refuses
-the build. CSV rows with no image are kept and reported as text-only coverage
-gaps, because the text lane names and vetoes those products from the CSV alone.
-Capture session ids come from the source manifest (``manifest.json`` beside the
-images by default), whose image entries use ``file`` plus
-``capture_session_id`` or ``session_id``. Pixel equality is not a leak test:
-re-cut crops pass it, so leakage is refused by capture session instead.
-
-Two checks report rather than refuse, because both are properties of the
-catalog as a whole: cross-SKU near-duplicates are flagged as
-text-discriminated siblings (the near-unknown case only package text
-separates), and ``--shelf-tags`` turns the completeness rule into a live-ready
-verdict. Without that list the verdict is ``unknown``, not a guess.
-
-    python code/rebuild_catalog.py --source /tmp/lopia-clean \
-        --output stock-lopia-clean --csv stock-lopia/stock.csv
-    python code/rebuild_catalog.py --source /tmp/lopia-clean \
-        --output stock-lopia-clean --csv stock-lopia/stock.csv --replace
-
-Views are named <sku>.jpg, <sku>_2.jpg, ... The CSV is copied byte-for-byte, so
-text-only products stay in the catalog and its content hash does not move.
-"""
-
-from __future__ import annotations
-
-import argparse
-import csv
-import json
-import os
-import shutil
-import sys
-import tempfile
-import unicodedata
-from typing import Any, Dict, List, Optional, Sequence, Set, Tuple
-
-current_dir = os.path.dirname(os.path.abspath(__file__))
-if current_dir not in sys.path:
-    sys.path.insert(0, current_dir)
-
-import cv2  # noqa: E402
-import numpy as np  # noqa: E402
-
-from ocr import OcrUnavailable, build_ocr_engine  # noqa: E402
-
-# A view narrower than this cannot carry the logo and text geometry that tells
-# two fine-grained products apart. The shipped LOPIA views were 71-119 px.
-MIN_WIDTH = 120
-# The enrollment protocol's floor: a reference view under 160 px tall carries no
-# text geometry (facings under 60 px wide failed every measured lane).
-MIN_HEIGHT = 160
-# The name/OCR gate, re-measured on the shipped packshots with the strict name
-# side in place, each image read four ways and scored against all 12 catalog
-# names: correct packshots 0.9167-1.0000 (seven read their whole name; l11's
-# read omits "マヨネーズ" and is the floor), the wrong-product packshot in
-# references/rejected/ 0.0476-0.5833. 0.75 is the midpoint: 0.167 of headroom
-# above every correct packshot and 0.167 below the wrong-product ceiling, so
-# OCR noise on either side does not move a verdict. Siblings are not this
-# gate's job - the コクうま packshot scores 0.85 against the マヨネーズ name,
-# which is what text_discriminated_siblings flags, not a gap a threshold closes.
-MIN_NAME_OCR = 0.75
-# Vision drops small print at native scale: the l11 packshot reads ハーフ only
-# when upscaled (0.57 -> 0.79). Reading at 2x is what makes the check about the
-# package rather than about Vision's recall.
-OCR_SCALE = 2.0
-# Two views of one SKU this similar are the same photograph counted twice.
-DUPLICATE_COSINE = 0.98
-
-# The enrollment protocol's pyramid target: a source at least this tall can be
-# stored as a 200/400/800 px pyramid, so a view under it cannot be scored
-# against the rest. Refused, not reported: the l09 defect was a 305 px view
-# whose inlier scores (85-129) dwarfed every other reference (10-40) and it won
-# facings it should not, and a report is exactly the kind of thing that gets
-# read once and then ignored. The fix is re-sourcing, which is a data task
-# this tool should not let through the door in the first place.
-PYRAMID_FLOOR = 800
-
-# Official pack-shot pages publish a single front view per JAN, so a catalog
-# built from them runs at 1 and says so; the floor exists to catch accidental
-# single-view enrollment from shelf crops, where several views are available.
-MIN_VIEWS = 2
-IMAGE_EXTENSIONS = (".jpg", ".jpeg", ".png")
-SESSION_KEYS = ("capture_session_id", "session_id")
-SESSION_LIST_KEYS = ("sessions", "session_ids", "evaluation_sessions")
-
-
-class RebuildError(Exception):
-    """Refused input. The destination, if it existed, is unchanged."""
-
-
-def sku_of(filename: str) -> str:
-    """`sku_l01_2.jpg` -> `sku_l01`; `sku_l01.jpg` -> `sku_l01`."""
-    stem = os.path.splitext(filename)[0]
-    parts = stem.split("_")
-    if len(parts) >= 2 and parts[0] == "sku":
-        return "_".join(parts[:2])
-    return stem
-
-
-def view_rank(filename: str) -> int:
-    """Order views so the primary view sorts first: `sku_l01.jpg` before `_2`."""
-    stem = os.path.splitext(filename)[0]
-    parts = stem.split("_")
-    return int(parts[2]) if len(parts) > 2 and parts[2].isdigit() else 1
-
-
-def cosine(left: np.ndarray, right: np.ndarray) -> float:
-    a = left.astype(np.float32).ravel()
-    b = right.astype(np.float32).ravel()
-    denom = float(np.linalg.norm(a) * np.linalg.norm(b))
-    return float((a * b).sum() / denom) if denom else 0.0
-
-
-def upscale(image: np.ndarray) -> np.ndarray:
-    """Enlarge for OCR only; the stored view keeps its own pixels."""
-    return cv2.resize(image, None, fx=OCR_SCALE, fy=OCR_SCALE, interpolation=cv2.INTER_CUBIC)
-
-
-def normalize_text(text: str) -> str:
-    """NFKC, uppercase, small kana folded, alnum and `%` kept."""
-    value = unicodedata.normalize("NFKC", text).upper()
-    # Vision varies between small and full-size kana, and Kewpie prints
-    # キユーピー with a small ユ; folding both keeps the comparison about the
-    # product name rather than the typesetter.
-    value = value.translate(str.maketrans("ッャュョァィゥェォヮヵヶ", "ツヤユヨアイウエオワカケ"))
-    value = value.replace("キユーピー", "キユピ").replace("キューピー", "キユピ")
-    return "".join(char for char in value if char.isalnum() or char == "%")
-
-
-def signature_bigrams(text: str) -> Set[str]:
-    """Character pairs of a catalog name, never across a word boundary.
-
-    A pair that spans two words is not a substring of either one, so it can
-    only ever be noise - and expensive noise, because it is rare across the
-    catalog and IDF therefore weights it as highly distinctive. "キユーピー
-    アマニ油" used to contribute "ピア", which let the garbage read
-    "キユピアク5C" be accepted as that row.
-    """
-    pairs: Set[str] = set()
-    for token in text.split():
-        value = normalize_text(token)
-        pairs.update(value[index:index + 2] for index in range(len(value) - 1))
-    return pairs
-
-
-def bigrams(text: str) -> Set[str]:
-    """Character pairs of a read, across whitespace.
-
-    Deliberately not `signature_bigrams`: OCR puts spaces where the package
-    has none, so "ピュア セレクト" has to keep matching the catalog's
-    "ピュアセレクト". The catalog name is authoritative about where its words
-    end; a read is noisy evidence about the same characters, and dropping its
-    spaces is the only way the two can meet. The read is the side that gets
-    the benefit of the doubt, so the fake-pair problem is fixed where it
-    originates - in the signature - rather than by refusing to match.
-    """
-    value = normalize_text(text)
-    return {value[index:index + 2] for index in range(len(value) - 1)}
-
-
-def name_ocr_similarity(name: str, ocr_text: str) -> float:
-    """Share of the catalog name's bigrams that the OCR text explains."""
-    name_bigrams = signature_bigrams(name)
-    return len(name_bigrams & bigrams(ocr_text)) / max(1, len(name_bigrams))
-
-
-def best_orientation_text(ocr_engine: Any, image: np.ndarray, name: str = "") -> str:
-    """OCR text from whichever of the four right-angle rotations matches best.
-
-    A packshot's identifying text is not always horizontal: the やまや
-    めんたいマヨネーズ packshot reads 明太手屋さんが only at 270 degrees, and
-    the upright pass returns three stray characters. Checking one orientation
-    refused a correct image, which is the worst failure this check has - it
-    rejects good data while claiming to validate it.
-
-    The winner is the rotation that explains the most of the catalog name, not
-    the one that reads the most characters: the upright pass on that packshot
-    returns stray kana that concatenate longer than the real read, so length
-    picks the wrong one. With no name to compare against, length is the only
-    signal left.
-    """
-    rotations = (
-        image,
-        cv2.rotate(image, cv2.ROTATE_90_CLOCKWISE),
-        cv2.rotate(image, cv2.ROTATE_180),
-        cv2.rotate(image, cv2.ROTATE_90_COUNTERCLOCKWISE),
-    )
-    reads = [" ".join(line.text for line in ocr_engine.read(upscale(rotated)).lines)
-             for rotated in rotations]
-    if name:
-        return max(reads, key=lambda text: name_ocr_similarity(name, text))
-    return max(reads, key=lambda text: len(text.strip()))
-
-
-def collect(source: str) -> Dict[str, List[str]]:
-    by_sku: Dict[str, List[str]] = {}
-    for name in sorted(os.listdir(source)):
-        if os.path.splitext(name)[1].lower() not in IMAGE_EXTENSIONS:
-            continue
-        by_sku.setdefault(sku_of(name), []).append(name)
-    for views in by_sku.values():
-        views.sort(key=view_rank)
-    return by_sku
-
-
-def read_catalog(csv_path: str) -> Tuple[List[str], List[Dict[str, str]], Dict[str, str]]:
-    """Columns, rows, and SKU -> product_name. Refuses a malformed catalog."""
-    with open(csv_path, newline="", encoding="utf-8-sig") as handle:
-        reader = csv.DictReader(handle)
-        fieldnames = list(reader.fieldnames or [])
-        rows = list(reader)
-    if "SKU_ID" not in fieldnames or "product_name" not in fieldnames:
-        raise RebuildError(f"{csv_path}: needs SKU_ID and product_name columns")
-    names: Dict[str, str] = {}
-    duplicates: Set[str] = set()
-    for row in rows:
-        sku = (row.get("SKU_ID") or "").strip()
-        if not sku:
-            raise RebuildError(f"{csv_path}: a row has an empty SKU_ID")
-        if sku in names:
-            duplicates.add(sku)
-        names[sku] = (row.get("product_name") or "").strip()
-    if duplicates:
-        raise RebuildError("duplicate CSV row(s): " + ", ".join(sorted(duplicates)))
-    return fieldnames, rows, names
-
-
-def _session_values(payload: Any) -> Set[str]:
-    """Every session id anywhere in a manifest, whatever its shape."""
-    found: Set[str] = set()
-    if isinstance(payload, dict):
-        for key, value in payload.items():
-            if key in SESSION_KEYS and isinstance(value, (str, int)):
-                found.add(str(value))
-            elif key in SESSION_LIST_KEYS and isinstance(value, list):
-                for item in value:
-                    found.update([str(item)] if isinstance(item, (str, int)) else _session_values(item))
-            elif isinstance(value, (dict, list)):
-                found.update(_session_values(value))
-    elif isinstance(payload, list):
-        for item in payload:
-            found.update(_session_values(item))
-    return found
-
-
-def load_eval_sessions(path: str) -> Set[str]:
-    """Sessions an evaluation used. A manifest naming none is refused, not trusted."""
-    with open(path, encoding="utf-8") as handle:
-        sessions = _session_values(json.load(handle))
-    if not sessions:
-        raise RebuildError(f"{path}: evaluation manifest names no capture session")
-    return sessions
-
-
-def load_capture_sessions(path: Optional[str]) -> Dict[str, str]:
-    """Image basename -> capture session, from an enrollment manifest."""
-    if not path:
-        return {}
-    with open(path, encoding="utf-8") as handle:
-        payload = json.load(handle)
-    if isinstance(payload, dict):
-        entries = payload.get("images", [])
-        default = payload.get("capture_session_id") or payload.get("session_id")
-    else:
-        entries, default = payload, None
-    if not isinstance(entries, list):
-        raise RebuildError(f"{path}: 'images' must be a list")
-    sessions: Dict[str, str] = {}
-    for entry in entries:
-        if not isinstance(entry, dict):
-            continue
-        filename = entry.get("file") or entry.get("filename") or entry.get("image")
-        session = entry.get("capture_session_id") or entry.get("session_id") or default
-        if filename and session is not None:
-            sessions[os.path.basename(str(filename))] = str(session)
-    return sessions
-
-
-def audit(
-    source: str,
-    min_views: int = MIN_VIEWS,
-    names: Optional[Dict[str, str]] = None,
-    ocr_engine: Any = None,
-    capture_sessions: Optional[Dict[str, str]] = None,
-    eval_sessions: Optional[Set[str]] = None,
-) -> Tuple[Dict[str, List[str]], List[Dict[str, str]]]:
-    """Keep the views that pass, and report every refusal with its reason."""
-    kept: Dict[str, List[str]] = {}
-    refused: List[Dict[str, str]] = []
-    capture_sessions = capture_sessions or {}
-    eval_sessions = eval_sessions or set()
-    for sku, views in sorted(collect(source).items()):
-        accepted: List[Tuple[str, np.ndarray]] = []
-        for name in views:
-            image = cv2.imread(os.path.join(source, name))
-            if image is None:
-                refused.append({"sku": sku, "view": name, "reason": "unreadable"})
-                continue
-            # Leakage first: a view from an evaluation session is refused even if
-            # it would fail another check, so the leak is never hidden by one.
-            session = capture_sessions.get(name)
-            if session and session in eval_sessions:
-                refused.append({"sku": sku, "view": name,
-                                "reason": f"capture session {session} is in the evaluation manifest"})
-                continue
-            if image.shape[0] < MIN_HEIGHT:
-                refused.append({"sku": sku, "view": name,
-                                "reason": f"{image.shape[0]}px tall, below the {MIN_HEIGHT}px floor"})
-                continue
-            if image.shape[1] < MIN_WIDTH:
-                refused.append({"sku": sku, "view": name,
-                                "reason": f"{image.shape[1]}px wide, below the {MIN_WIDTH}px floor"})
-                continue
-            if image.shape[0] < PYRAMID_FLOOR:
-                refused.append({"sku": sku, "view": name,
-                                "reason": f"{image.shape[0]}px tall, below the {PYRAMID_FLOOR}px "
-                                          "pyramid floor; its scores would not be comparable "
-                                          "with the rest - re-source this view"})
-                continue
-            if ocr_engine is not None and names is not None:
-                try:
-                    text = best_orientation_text(ocr_engine, image, names.get(sku, ""))
-                except OcrUnavailable as error:
-                    print(f"OCR skipped for {name}: {error}", file=sys.stderr)
-                else:
-                    similarity = name_ocr_similarity(names.get(sku, ""), text)
-                    if similarity < MIN_NAME_OCR:
-                        refused.append({"sku": sku, "view": name,
-                                        "reason": f"name/OCR {similarity:.2f} below {MIN_NAME_OCR:.2f}: "
-                                                  f"{text[:100] or '<no text read>'}"})
-                        continue
-            duplicate = next((other for other, pixels in accepted
-                              if image.shape == pixels.shape and cosine(image, pixels) >= DUPLICATE_COSINE), None)
-            if duplicate:
-                refused.append({"sku": sku, "view": name,
-                                "reason": f"near-duplicate of {duplicate}"})
-                continue
-            accepted.append((name, image))
-        if len(accepted) < min_views:
-            for name, _ in accepted:
-                refused.append({"sku": sku, "view": name,
-                                "reason": f"only {len(accepted)} usable view(s), {min_views} required"})
-            continue
-        kept[sku] = [name for name, _ in accepted]
-    return kept, refused
-
-
-def unattributed(source: str) -> List[str]:
-    """SKUs whose views have no entry in the source's ATTRIBUTION.json.
-
-    Reported, not refused. A view with no recorded source is still a usable
-    reference - the six that predate the attribution file are independent of
-    the evaluation photo and their OCR matches their catalog names - but the
-    provenance is gone, and provenance is what lets a later audit tell a
-    re-sourced packshot from a leaked shelf crop. Naming them keeps the gap
-    from growing quietly.
-    """
-    path = os.path.join(source, "ATTRIBUTION.json")
-    try:
-        with open(path) as handle:
-            recorded = json.load(handle)
-    except (OSError, ValueError):
-        recorded = {}
-    return sorted(sku for sku in collect(source) if sku not in recorded)
-
-
-def text_discriminated_siblings(
-    kept: Dict[str, List[str]], source: str, names: Optional[Dict[str, str]] = None
-) -> List[Dict[str, Any]]:
-    """Cross-SKU near-duplicates: the same artwork on two different products.
-
-    These are the near-unknown case no threshold rejects. The geometry is real
-    because the sibling shares the brand's artwork, so only the package text
-    separates them. They are kept - they are legitimately different products -
-    but flagged, because a catalog that enrolls one and not the other will
-    accept the missing one as the enrolled one.
-    """
-    views: List[Tuple[str, str, np.ndarray]] = []
-    for sku, filenames in sorted(kept.items()):
-        for filename in filenames:
-            image = cv2.imread(os.path.join(source, filename))
-            if image is not None:
-                views.append((sku, filename, image))
-    flagged: List[Dict[str, Any]] = []
-    for index, (sku, filename, image) in enumerate(views):
-        for other_sku, other_name, other in views[index + 1:]:
-            if other_sku == sku or image.shape != other.shape:
-                continue
-            similarity = cosine(image, other)
-            if similarity < DUPLICATE_COSINE:
-                continue
-            flagged.append({
-                "sku": sku,
-                "view": filename,
-                "sibling_sku": other_sku,
-                "sibling_view": other_name,
-                "cosine": round(similarity, 4),
-                "name_similarity": round(
-                    name_ocr_similarity(
-                        (names or {}).get(sku, ""), (names or {}).get(other_sku, "")
-                    ), 3,
-                ),
-            })
-    return flagged
-
-
-def live_ready_verdict(
-    kept: Dict[str, List[str]], names: Optional[Dict[str, str]], shelf_skus: Optional[Set[str]]
-) -> Dict[str, Any]:
-    """The enrollment protocol's completeness rule, as a verdict.
-
-    A bay is live-ready when every SKU on its shelf tags is enrolled or
-    explicitly listed as text-only. Without a shelf-tag list the rule cannot be
-    evaluated, and the verdict says so rather than guessing.
-    """
-    catalog = set(names or {})
-    enrolled = set(kept)
-    text_only = catalog - enrolled
-    if shelf_skus is None:
-        return {
-            "live_ready": None,
-            "reason": "no --shelf-tags given; the completeness rule needs the shelf's SKU list",
-            "enrolled": len(enrolled),
-            "text_only": sorted(text_only),
-        }
-    missing = sorted(shelf_skus - enrolled - text_only)
-    return {
-        "live_ready": not missing,
-        "reason": (
-            "every shelf SKU is enrolled or text-only"
-            if not missing
-            else "on the shelf but absent from the catalog: " + ", ".join(missing)
-        ),
-        "enrolled": len(enrolled),
-        "text_only": sorted(text_only),
-        "shelf_skus": len(shelf_skus),
-        "missing_from_catalog": missing,
-    }
-
-
-def load_shelf_skus(path: Optional[str]) -> Optional[Set[str]]:
-    """SKUs on the shelf's tags: one per line, or a JSON list."""
-    if not path:
-        return None
-    with open(path, encoding="utf-8") as handle:
-        text = handle.read()
-    try:
-        payload = json.loads(text)
-    except json.JSONDecodeError:
-        return {line.strip() for line in text.splitlines() if line.strip()}
-    if isinstance(payload, list):
-        return {str(item).strip() for item in payload if str(item).strip()}
-    raise RebuildError(f"{path}: expected a JSON list or one SKU per line")
-
-
-def write_catalog(kept: Dict[str, List[str]], source: str, output: str, csv_path: Optional[str]) -> None:
-    """Copy the kept views and the whole CSV into a staging dir, then swap it in."""
-    staging = tempfile.mkdtemp(prefix=".catalog-", dir=os.path.dirname(os.path.abspath(output)) or ".")
-    try:
-        for views in kept.values():
-            for name in views:
-                shutil.copy2(os.path.join(source, name), os.path.join(staging, name))
-        if csv_path:
-            # Byte-for-byte, not re-serialized: gallery.catalog_digest hashes the
-            # raw file, and csv.DictWriter's default CRLF would move the digest
-            # and invalidate a trained identity head over a newline.
-            shutil.copy2(csv_path, os.path.join(staging, "stock.csv"))
-        if os.path.isdir(output):
-            # The documented rebuild runs with --source == --output, and the
-            # catalog directory also holds provenance this tool does not manage
-            # (README, references/rejected/, label files). Carry those over so a
-            # rebuild replaces the views and the CSV, not the record of where
-            # they came from.
-            for entry in os.listdir(output):
-                if entry == "stock.csv" or os.path.splitext(entry)[1].lower() in IMAGE_EXTENSIONS:
-                    continue
-                origin, target = os.path.join(output, entry), os.path.join(staging, entry)
-                if os.path.isdir(origin):
-                    shutil.copytree(origin, target)
-                else:
-                    shutil.copy2(origin, target)
-        if os.path.exists(output):
-            shutil.rmtree(output)
-        os.replace(staging, output)
-    except BaseException:
-        shutil.rmtree(staging, ignore_errors=True)
-        raise
-
-
-def main(argv: Optional[Sequence[str]] = None) -> int:
-    parser = argparse.ArgumentParser(description=__doc__,
-                                     formatter_class=argparse.RawDescriptionHelpFormatter)
-    parser.add_argument("--source", required=True, help="directory of pack shots")
-    parser.add_argument("--output", required=True, help="catalog directory to write")
-    parser.add_argument("--csv", help="stock.csv to validate and carry over whole")
-    parser.add_argument("--replace", action="store_true", help="overwrite an existing output")
-    parser.add_argument("--min-views", type=int, default=MIN_VIEWS,
-                        help=f"views required per SKU (default {MIN_VIEWS}; "
-                             "official pack-shot pages publish one front view per JAN)")
-    parser.add_argument("--ocr", choices=("auto", "vision", "none"), default=None,
-                        help="packshot/name OCR backend (default: DORAYAKI_OCR, else vision "
-                             "on macOS; an unavailable backend is skipped, not fatal)")
-    parser.add_argument("--source-manifest",
-                        help="image capture metadata (default: SOURCE/manifest.json when present)")
-    parser.add_argument("--eval-manifest",
-                        help="manifest of evaluation sessions; images captured in them are refused")
-    parser.add_argument("--shelf-tags",
-                        help="the shelf's SKU list (JSON list or one per line) for the "
-                             "completeness rule; without it the verdict is 'unknown'")
-    parser.add_argument("--json", help="write the audit here")
-    args = parser.parse_args(argv)
-
-    if os.path.exists(args.output) and not args.replace:
-        raise SystemExit(f"{args.output} exists; pass --replace to overwrite")
-
-    try:
-        if args.csv:
-            _, _, names = read_catalog(args.csv)
-            missing_rows = set(collect(args.source)) - set(names)
-            if missing_rows:
-                raise RebuildError("enrolled image SKU(s) with no CSV row: " + ", ".join(sorted(missing_rows)))
-        else:
-            names = {}
-            print("no --csv: image/CSV match and name/OCR checks skipped", file=sys.stderr)
-
-        source_manifest = args.source_manifest
-        default_manifest = os.path.join(args.source, "manifest.json")
-        if source_manifest is None and os.path.isfile(default_manifest):
-            source_manifest = default_manifest
-        capture_sessions = load_capture_sessions(source_manifest)
-        eval_sessions = load_eval_sessions(args.eval_manifest) if args.eval_manifest else set()
-        if eval_sessions:
-            unlabelled = sorted(name for views in collect(args.source).values() for name in views
-                                if name not in capture_sessions)
-            if unlabelled:
-                print("eval-session check cannot cover image(s) with no capture_session_id: "
-                      + ", ".join(unlabelled), file=sys.stderr)
-
-        # Read the shelf list before anything is written: a bad path or a
-        # malformed list must fail here, with the tool's own message, rather
-        # than after write_catalog has already replaced the catalog.
-        shelf_skus = load_shelf_skus(args.shelf_tags)
-        engine = build_ocr_engine(args.ocr)
-        ocr_engine = engine if engine.available() else None
-        if ocr_engine is None:
-            print("name/OCR check skipped: no OCR backend available", file=sys.stderr)
-        kept, refused = audit(args.source, min_views=args.min_views, names=names,
-                              ocr_engine=ocr_engine, capture_sessions=capture_sessions,
-                              eval_sessions=eval_sessions)
-        if refused:
-            print(f"refused {len(refused)} view(s):", file=sys.stderr)
-            for row in refused:
-                print(f"  {row['sku']:<10} {row['view']:<20} {row['reason']}", file=sys.stderr)
-        if not kept:
-            raise RebuildError("no SKU has usable views; nothing written")
-        write_catalog(kept, args.source, args.output, args.csv)
-    except (OSError, ValueError, json.JSONDecodeError, RebuildError, OcrUnavailable) as error:
-        raise SystemExit(str(error)) from error
-
-    # A row with no kept image is a text-only entry: the text lane still names
-    # and vetoes it from the CSV alone, so it is a coverage gap, not a refusal.
-    text_only = sorted(set(names) - set(kept))
-    siblings = text_discriminated_siblings(kept, args.source, names)
-    no_source = unattributed(args.source)
-    verdict = live_ready_verdict(kept, names, shelf_skus)
-    report = {
-        "source": args.source,
-        "output": args.output,
-        "min_width": MIN_WIDTH,
-        "min_height": MIN_HEIGHT,
-        "min_views": args.min_views,
-        "duplicate_cosine": DUPLICATE_COSINE,
-        "min_name_ocr": MIN_NAME_OCR,
-        "ocr": engine.name if ocr_engine is not None else "skipped",
-        "text_only_skus": text_only,
-        "text_discriminated_siblings": siblings,
-        "unattributed_skus": no_source,
-        "completeness": verdict,
-        "skus": {sku: views for sku, views in sorted(kept.items())},
-        "refused": refused,
-    }
-    if args.json:
-        with open(args.json, "w", encoding="utf-8") as handle:
-            json.dump(report, handle, indent=1, ensure_ascii=False)
-
-    print(f"{len(kept)} SKU(s), {sum(len(v) for v in kept.values())} view(s) -> {args.output}")
-    for sku, views in sorted(kept.items()):
-        print(f"  {sku:<10} {len(views)} view(s): {', '.join(views)}")
-    if text_only:
-        print(f"\ntext-only CSV row(s), no enrolled image ({len(text_only)}): {', '.join(text_only)}")
-    if siblings:
-        print(f"\ntext-discriminated sibling(s) ({len(siblings)}):")
-        for row in siblings:
-            print(f"  {row['sku']:<10} {row['view']:<20} ~ {row['sibling_sku']} "
-                  f"({row['sibling_view']}) cosine {row['cosine']}")
-    if no_source:
-        print(f"\nno recorded source ({len(no_source)}): {', '.join(no_source)} - "
-              "usable, but provenance is missing; re-sourcing would close it")
-    if verdict["live_ready"] is None:
-        print(f"\ncompleteness: unknown - {verdict['reason']}")
-    elif verdict["live_ready"]:
-        print(f"\ncompleteness: live-ready ({verdict['reason']})")
-    else:
-        print(f"\ncompleteness: NOT live-ready - {verdict['reason']}")
-    if refused:
-        print(f"\nrefused {len(refused)} view(s):")
-        for row in refused:
-            print(f"  {row['sku']:<10} {row['view']:<20} {row['reason']}")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/recognization/code/reconcile.py b/recognization/code/reconcile.py
deleted file mode 100644
index ef3c414..0000000
--- a/recognization/code/reconcile.py
+++ /dev/null
@@ -1,920 +0,0 @@
-"""
-Reconciliation (item vs price tag) and the first compliance rule family.
-
-Two pure layers, no I/O:
-
-- `reconcile()` compares one accepted identity with its tag and the catalog entry,
-  and publishes a state plus the individual signals. It never changes the product
-  identity: a disagreement is a second opinion, displayed next to the first.
-- `evaluate_compliance()` turns settled evidence into findings. Every rule fails
-  closed on evidence quality: a row whose tags are merely unreadable produces a
-  capture hint, never a violation.
-"""
-
-from __future__ import annotations
-
-import os
-import re
-import sys
-import unicodedata
-from dataclasses import dataclass, field
-from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple
-
-current_dir = os.path.dirname(os.path.abspath(__file__))
-if current_dir not in sys.path:
-    sys.path.insert(0, current_dir)
-
-from tags import TAG_STATE_ABSENT, TAG_STATE_READABLE, TAG_STATE_UNREADABLE  # noqa: E402
-
-RECONCILED_CONFIRMED = "confirmed"
-RECONCILED_MISMATCH = "mismatch"
-RECONCILED_UNVERIFIED = "unverified"
-RECONCILED_NO_TAG = "no_tag"
-RECONCILED_TAG_ONLY = "tag_only"
-
-RULE_MISSING_TAG = "missing_price_tag"
-RULE_MISSING_TAG_GROUP = "missing_tag_group"
-
-# How far along a row a tag may name facings: enough to cover the product's own run
-# of facings (measured: up to four facings, 70-120 px each), not enough to reach
-# across the shelf. The cap exists because a product whose own tag was not detected
-# would otherwise inherit whichever readable tag happens to be nearest.
-TAG_REACH_FACINGS = 2.5
-TAG_REACH_PX = 200
-# The name similarity a tag needs before it may overrule the packaging channel.
-TAG_OVERRIDE_SIMILARITY = 0.6
-RULE_ORPHAN_TAG = "orphan_tag"
-RULE_PRICE_MISMATCH = "price_mismatch"
-RULE_DUPLICATE_TAG = "duplicate_tag"
-RULE_UNREADABLE_TAGS = "unreadable_tags"
-
-NAME_MATCH_THRESHOLD = 0.4
-GROUP_MIN_FACINGS = 3
-STABILITY_FRAMES = 3
-STABILITY_WINDOW = 5
-
-
-# ---------------------------------------------------------------------------
-# text matching
-# ---------------------------------------------------------------------------
-
-
-def normalize_name(text: Optional[str]) -> str:
-    if not text:
-        return ""
-    text = unicodedata.normalize("NFKC", text)
-    text = re.sub(r"<[^>]*>", "", text)
-    text = re.sub(r"[\s・、。,.\-–—_/]+", "", text)
-    return text.lower()
-
-
-def longest_common_substring_ratio(a: str, b: str) -> float:
-    """Length of the longest common substring over the shorter string."""
-    if not a or not b:
-        return 0.0
-    shorter, longer = (a, b) if len(a) <= len(b) else (b, a)
-    best = 0
-    for start in range(len(shorter)):
-        for end in range(start + best + 1, len(shorter) + 1):
-            if shorter[start:end] in longer:
-                best = end - start
-            else:
-                break
-    return best / len(shorter)
-
-
-# ---------------------------------------------------------------------------
-# reconciliation
-# ---------------------------------------------------------------------------
-
-
-@dataclass
-class Signal:
-    kind: str          # "barcode" | "name" | "price"
-    agrees: bool
-    item: Any
-    tag: Any
-    note: Optional[str] = None
-    score: Optional[float] = None
-
-    def as_dict(self) -> Dict[str, Any]:
-        payload = {"kind": self.kind, "agrees": self.agrees, "item": self.item, "tag": self.tag}
-        if self.note:
-            payload["note"] = self.note
-        if self.score is not None:
-            payload["score"] = round(float(self.score), 3)
-        return payload
-
-
-def reconcile(
-    identity: Optional[Dict[str, Any]],
-    tag: Optional[Dict[str, Any]],
-    product: Optional[Dict[str, Any]],
-) -> Optional[Dict[str, Any]]:
-    """Compare one facing with its tag.
-
-    Returns None when there is nothing to reconcile (no accepted identity and no
-    readable tag). A tag with no product above it is `tag_only`.
-    """
-    identity_state = (identity or {}).get("state")
-    tag_state = (tag or {}).get("state", TAG_STATE_ABSENT)
-    accepted = identity_state == "recognized"
-    readable_tag = tag_state == TAG_STATE_READABLE
-
-    if not accepted and not readable_tag:
-        return None
-
-    if accepted and tag_state == TAG_STATE_ABSENT:
-        return {"state": RECONCILED_NO_TAG, "signals": [], "message": None}
-    if accepted and tag_state == TAG_STATE_UNREADABLE:
-        return {
-            "state": RECONCILED_UNVERIFIED,
-            "signals": [],
-            "message": "tag found but its text was not read",
-        }
-    if not accepted and readable_tag:
-        return {"state": RECONCILED_TAG_ONLY, "signals": [], "message": None}
-
-    signals: List[Signal] = []
-
-    # A barcode is the only exact signal; a checksummed code is not a guess. The
-    # number a tag prints large beside the sale price is NOT a barcode - it is the
-    # regular price (verified against the manufacturer: 4901577092381 for a tag
-    # printing 357), so it is compared as a price below.
-    item_barcode = str((product or {}).get("jan") or "").strip()
-    tag_barcode = str((tag or {}).get("barcode") or "").strip()
-    if item_barcode and tag_barcode:
-        signals.append(Signal("barcode", item_barcode == tag_barcode, item_barcode, tag_barcode))
-
-    item_name = normalize_name((product or {}).get("product_name"))
-    tag_name = normalize_name((tag or {}).get("name"))
-    if item_name and tag_name:
-        ratio = longest_common_substring_ratio(item_name, tag_name)
-        signals.append(
-            Signal("name", ratio >= NAME_MATCH_THRESHOLD, item_name[:32], tag_name[:32],
-                   note=f"match {ratio:.2f}", score=ratio)
-        )
-
-    item_price = (product or {}).get("price")
-    tag_price = (tag or {}).get("price")
-    tag_regular = (tag or {}).get("regular_price")
-    if item_price is not None and (tag_price is not None or tag_regular is not None):
-        candidates = [value for value in (tag_price, tag_regular) if value is not None]
-        agrees = any(abs(float(item_price) - float(value)) < 0.5 for value in candidates)
-        signals.append(Signal("price", agrees, item_price, tag_price if tag_price is not None else tag_regular))
-
-    if not signals:
-        return {
-            "state": RECONCILED_UNVERIFIED,
-            "signals": [],
-            "message": "tag read, but nothing comparable in the catalog",
-        }
-
-    if any(signal.kind == "barcode" for signal in signals):
-        decided = [signal for signal in signals if signal.kind == "barcode"][0]
-        state = RECONCILED_CONFIRMED if decided.agrees else RECONCILED_MISMATCH
-    elif any(signal.agrees for signal in signals):
-        state = RECONCILED_CONFIRMED
-    elif all(not signal.agrees for signal in signals):
-        state = RECONCILED_MISMATCH
-    else:
-        state = RECONCILED_UNVERIFIED
-
-    message = None
-    if state == RECONCILED_MISMATCH:
-        price_signal = next((signal for signal in signals if signal.kind == "price"), None)
-        name_signal = next((signal for signal in signals if signal.kind == "name"), None)
-        if price_signal is not None:
-            message = f"tag says {price_signal.tag}, catalog says {price_signal.item}"
-        elif name_signal is not None:
-            message = "tag name does not match the catalog entry"
-    return {"state": state, "signals": [signal.as_dict() for signal in signals], "message": message}
-
-
-# ---------------------------------------------------------------------------
-# compliance
-# ---------------------------------------------------------------------------
-
-
-@dataclass
-class ComplianceSettings:
-    group_min_facings: int = GROUP_MIN_FACINGS
-    stability_frames: int = STABILITY_FRAMES
-    stability_window: int = STABILITY_WINDOW
-    # A row may only be judged when enough of its facings carry tag evidence
-    # (readable or unreadable). Below this, the row is "not evaluated": claiming a
-    # tag is missing because our detector found none would be a false violation.
-    # 0.7 measured: at 0.5 a row where our own tag detection was patchy produced a
-    # false "group without price tag" on a shelf where every facing has a tag.
-    min_row_tag_coverage: float = 0.7
-
-
-@dataclass
-class Finding:
-    rule: str
-    severity: str                 # "violation" | "hint"
-    row_index: Optional[int]
-    box: List[int]
-    count: int = 1
-    skus: List[str] = field(default_factory=list)
-    detail: Optional[str] = None
-
-    def key(self) -> Tuple[Any, ...]:
-        return (self.rule, self.row_index, self.box[0] // 32, self.box[1] // 32, self.count)
-
-    def as_dict(self) -> Dict[str, Any]:
-        payload = {
-            "rule": self.rule,
-            "severity": self.severity,
-            "row_index": self.row_index,
-            "box": list(self.box),
-            "count": self.count,
-            "skus": list(self.skus),
-        }
-        if self.detail:
-            payload["detail"] = self.detail
-        return payload
-
-
-class ComplianceTracker:
-    """Keeps findings only while they persist, so the UI never flickers."""
-
-    def __init__(self, settings: Optional[ComplianceSettings] = None):
-        self.settings = settings or ComplianceSettings()
-        self._history: List[set] = []
-
-    def update(self, findings: Sequence[Finding]) -> List[Finding]:
-        current = {finding.key(): finding for finding in findings}
-        self._history.append(set(current))
-        if len(self._history) > self.settings.stability_window:
-            self._history.pop(0)
-        stable: List[Finding] = []
-        for key, finding in current.items():
-            seen = sum(1 for frame in self._history if key in frame)
-            if seen >= self.settings.stability_frames:
-                stable.append(finding)
-        return stable
-
-    def reset(self) -> None:
-        self._history.clear()
-
-
-def _sku_of_track(track: Dict[str, Any]) -> Optional[str]:
-    identity = track.get("identity") or {}
-    if identity.get("state") == "recognized" and identity.get("sku_id"):
-        return str(identity["sku_id"])
-    return None
-
-
-def group_facings(
-    row_tracks: Sequence[Dict[str, Any]],
-    max_gap_ratio: float = 0.35,
-    size_tolerance: float = 0.45,
-) -> List[List[Dict[str, Any]]]:
-    """Group a row's facings into products.
-
-    A store prints one price tag per *product*, not per facing: measured on the
-    demo shelf, a row carries 16-18 facings but only 4-5 tags, because the four
-    facings of one mayonnaise share a single tag. Counting facings therefore
-    invents missing tags on a shelf that is perfectly labelled, so the rules count
-    *groups*.
-
-    Identity wins over geometry when both facings were recognised: measured, a
-    run of ピュアセレクト facings was split by geometry alone because one facing
-    was detected narrower than its neighbours, and the product was then reported
-    as missing the tag that was in fact sitting further along its own run.
-    """
-    ordered = sorted(
-        (track for track in row_tracks if track.get("box") or track.get("raw_box")),
-        key=lambda track: (track.get("box") or track.get("raw_box"))[0],
-    )
-    groups: List[List[Dict[str, Any]]] = []
-    for track in ordered:
-        box = track.get("box") or track.get("raw_box")
-        if groups:
-            previous = groups[-1][-1]
-            pbox = previous.get("box") or previous.get("raw_box")
-            gap = box[0] - pbox[2]
-            width = max(1, pbox[2] - pbox[0])
-            height = max(1, pbox[3] - pbox[1])
-            width2 = max(1, box[2] - box[0])
-            height2 = max(1, box[3] - box[1])
-            sku, previous_sku = _sku_of_track(track), _sku_of_track(previous)
-            close = gap <= max_gap_ratio * width
-            if sku and previous_sku:
-                same_product = sku == previous_sku and close
-            else:
-                same_product = close and (
-                    abs(height2 - height) / height <= size_tolerance
-                    and abs(width2 - width) / width <= size_tolerance
-                )
-            if same_product:
-                groups[-1].append(track)
-                continue
-        groups.append([track])
-    return groups
-
-
-def cluster_into_rows(tracks: Sequence[Dict[str, Any]]) -> List[List[Dict[str, Any]]]:
-    """Split facings into shelf rows by vertical overlap.
-
-    `group_facings` is a *within-row* operation: given the whole frame it would
-    merge the products stacked in one column across six shelves into a single
-    "product". Identity assignment needs rows first.
-    """
-    rows: List[List[Dict[str, Any]]] = []
-    for track in sorted(
-        (track for track in tracks if track.get("box") or track.get("raw_box")),
-        key=lambda track: (track.get("box") or track.get("raw_box"))[1],
-    ):
-        box = track.get("box") or track.get("raw_box")
-        placed = False
-        for row in rows:
-            row_box = _group_box(row)
-            overlap = min(row_box[3], box[3]) - max(row_box[1], box[1])
-            if overlap > 0.5 * min(row_box[3] - row_box[1], box[3] - box[1]):
-                row.append(track)
-                placed = True
-                break
-        if not placed:
-            rows.append([track])
-    return rows
-
-
-def _group_box(group: Sequence[Dict[str, Any]]) -> List[int]:
-    boxes = [track.get("box") or track.get("raw_box") for track in group]
-    return [
-        min(box[0] for box in boxes),
-        min(box[1] for box in boxes),
-        max(box[2] for box in boxes),
-        max(box[3] for box in boxes),
-    ]
-
-
-def evaluate_compliance(
-    tracks: Sequence[Dict[str, Any]],
-    tag_by_track: Dict[int, Dict[str, Any]],
-    all_tags: Sequence[Dict[str, Any]],
-    bands: Sequence[Any],
-    reconciliations: Optional[Dict[int, Dict[str, Any]]] = None,
-    settings: Optional[ComplianceSettings] = None,
-) -> List[Finding]:
-    """Findings from settled evidence. Rules with unreadable evidence stay silent.
-
-    `tag_by_track` is the per-facing attachment; `all_tags` is every tag the pass
-    found, so a tag nobody claimed can be reported as an orphan.
-    """
-    settings = settings or ComplianceSettings()
-    findings: List[Finding] = []
-
-    by_row: Dict[int, List[Dict[str, Any]]] = {}
-    for track in tracks:
-        row_index = _row_for(track.get("box") or track.get("raw_box"), bands)
-        by_row.setdefault(row_index, []).append(track)
-
-    for row_index, row_tracks in by_row.items():
-        row_tracks.sort(key=lambda track: (track.get("box") or [0])[0])
-        row_tags = [tag_by_track.get(int(track["track_id"]), {}) for track in row_tracks]
-        readable = [tag for tag in row_tags if tag.get("state") == TAG_STATE_READABLE]
-        evidenced = [
-            tag for tag in row_tags if tag.get("state") in (TAG_STATE_READABLE, TAG_STATE_UNREADABLE)
-        ]
-        # Count products, not facings: one tag serves a run of identical facings.
-        products = group_facings(row_tracks)
-        product_tags = [
-            [
-                tag_by_track.get(int(track["track_id"]), {})
-                for track in group
-            ]
-            for group in products
-        ]
-        products_evidenced = [
-            group
-            for group, tags in zip(products, product_tags)
-            if any(tag.get("state") in (TAG_STATE_READABLE, TAG_STATE_UNREADABLE) for tag in tags)
-        ]
-        coverage = len(products_evidenced) / max(1, len(products))
-
-        if not readable or coverage < settings.min_row_tag_coverage:
-            # Fail closed on evidence quality. Either nothing in this row could be
-            # read, or too few facings carry any tag evidence at all - in both cases
-            # the honest answer is "not evaluated", never "tag missing".
-            if evidenced:
-                detail = (
-                    "move closer: the price tags in this row could not be read"
-                    if not readable
-                    else (
-                        f"tags detected for {len(products_evidenced)} of {len(products)} products "
-                        f"in this row"
-                    )
-                )
-                findings.append(
-                    Finding(
-                        rule=RULE_UNREADABLE_TAGS,
-                        severity="hint",
-                        row_index=row_index,
-                        box=_row_box(row_tracks),
-                        count=len(row_tracks),
-                        detail=detail,
-                    )
-                )
-            continue
-
-        # missing_price_tag: one finding per row (the UI highlights the row), with
-        # the products that lack a tag listed rather than one finding per facing.
-        # A product's tag may sit anywhere along its run of facings, so a group
-        # only counts as missing when no tag exists for its SKU elsewhere in the
-        # row either. This is the domain rule, applied as a guard.
-        skus_with_tags = {
-            _sku_of_track(track)
-            for track, tag in zip(row_tracks, row_tags)
-            if tag.get("state") in (TAG_STATE_READABLE, TAG_STATE_UNREADABLE)
-        } - {None}
-        # A fragment of a product is not a product. Measured without identities: a
-        # facing detected at 54 px wide beside 150 px neighbours was split off and
-        # reported as missing the tag that belonged to its own run. Anything much
-        # narrower than the row's typical product is not judged.
-        widths = sorted(
-            max(1, (_group_box(group)[2] - _group_box(group)[0])) for group in products
-        )
-        typical_width = widths[len(widths) // 2] if widths else 0
-        missing_groups = [
-            group
-            for group, tags in zip(products, product_tags)
-            if all(tag.get("state") == TAG_STATE_ABSENT for tag in tags)
-            and not (_sku_of_track(group[0]) in skus_with_tags)
-            and (_group_box(group)[2] - _group_box(group)[0]) >= 0.6 * typical_width
-        ]
-        if missing_groups:
-            findings.append(
-                Finding(
-                    rule=RULE_MISSING_TAG,
-                    severity="violation",
-                    row_index=row_index,
-                    box=_row_box([track for group in missing_groups for track in group]),
-                    count=len(missing_groups),
-                    skus=[_sku_of(group[0]) for group in missing_groups],
-                    detail=(
-                        f"{len(missing_groups)} of {len(products)} products in this row "
-                        f"have no price tag"
-                    ),
-                )
-            )
-
-        # missing_tag_group: consecutive products without a tag
-        missing_ids = {id(group) for group in missing_groups}
-        run: List[List[Dict[str, Any]]] = []
-        for group, tags in zip(products, product_tags):
-            if id(group) in missing_ids:
-                run.append(group)
-                continue
-            if len(run) >= settings.group_min_facings:
-                findings.append(
-                    _group_finding(row_index, [track for g in run for track in g], len(run))
-                )
-            run = []
-        if len(run) >= settings.group_min_facings:
-            findings.append(_group_finding(row_index, [track for g in run for track in g], len(run)))
-
-    # price_mismatch from reconciliation
-    for track in tracks:
-        track_id = int(track["track_id"])
-        reconciliation = (reconciliations or {}).get(track_id)
-        if not reconciliation or reconciliation.get("state") != RECONCILED_MISMATCH:
-            continue
-        signals = reconciliation.get("signals", [])
-        if not any(signal.get("kind") == "price" for signal in signals):
-            continue
-        # A mismatch needs one trustworthy anchor before it is a finding. Measured:
-        # a wrong identity plus a garbled tag read (name match 0.11, no JAN)
-        # produced a confident-looking "tag says 90, catalog says 188" that was
-        # really just noise. With an anchor - the tag's JAN, or a name that
-        # actually resembles the product - the disagreement is worth reporting.
-        tag_for_track = tag_by_track.get(track_id, {})
-        name_signal = next(
-            (signal for signal in signals if signal.get("kind") == "name"), None
-        )
-        # The anchor is the tag's own arithmetic agreeing, or a name that actually
-        # resembles the product. (It used to be `tag.jan`, which held a *price*.)
-        flags = set(tag_for_track.get("flags") or [])
-        corroborated = bool({"tax_consistent", "discount_consistent"} & flags)
-        name_score = float(name_signal.get("score") or 0.0) if name_signal else 0.0
-        has_anchor = corroborated or name_score >= 0.35
-        if not has_anchor:
-            findings.append(
-                Finding(
-                    rule=RULE_UNREADABLE_TAGS,
-                    severity="hint",
-                    row_index=_row_for(track.get("box") or track.get("raw_box"), bands),
-                    box=list(track.get("box") or track.get("raw_box") or [0, 0, 0, 0]),
-                    count=1,
-                    skus=[_sku_of(track)],
-                    detail="the tag under this product could not be read well enough to compare",
-                )
-            )
-            continue
-        findings.append(
-            Finding(
-                rule=RULE_PRICE_MISMATCH,
-                severity="violation",
-                row_index=_row_for(track.get("box") or track.get("raw_box"), bands),
-                box=list(track.get("box") or track.get("raw_box") or [0, 0, 0, 0]),
-                count=1,
-                skus=[_sku_of(track)],
-                detail=reconciliation.get("message"),
-            )
-        )
-
-    # orphan_tag: a readable tag that no facing claims (a tag with no product above
-    # it is either an empty slot with a leftover tag or a mis-clustered read).
-    attached_ids = {
-        tag.get("id") for tag in tag_by_track.values() if tag.get("id")
-    }
-    for tag in all_tags:
-        if tag.get("state") != TAG_STATE_READABLE or not tag.get("id"):
-            continue
-        if tag["id"] in attached_ids:
-            continue
-        findings.append(
-            Finding(
-                rule=RULE_ORPHAN_TAG,
-                severity="violation",
-                row_index=None,
-                box=list(tag.get("box") or [0, 0, 0, 0]),
-                count=1,
-                detail="price tag with no product above it",
-            )
-        )
-
-    return findings
-
-
-def _group_finding(row_index: int, run: Sequence[Dict[str, Any]], products: int = 0) -> Finding:
-    boxes = [track.get("box") or track.get("raw_box") or [0, 0, 0, 0] for track in run]
-    x1 = min(box[0] for box in boxes)
-    y1 = min(box[1] for box in boxes)
-    x2 = max(box[2] for box in boxes)
-    y2 = max(box[3] for box in boxes)
-    return Finding(
-        rule=RULE_MISSING_TAG_GROUP,
-        severity="violation",
-        row_index=row_index,
-        box=[x1, y1, x2, y2],
-        count=len(run),
-        skus=[_sku_of(track) for track in run],
-        detail=(
-            f"{products or len(run)} products in a row without a price tag"
-        ),
-    )
-
-
-def _row_box(tracks: Sequence[Dict[str, Any]]) -> List[int]:
-    boxes = [track.get("box") or track.get("raw_box") or [0, 0, 0, 0] for track in tracks]
-    return [
-        min(box[0] for box in boxes),
-        min(box[1] for box in boxes),
-        max(box[2] for box in boxes),
-        max(box[3] for box in boxes),
-    ]
-
-
-def _row_for(box: Optional[Sequence[int]], bands: Sequence[Any]) -> int:
-    """Which rail band (i.e. row) a facing sits above."""
-    if not box or not bands:
-        return 0
-    x_center = (box[0] + box[2]) / 2.0
-    y_bottom = box[3]
-    best_index = 0
-    best_distance = None
-    for band in bands:
-        bx1, by1, bx2, by2 = band.box
-        if not (bx1 - 40 <= x_center <= bx2 + 40):
-            continue
-        distance = abs(by1 - y_bottom)
-        if best_distance is None or distance < best_distance:
-            best_distance = distance
-            best_index = band.row_index
-    return best_index
-
-
-def _sku_of(track: Dict[str, Any]) -> str:
-    identity = track.get("identity") or {}
-    return str(identity.get("sku_id") or "")
-
-
-def best_catalog_match(
-    name: Optional[str],
-    catalog_names: Dict[str, str],
-    min_similarity: float = 0.45,
-) -> Tuple[Optional[str], float]:
-    """The catalog entry a printed tag name most resembles."""
-    if not name:
-        return None, 0.0
-    normalized = normalize_name(name)
-    best_sku, best_score = None, 0.0
-    for sku_id, catalog_name in catalog_names.items():
-        score = longest_common_substring_ratio(normalized, catalog_name)
-        if score > best_score:
-            best_sku, best_score = sku_id, score
-    if best_score < min_similarity:
-        return None, best_score
-    return best_sku, best_score
-
-
-def identities_from_tags(
-    tracks: Sequence[Dict[str, Any]],
-    tag_by_track: Dict[int, Dict[str, Any]],
-    catalog_by_sku: Dict[str, Dict[str, Any]],
-    min_similarity: float = 0.40,
-    all_tags: Optional[Sequence[Dict[str, Any]]] = None,
-) -> Dict[int, Dict[str, Any]]:
-    """Identify a product from the tag printed under it.
-
-    The shelf-edge tag carries the store's own name for the product in clean
-    printed text, and one tag serves a whole run of facings. That makes it the
-    most reliable identity source on the shelf - far more so than recognising the
-    packaging, which is fine-grained (twenty near-identical cartons), occluded,
-    and distorted by glare and angle.
-
-    Measured with an off-the-shelf DINOv2 embedding: identical products do cluster
-    (two orange juices, two apple juices, two almond drinks) but fine-grained
-    cases do not - 32 cartons collapsed into one cluster and nine different
-    bottled drinks grouped by *container shape*.
-
-    Assignment is by *tag position*, not by geometric grouping: the tags on a rail
-    mark where each product sits, so a facing belongs to the nearest tag along the
-    row. Grouping facings by size and spacing was measured to merge different
-    products (a 945 px "product" spanning half a shelf), which then swallowed the
-    wrong tag.
-    """
-    catalog_names = {
-        sku_id: normalize_name(product.get("product_name"))
-        for sku_id, product in catalog_by_sku.items()
-        if product.get("product_name")
-    }
-    if not catalog_names:
-        return {}
-
-    tags = [dict(tag) for tag in (all_tags or []) if tag.get("box")]
-    for tag in tags:
-        if tag.get("sku_from_name") is None:
-            sku_id, score = best_catalog_match(tag.get("name"), catalog_names, min_similarity)
-            tag["sku_from_name"] = sku_id
-            tag["sku_score"] = score
-    if not tags:
-        return {}
-
-    found: Dict[int, Dict[str, Any]] = {}
-    for row in cluster_into_rows(tracks):
-        row_box = _group_box(row)
-        row_height = max(1, row_box[3] - row_box[1])
-        # The rail's tags sit just under the row they describe.
-        row_tags = [
-            tag
-            for tag in tags
-            if row_box[3] - 0.5 * (tag["box"][3] - tag["box"][1])
-            <= tag["box"][1]
-            <= row_box[3] + 1.5 * (tag["box"][3] - tag["box"][1])
-        ]
-        if not row_tags:
-            continue
-        for track in row:
-            box = track.get("box") or track.get("raw_box")
-            center = (box[0] + box[2]) / 2.0
-            facing_width = max(1, box[2] - box[0])
-            # A tag names the product at its own position on the rail. Without a
-            # distance cap a facing whose own tag was not detected inherits the
-            # nearest readable tag's product, which can belong to a different
-            # product several columns away.
-            limit = max(TAG_REACH_FACINGS * facing_width, TAG_REACH_PX)
-            candidates = [
-                tag for tag in row_tags
-                if abs(center - (tag["box"][0] + tag["box"][2]) / 2.0) <= limit
-            ]
-            if not candidates:
-                continue
-            nearest = min(
-                candidates,
-                key=lambda tag: abs(center - (tag["box"][0] + tag["box"][2]) / 2.0),
-            )
-            sku_id = nearest.get("sku_from_name")
-            if not sku_id:
-                continue
-            found[int(track["track_id"])] = {
-                "sku_id": sku_id,
-                "score": round(float(nearest.get("sku_score") or 0.0), 3),
-                "source": "tag_name",
-            }
-    return found
-
-
-def attach_tags_to_boxes(
-    tag_service: Any,
-    image: Any,
-    boxes: Sequence[Dict[str, Any]],
-    catalog_by_sku: Dict[str, Dict[str, Any]],
-    identities: Optional[Dict[int, Dict[str, Any]]] = None,
-) -> Dict[str, Any]:
-    """One-shot tag pass for a still image (photo mode / `?tags=true`).
-
-    Runs the passes synchronously - a still is not a stream, so there is no frame
-    loop to protect - then attaches tags, reconciles, and evaluates compliance.
-    """
-    image_shape = image.shape
-    # A still has no later frame to pick up the remaining bands, and `plan` only
-    # hands out a few per call (a stream must stay responsive). Drain them, with a
-    # cap so a huge shelf cannot turn one request into minutes of OCR. Draining
-    # also feeds the vote: a band read in two rounds is two observations.
-    bands: List[Any] = []
-    rounds = 0
-    while rounds < 4:
-        planned = tag_service.plan(boxes, image_shape)
-        if not planned:
-            break
-        for band in planned:
-            # Two independent reads (different upscale) so the vote has evidence
-            # to work with; a single read has nothing to vote against.
-            tag_service.run_pass(image, band, scale_bias=1.0)
-            tag_service.run_pass(image, band, scale_bias=1.7)
-            bands.append(band)
-        rounds += 1
-
-    tracks = [
-        {
-            "track_id": int(box["id"]),
-            "box": list(box["box"]),
-            "identity": (identities or {}).get(int(box["id"])) or {},
-            "tag_identity": {},
-        }
-        for box in boxes
-    ]
-    tag_by_track, all_tags, counts = tag_service.attach(tracks)
-
-    # Tag-first identity, applied before reconciliation so the comparison below
-    # sees the identity the tag itself provides.
-    tag_identities = identities_from_tags(
-        tracks, tag_by_track, catalog_by_sku, all_tags=all_tags
-    )
-    for track in tracks:
-        derived = tag_identities.get(int(track["track_id"]))
-        if not derived:
-            continue
-        # Always keep the tag's claim, even when packaging recognition already
-        # named the product: two independent identity channels that can be
-        # compared are worth more than one that silently wins. A disagreement is
-        # itself a finding (a misplaced tag, or a product in the wrong place).
-        track["tag_identity"] = dict(derived)
-        current = track["identity"] or {}
-        if current.get("state") == "recognized":
-            if current.get("sku_id") != derived["sku_id"]:
-                # Only a corroborated tag, or a name that closely resembles the
-                # product, may overrule the packaging channel. A weak 0.40 name
-                # match used to be enough, which is not evidence.
-                tag_flags = set((tag_by_track.get(int(track["track_id"])) or {}).get("flags") or [])
-                strong = bool({"tax_consistent", "discount_consistent"} & tag_flags) or \
-                    float(derived.get("score") or 0.0) >= TAG_OVERRIDE_SIMILARITY
-                if not strong:
-                    track["identity_conflict"] = {
-                        "packaging": current.get("sku_id"),
-                        "tag": derived["sku_id"],
-                        "packaging_score": current.get("score"),
-                        "note": "tag name match too weak to overrule packaging",
-                    }
-                    continue
-                # Two channels disagree. Measured on the demo shelf: where both
-                # named the same facing they agreed only 69% of the time, and the
-                # packaging side of every disagreement carried a low score - so
-                # the store's own label wins, and the disagreement is recorded
-                # rather than hidden (a misplaced tag is a real finding).
-                track["identity_conflict"] = {
-                    "packaging": current.get("sku_id"),
-                    "tag": derived["sku_id"],
-                    "packaging_score": current.get("score"),
-                }
-                track["identity"] = {
-                    "state": "recognized",
-                    "sku_id": derived["sku_id"],
-                    "identity_level": "tag_over_packaging",
-                    "confidence": derived["score"],
-                    "score": derived["score"],
-                    "source": "tag_name",
-                    "candidates": [],
-                    "attempts": 1,
-                    "method": "tag_name",
-                }
-            continue
-        track["identity"] = {
-            "state": "recognized",
-            "sku_id": derived["sku_id"],
-            "identity_level": "tag_derived",
-            "confidence": derived["score"],
-            "score": derived["score"],
-            "source": "tag_name",
-            "candidates": [],
-            "attempts": 1,
-            "method": "tag_name",
-        }
-
-    reconciliations: Dict[int, Dict[str, Any]] = {}
-    for track in tracks:
-        track_id = track["track_id"]
-        tag = tag_by_track.get(track_id)
-        product = catalog_by_sku.get((track["identity"] or {}).get("sku_id") or "")
-        decision = reconcile(track["identity"], tag, product)
-        if decision is not None:
-            reconciliations[track_id] = decision
-
-    findings = evaluate_compliance(tracks, tag_by_track, all_tags, bands, reconciliations)
-    summary = summarize_findings(findings)
-    return {
-        "by_box": tag_by_track,
-        "tags": all_tags,
-        "counts": counts,
-        "reconciliations": reconciliations,
-        "tag_identities": tag_identities,
-        "bands": [{"row_index": band.row_index, "box": band.box} for band in bands],
-        "bands_objects": list(bands),
-        "compliance": {
-            "violations": summary["violations"],
-            "by_rule": summary["by_rule"],
-            "rows": [
-                {
-                    "row_index": finding.row_index,
-                    "rule": finding.rule,
-                    "box": finding.box,
-                    "missing": finding.count,
-                    "skus": finding.skus,
-                }
-                for finding in findings
-                if finding.severity == "violation"
-            ],
-            "hints": summary["hints"],
-        },
-    }
-
-
-def reconcile_bundle(
-    bundle: Dict[str, Any],
-    detections: Sequence[Dict[str, Any]],
-    catalog_by_sku: Dict[str, Dict[str, Any]],
-) -> Dict[str, Any]:
-    """Re-run reconciliation and compliance against a cached tag bundle.
-
-    Used when identities arrive after the tag pass (photo mode: recognize paints
-    boxes, classify names them). Without this, every tag would look like `tag_only`
-    because it was compared against an identity that did not exist yet.
-    """
-    tracks = [
-        {
-            "track_id": int(detection["id"]),
-            "box": list(detection["box"]),
-            "identity": detection.get("identity") or {},
-        }
-        for detection in detections
-    ]
-    tag_by_track = bundle.get("by_box") or {}
-    reconciliations: Dict[int, Dict[str, Any]] = {}
-    for track in tracks:
-        track_id = track["track_id"]
-        tag = tag_by_track.get(track_id)
-        product = catalog_by_sku.get((track["identity"] or {}).get("sku_id") or "")
-        decision = reconcile(track["identity"], tag, product)
-        if decision is not None:
-            reconciliations[track_id] = decision
-
-    findings = evaluate_compliance(
-        tracks, tag_by_track, bundle.get("tags") or [], bundle.get("bands_objects") or [], reconciliations
-    )
-    summary = summarize_findings(findings)
-    return {
-        "reconciliations": reconciliations,
-        "compliance": {
-            "violations": summary["violations"],
-            "by_rule": summary["by_rule"],
-            "rows": [
-                {
-                    "row_index": finding.row_index,
-                    "rule": finding.rule,
-                    "box": finding.box,
-                    "missing": finding.count,
-                    "skus": finding.skus,
-                }
-                for finding in findings
-                if finding.severity == "violation"
-            ],
-            "hints": summary["hints"],
-        },
-    }
-
-
-def summarize_findings(findings: Sequence[Finding]) -> Dict[str, Any]:
-    by_rule: Dict[str, int] = {}
-    for finding in findings:
-        if finding.severity != "violation":
-            continue
-        by_rule[finding.rule] = by_rule.get(finding.rule, 0) + 1
-    return {
-        "violations": sum(by_rule.values()),
-        "by_rule": by_rule,
-        "hints": [finding.as_dict() for finding in findings if finding.severity != "violation"],
-    }
diff --git a/recognization/code/record.py b/recognization/code/record.py
deleted file mode 100644
index cba02f2..0000000
--- a/recognization/code/record.py
+++ /dev/null
@@ -1,147 +0,0 @@
-"""
-Record a live session so it can become data.
-
-Everything measured so far comes from one or two photographs, which is the largest
-weakness in the project. The fix is a store walk recorded from the device that will
-do the scanning, with enough context to label it afterwards.
-
-`--record-frames DIR` writes each processed frame plus a JSON line describing what
-the server saw in it. The lines are the labelling input: a facing that the tag
-channel named is a candidate label, and the confirmation screen is where a human
-accepts or corrects it. Nothing here is a training set on its own - it is the raw
-material for one.
-
-Layout:
-
-    DIR/<session>/000123.jpg          the frame, exactly as the client sent it
-    DIR/<session>/index.jsonl         one line per frame:
-        {"frame": 123, "path": "...", "at": 1699..., "identity_backend": "gallery",
-         "summary": {...},
-         "tracks": [{"box": [...], "identity": {...}, "tag": {...}}]}
-
-Frames are written at most every `every` processed frames (default 3) so a long
-walk does not fill a disk, and the index keeps the frame's own numbering so the
-sampling is visible rather than silent.
-"""
-
-from __future__ import annotations
-
-import json
-import os
-import threading
-import time
-from typing import Any, Dict, List, Optional
-
-
-class FrameRecorder:
-    """Write frames and their results to disk, for labelling later."""
-
-    def __init__(
-        self,
-        root: str,
-        every: int = 3,
-        max_frames_per_session: int = 4000,
-        identity_backend: Optional[str] = None,
-    ) -> None:
-        self.root = root
-        self.every = max(1, int(every))
-        self.max_frames_per_session = int(max_frames_per_session)
-        self.identity_backend = (identity_backend or "").strip().lower() or None
-        self._lock = threading.Lock()
-        self._counts: Dict[str, int] = {}
-        self._written: Dict[str, int] = {}
-        os.makedirs(self.root, exist_ok=True)
-
-    def _session_dir(self, session_id: str) -> str:
-        path = os.path.join(self.root, session_id)
-        os.makedirs(path, exist_ok=True)
-        return path
-
-    def write(self, session_id: str, frame_id: int, payload: bytes, result: Dict[str, Any]) -> None:
-        """Record one processed frame and what was seen in it."""
-        with self._lock:
-            seen = self._counts.get(session_id, 0) + 1
-            self._counts[session_id] = seen
-            if seen % self.every:
-                return
-            written = self._written.get(session_id, 0)
-            if written >= self.max_frames_per_session:
-                return
-            self._written[session_id] = written + 1
-
-        directory = self._session_dir(session_id)
-        name = f"{written:06d}.jpg"
-        try:
-            with open(os.path.join(directory, name), "wb") as handle:
-                handle.write(payload)
-        except OSError:
-            return
-
-        tracks: List[Dict[str, Any]] = []
-        for track in result.get("tracks") or []:
-            tracks.append(
-                {
-                    "box": track.get("box"),
-                    "identity": track.get("identity"),
-                    "tag_identity": track.get("tag_identity"),
-                    "tag": track.get("price_tag"),
-                }
-            )
-        line = {
-            "frame": frame_id,
-            "path": name,
-            "at": round(time.time(), 3),
-            "summary": result.get("summary"),
-            "tracks": tracks,
-        }
-        if self.identity_backend:
-            line["identity_backend"] = self.identity_backend
-        try:
-            with open(os.path.join(directory, "index.jsonl"), "a", encoding="utf-8") as handle:
-                handle.write(json.dumps(line, ensure_ascii=False) + "\n")
-        except OSError:
-            return
-
-    def stats(self) -> Dict[str, Any]:
-        with self._lock:
-            return {
-                "root": self.root,
-                "every": self.every,
-                "sessions": len(self._counts),
-                "frames_written": sum(self._written.values()),
-            }
-
-
-def labelling_candidates(index_path: str) -> List[Dict[str, Any]]:
-    """Turn a recorded index into candidate labels for a human to confirm.
-
-    A facing that the tag channel named carries a product name and a corroborated
-    price; those are the seeds. The rest are candidates grouped by position, to be
-    labelled in the confirmation screen rather than by hand from scratch.
-    """
-    seeds: List[Dict[str, Any]] = []
-    with open(index_path, "r", encoding="utf-8") as handle:
-        for line in handle:
-            line = line.strip()
-            if not line:
-                continue
-            entry = json.loads(line)
-            for track in entry.get("tracks") or []:
-                tag_identity = track.get("tag_identity") or {}
-                tag = track.get("tag") or {}
-                if not tag_identity.get("sku_id"):
-                    continue
-                seeds.append(
-                    {
-                        "frame": entry["frame"],
-                        "box": track.get("box"),
-                        "sku_id": tag_identity["sku_id"],
-                        "name_similarity": tag_identity.get("score"),
-                        "tag_price": tag.get("price"),
-                        "corroborated": bool(
-                            {"tax_consistent", "tax_pair_consistent", "discount_consistent"}
-                            & set(tag.get("flags") or [])
-                        ),
-                    }
-                )
-    return seeds
diff --git a/recognization/code/score_stream.py b/recognization/code/score_stream.py
deleted file mode 100644
index 09bc3d7..0000000
--- a/recognization/code/score_stream.py
+++ /dev/null
@@ -1,328 +0,0 @@
-"""Score a streamed clip's labels against ground truth: the G1 number.
-
-The stream path was reported as "53 of 131 tracks", which is not comparable to
-Van's 57% of labelled facings - the two count different things. But the clip is
-built from `add/image6.jpg`, which has labels, so matching each confirmed
-track's `label_box` to the labelled facings gives stream-path coverage directly.
-
-    python code/score_stream.py TRACKS.json --labels eval/labels/image6.json
-
-`TRACKS.json` is what `stream_client.py --tracks` writes: one row per track that
-settled on a label, with the box that label was measured on, already mapped back
-to source-image coordinates.
-
-Reported per run: correct / in-catalog facings, precision, unknown accepts, and
-tracks that matched no labelled facing. A track whose box overlaps a facing the
-catalog does not enrol is an unenrolled false accept - the dangerous kind, and
-the one the evaluator's own precision denominator used to hide.
-
-G1 has two halves - new covers at least as much as Van, and the lower bound of
-the paired-bootstrap difference clears -2 pp - and the second half needs the
-per-facing pairing, so every row carries the `facing` index it matched. Two
-reports plus the labels then answer the gate on the same facings:
-
-    python code/score_stream.py NEW.json --van VAN.json
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import os
-import random
-import sys
-from typing import Any, Dict, List, Optional, Sequence, Tuple
-
-current_dir = os.path.dirname(os.path.abspath(__file__))
-if current_dir not in sys.path:
-    sys.path.insert(0, current_dir)
-
-# A facing clipped by the window edge is not something a run could have named,
-# so the denominator keeps only facings at least this far inside the window.
-VISIBLE_MIN = 0.5
-
-# G1's second half: the lower bound of the paired difference must clear -2 pp.
-G1_BOUND_MIN = -0.02
-
-# Resampling draws for the paired bootstrap, and the seed that makes the bound
-# a number this repo can print twice.
-BOOTSTRAP_DRAWS = 10000
-BOOTSTRAP_SEED = 0
-
-
-def iou(a: Sequence[int], b: Sequence[int]) -> float:
-    ax1, ay1, ax2, ay2 = a
-    bx1, by1, bx2, by2 = b
-    inter_w = max(0, min(ax2, bx2) - max(ax1, bx1))
-    inter_h = max(0, min(ay2, by2) - max(ay1, by1))
-    inter = inter_w * inter_h
-    area_a = max(0, ax2 - ax1) * max(0, ay2 - ay1)
-    area_b = max(0, bx2 - bx1) * max(0, by2 - by1)
-    union = area_a + area_b - inter
-    return inter / union if union > 0 else 0.0
-
-
-def load_facings(labels_path: str) -> List[Dict[str, Any]]:
-    with open(labels_path) as handle:
-        payload = json.load(handle)
-    return payload["facings"]
-
-
-def enrolled_skus(stock_dir: str) -> set:
-    """Canonical SKUs with at least one enrolled view."""
-    from rebuild_catalog import sku_of
-
-    return {
-        sku_of(name) for name in os.listdir(stock_dir)
-        if name.lower().endswith((".jpg", ".jpeg", ".png"))
-    }
-
-
-def visible_fraction(box: Sequence[int], window: Sequence[int]) -> float:
-    """Share of a facing's area inside the window the client actually showed."""
-    wx0, wy0, wx1, wy1 = window
-    bx0, by0, bx1, by1 = box
-    inter_w = max(0, min(bx1, wx1) - max(bx0, wx0))
-    inter_h = max(0, min(by1, wy1) - max(by0, wy0))
-    area = max(0, bx1 - bx0) * max(0, by1 - by0)
-    return (inter_w * inter_h) / area if area > 0 else 0.0
-
-
-def reachable(facings: Sequence[Dict[str, Any]],
-              windows: Optional[Sequence[Sequence[int]]]
-              ) -> List[Dict[str, Any]]:
-    """Facings a run's windows showed well enough for the run to have named."""
-    if not windows:
-        return list(facings)
-    return [f for f in facings
-            if max(visible_fraction(f["box"], w) for w in windows) >= VISIBLE_MIN]
-
-
-def correct_facings(rows: Sequence[Dict[str, Any]]) -> set:
-    """Facing indices at least one track got right - one hit each, not one per track."""
-    return {row["facing"] for row in rows
-            if row["outcome"] == "correct" and row["facing"] is not None}
-
-
-def score(tracks: Sequence[Dict[str, Any]], facings: Sequence[Dict[str, Any]],
-          stock_dir: str, iou_min: float,
-          windows: Optional[Sequence[Sequence[int]]] = None
-          ) -> Dict[str, Any]:
-    """Score the labels a stream settled on.
-
-    `windows` are the source-image rects the client showed, as
-    [x0, y0, x1, y1] each. The denominator is the facings at least half inside
-    one of them, in x and y both: a 1280x720 window over a 1500x2000 photo sees
-    24 of the 63 in-catalog facings, and a facing clipped by the window edge is
-    not something any run could have named. A panned run shows a different rect
-    each frame, so the reachable set is the union. Without it the denominator is
-    every in-catalog facing.
-
-    A correct answer is counted before the unenrolled and unknown branches. The
-    text lane names SKUs the image lane has no view for, which is the point of
-    it, and scoring that as a false accept punishes the lane for working.
-    """
-    facings = reachable(facings, windows)
-    in_catalog = [f for f in facings if f["label"] != "unknown"]
-    enrolled = enrolled_skus(stock_dir)
-
-    correct = 0
-    wrong = 0
-    unenrolled_false = 0
-    unknown_false = 0
-    unmatched = 0
-    rows: List[Dict[str, Any]] = []
-    # Coverage is per facing, precision is per label. A track that restarts on
-    # the same product, or two tracks on one facing, is two claims but one
-    # facing: counting tracks as coverage reports 138% of a 24-facing window.
-    named: Dict[int, str] = {}
-
-    for track in tracks:
-        box = track.get("label_box") or []
-        if len(box) != 4:
-            unmatched += 1
-            continue
-        best, best_iou = None, 0.0
-        for facing in facings:
-            value = iou(box, facing["box"])
-            if value > best_iou:
-                best, best_iou = facing, value
-        if best is None or best_iou < iou_min:
-            unmatched += 1
-            rows.append({"track_id": track["track_id"], "predicted": track["sku_id"],
-                         "facing": None, "truth": None, "iou": round(best_iou, 3),
-                         "outcome": "unmatched"})
-            continue
-        truth = best["label"]
-        predicted = track["sku_id"]
-        if predicted == truth:
-            correct += 1
-            outcome = "correct"
-            named[best["index"]] = predicted
-        elif truth == "unknown":
-            unknown_false += 1
-            outcome = "unknown_false_accept"
-        elif truth not in enrolled:
-            unenrolled_false += 1
-            outcome = "unenrolled_false_accept"
-        else:
-            wrong += 1
-            outcome = "wrong"
-        rows.append({"track_id": track["track_id"], "predicted": predicted,
-                     "facing": best["index"], "truth": truth,
-                     "iou": round(best_iou, 3), "outcome": outcome})
-
-    shown = correct + wrong + unenrolled_false + unknown_false
-    return {
-        "in_catalog_facings": len(in_catalog),
-        "facings_named": len(named),
-        "tracks_scored": len(tracks),
-        "correct": correct,
-        "wrong": wrong,
-        "unenrolled_false_accepts": unenrolled_false,
-        "unknown_false_accepts": unknown_false,
-        "unmatched_tracks": unmatched,
-        "shown": shown,
-        "coverage": round(len(named) / len(in_catalog), 4) if in_catalog else 0.0,
-        "precision": round(correct / shown, 4) if shown else 0.0,
-        # The window set travels with the report: a paired comparison has to
-        # know what each run was shown, not just what it named.
-        "windows": [list(w) for w in windows] if windows else None,
-        "rows": rows,
-    }
-
-
-def paired_bootstrap(van: Dict[str, Any], new: Dict[str, Any],
-                     facings: Sequence[Dict[str, Any]],
-                     draws: int = BOOTSTRAP_DRAWS,
-                     seed: int = BOOTSTRAP_SEED) -> Dict[str, Any]:
-    """G1's second half: new minus Van, per facing, with the 95 % lower bound.
-
-    Both reports are graded on one facing set - the in-catalog facings either
-    run's windows showed - so a facing one run panned past cannot flatter the
-    other. Resampling draws facings with replacement rather than comparing two
-    independent rates, because the two runs saw the same photo: pairing removes
-    the facing-to-facing variance that otherwise swamps a 27-facing sample.
-
-    Percentile method on the sorted draw differences; the seed is fixed so the
-    number in a report is one this repo reproduces.
-    """
-    windows = list(van.get("windows") or []) + list(new.get("windows") or [])
-    shared = sorted(f["index"] for f in reachable(facings, windows)
-                    if f["label"] != "unknown")
-    if not shared:
-        return {"facings": 0, "difference": 0.0, "lower_95": 0.0, "upper_95": 0.0,
-                "van": 0, "new": 0, "draws": draws, "seed": seed}
-
-    van_hits = correct_facings(van["rows"])
-    new_hits = correct_facings(new["rows"])
-    van_vector = [1 if i in van_hits else 0 for i in shared]
-    new_vector = [1 if i in new_hits else 0 for i in shared]
-    n = len(shared)
-
-    rng = random.Random(seed)
-    spread = []
-    for _ in range(draws):
-        van_sum = new_sum = 0
-        for _ in range(n):
-            # The same draw index into both vectors: that is the pairing.
-            drawn = rng.randrange(n)
-            van_sum += van_vector[drawn]
-            new_sum += new_vector[drawn]
-        spread.append((new_sum - van_sum) / n)
-    spread.sort()
-    tail = max(1, int(0.025 * draws))
-    return {
-        "facings": n,
-        "van": sum(van_vector),
-        "new": sum(new_vector),
-        "van_rate": round(sum(van_vector) / n, 4),
-        "new_rate": round(sum(new_vector) / n, 4),
-        "difference": round((sum(new_vector) - sum(van_vector)) / n, 4),
-        "lower_95": round(spread[tail - 1], 4),
-        "upper_95": round(spread[min(draws - 1, int(0.975 * draws))], 4),
-        "draws": draws,
-        "seed": seed,
-    }
-
-
-def load_run(path: str, window: Optional[str] = None
-             ) -> Tuple[Dict[str, Any], Optional[List[Tuple[int, ...]]]]:
-    """A stream_client --tracks dump and the source-image windows it showed."""
-    with open(path) as handle:
-        payload = json.load(handle)
-    if window:
-        windows = [tuple(int(part) for part in window.split(","))]
-    elif payload.get("windows"):
-        windows = [tuple(w) for w in payload["windows"]]
-    else:
-        windows = None
-    return payload, windows
-
-
-def main(argv: Optional[Sequence[str]] = None) -> int:
-    parser = argparse.ArgumentParser(description="Score streamed labels against ground truth")
-    parser.add_argument("tracks", help="JSON written by stream_client.py --tracks")
-    parser.add_argument("--labels", default="eval/labels/image6.json")
-    parser.add_argument("--stock-dir", default="stock-lopia-clean")
-    parser.add_argument("--iou", type=float, default=0.5)
-    parser.add_argument("--json", dest="json_path", default=None)
-    parser.add_argument("--window", default=None,
-                        help="x0,y0,x1,y1 the client showed; the denominator is "
-                             "restricted to facings at least half inside it")
-    parser.add_argument("--van", default=None,
-                        help="Van baseline track dump on the same clip; scores G1's "
-                             "paired-bootstrap half against it")
-    parser.add_argument("--draws", type=int, default=BOOTSTRAP_DRAWS)
-    parser.add_argument("--seed", type=int, default=BOOTSTRAP_SEED)
-    parser.add_argument("--verbose", action="store_true", help="print every scored track")
-    args = parser.parse_args(argv)
-
-    facings = load_facings(args.labels)
-    payload, windows = load_run(args.tracks, args.window)
-    report = score(payload["tracks"], facings, args.stock_dir, args.iou, windows)
-    report["source"] = payload.get("source")
-    report["pace"] = payload.get("pace")
-    report["seconds"] = payload.get("seconds")
-
-    print(f"{os.path.basename(args.tracks)}  pace={report['pace']}  "
-          f"{report['seconds']}s")
-    print(f"  facings named {report['facings_named']}/{report['in_catalog_facings']} "
-          f"in-catalog ({report['coverage']:.0%})  labels correct {report['correct']}  "
-          f"precision {report['precision']:.0%}  shown {report['shown']}")
-    print(f"  wrong {report['wrong']}  unenrolled false accepts "
-          f"{report['unenrolled_false_accepts']}  unknown false accepts "
-          f"{report['unknown_false_accepts']}  unmatched tracks {report['unmatched_tracks']}")
-    if args.verbose:
-        for row in report["rows"]:
-            print(f"    t{row['track_id']:4d} {row['outcome']:24s} "
-                  f"facing={row['facing']} pred={row['predicted']} "
-                  f"truth={row['truth']} iou={row['iou']}")
-
-    if args.van:
-        van_payload, van_windows = load_run(args.van, args.window)
-        van_report = score(van_payload["tracks"], facings, args.stock_dir,
-                           args.iou, van_windows)
-        paired = paired_bootstrap(van_report, report, facings, args.draws, args.seed)
-        report["van"] = {k: van_report[k] for k in
-                         ("in_catalog_facings", "facings_named", "coverage",
-                          "correct", "wrong", "precision", "shown")}
-        report["g1_paired"] = paired
-        print(f"  G1 vs {os.path.basename(args.van)}: new {paired['new']}/"
-              f"{paired['facings']} ({paired['new_rate']:.0%}) - van {paired['van']}/"
-              f"{paired['facings']} ({paired['van_rate']:.0%}) = "
-              f"{paired['difference'] * 100:+.1f} pp")
-        print(f"    paired bootstrap {paired['draws']} draws seed {paired['seed']}: "
-              f"95% bounds {paired['lower_95'] * 100:+.1f} pp to "
-              f"{paired['upper_95'] * 100:+.1f} pp; lower bound clears "
-              f"{G1_BOUND_MIN * 100:+.0f} pp: "
-              f"{'yes' if paired['lower_95'] >= G1_BOUND_MIN else 'NO'}")
-
-    if args.json_path:
-        with open(args.json_path, "w") as handle:
-            json.dump(report, handle, indent=2)
-    return 0
-
-
-if __name__ == "__main__":
-    sys.exit(main())
diff --git a/recognization/code/shelf_map.py b/recognization/code/shelf_map.py
deleted file mode 100644
index c644dfb..0000000
--- a/recognization/code/shelf_map.py
+++ /dev/null
@@ -1,348 +0,0 @@
-"""
-The shelf as a plane, not a stream of boxes.
-
-Every measurement so far treats a frame as the world: a facing is "a track id",
-and a track id dies the moment the camera pans past and comes back. Label
-inheritance, drift checks and restart counters were each correct fixes for that,
-and all of them are symptoms. The shelf does not move; the camera does.
-
-This module keeps a per-session **shelf map**. Each frame is registered to the map
-by its content shift, every detection is placed in shelf coordinates, and a
-detection that lands where a facing already is *merges* into it. Identity votes and
-tag readings accumulate on the physical facing rather than on a transient track.
-
-That is what the current literature does for retail: RetailGlue (CVPR 2026W) builds
-shelf panoramas by matching products across frames and reports 88.4 % stitching F1,
-and the 7-Eleven Taiwan system (Sci. Rep. 2025, 7,000+ stores) stitches "virtual
-shelves" before checking planograms.
-
-**What is measured, and what is not.** The synthetic walk registers to within
-0.01 px of its ground truth - but it is a pure translation of a single photograph,
-so that validates the harness, not handheld video. The number that matters (real
-registration error, and the duplicate rate that follows from it) needs real
-footage, which `record.py` exists to collect.
-"""
-
-from __future__ import annotations
-
-import threading
-import uuid
-from dataclasses import dataclass, field
-from typing import Any, Dict, List, Optional, Sequence, Tuple
-
-# A frame whose content shift cannot be estimated this confidently is treated as
-# discontinuous: the map starts a new section rather than guessing a position.
-MIN_REGISTRATION_RESPONSE = 0.20
-# Two detections are the same physical facing when their shelf boxes overlap this
-# much. Measured: adjacent facings of one product overlap ~0 by construction.
-MERGE_IOU = 0.45
-
-
-def _iou(a: Sequence[int], b: Sequence[int]) -> float:
-    x1, y1 = max(a[0], b[0]), max(a[1], b[1])
-    x2, y2 = min(a[2], b[2]), min(a[3], b[3])
-    intersection = max(0, x2 - x1) * max(0, y2 - y1)
-    area_a = max(0, a[2] - a[0]) * max(0, a[3] - a[1])
-    area_b = max(0, b[2] - b[0]) * max(0, b[3] - b[1])
-    return intersection / max(1.0, float(area_a + area_b - intersection))
-
-
-@dataclass
-class Facing:
-    """One physical position on the shelf, with everything observed there."""
-
-    facing_id: str
-    shelf_box: List[int]
-    row: int = 0
-    observations: int = 0
-    first_seq: int = 0
-    last_seq: int = 0
-    identity_votes: Dict[str, float] = field(default_factory=dict)
-    tag_votes: Dict[str, float] = field(default_factory=dict)
-    tag_readings: List[Dict[str, Any]] = field(default_factory=list)
-    seen_track_ids: set = field(default_factory=set)
-
-    def vote_identity(self, sku_id: Optional[str], weight: float = 1.0) -> None:
-        if sku_id:
-            self.identity_votes[sku_id] = self.identity_votes.get(sku_id, 0.0) + weight
-
-    def vote_tag(self, sku_id: Optional[str], reading: Optional[Dict[str, Any]] = None) -> None:
-        if sku_id:
-            self.tag_votes[sku_id] = self.tag_votes.get(sku_id, 0.0) + 1.0
-        if reading:
-            self.tag_readings.append(reading)
-
-    def best_identity(self) -> Tuple[Optional[str], float]:
-        """The identity the facing's pooled evidence supports, and its agreement."""
-        if not self.identity_votes:
-            return None, 0.0
-        sku_id, weight = max(self.identity_votes.items(), key=lambda item: item[1])
-        total = sum(self.identity_votes.values())
-        return sku_id, weight / max(1e-6, total)
-
-    def best_tag_identity(self) -> Tuple[Optional[str], float]:
-        if not self.tag_votes:
-            return None, 0.0
-        sku_id, weight = max(self.tag_votes.items(), key=lambda item: item[1])
-        total = sum(self.tag_votes.values())
-        return sku_id, weight / max(1e-6, total)
-
-    def as_payload(self) -> Dict[str, Any]:
-        identity, agreement = self.best_identity()
-        tag_identity, tag_agreement = self.best_tag_identity()
-        return {
-            "facing_id": self.facing_id,
-            "box": list(self.shelf_box),
-            "row": self.row,
-            "observations": self.observations,
-            "track_ids": len(self.seen_track_ids),
-            "sku_id": identity,
-            "identity_agreement": round(agreement, 3),
-            "tag_sku_id": tag_identity,
-            "tag_agreement": round(tag_agreement, 3),
-            "tag_readings": self.tag_readings[-3:],
-        }
-
-
-class ShelfMap:
-    """Registers frames and accumulates facings in shelf coordinates."""
-
-    def __init__(self, settings: Optional[Any] = None) -> None:
-        self._lock = threading.Lock()
-        self._prev_gray: Optional[Any] = None
-        self._offset: List[float] = [0.0, 0.0]      # shelf position of the frame origin
-        self._facings: List[Facing] = []
-        self._orb = None
-        self.registrations = 0
-        self.registration_failures = 0
-        self.anchors = 0
-        self.continuations = 0
-        self.detections = 0
-        self.merges = 0
-        self.responses: List[float] = []
-
-    # ------------------------------------------------------------- registration
-    def _gray(self, frame: Any, scale: float = 0.5) -> Any:
-        import cv2
-
-        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-        if scale != 1.0:
-            gray = cv2.resize(gray, None, fx=scale, fy=scale, interpolation=cv2.INTER_AREA)
-        return gray
-
-    def register(self, frame: Any) -> Optional[Tuple[float, float]]:
-        """Content shift from the previous frame, in full-resolution pixels.
-
-        Phase correlation first: it is the right tool for a translating scene and
-        costs a couple of milliseconds. ORB + RANSAC only when the correlation is
-        weak (blur, a large jump, a scene with no texture), because that is the case
-        where a fast estimator quietly returns nonsense.
-        """
-        import cv2
-        import numpy as np
-
-        gray = self._gray(frame)
-        with self._lock:
-            previous = self._prev_gray
-            self._prev_gray = gray
-        if previous is None or previous.shape != gray.shape:
-            return (0.0, 0.0)
-        window = cv2.createHanningWindow((gray.shape[1], gray.shape[0]), cv2.CV_32F)
-        (dx, dy), response = cv2.phaseCorrelate(previous.astype(np.float32), gray.astype(np.float32), window)
-        self.responses.append(float(response))
-        if response >= MIN_REGISTRATION_RESPONSE:
-            self.registrations += 1
-            return (dx / 0.5, dy / 0.5)          # back to full resolution
-
-        shift = self._orb_shift(previous, gray)
-        if shift is None:
-            self.registration_failures += 1
-            return None
-        self.registrations += 1
-        return (shift[0] / 0.5, shift[1] / 0.5)
-
-    def _orb_shift(self, previous: Any, current: Any) -> Optional[Tuple[float, float]]:
-        import cv2
-        import numpy as np
-
-        if self._orb is None:
-            self._orb = cv2.ORB_create(nfeatures=1500, fastThreshold=10)
-        key_a, desc_a = self._orb.detectAndCompute(previous, None)
-        key_b, desc_b = self._orb.detectAndCompute(current, None)
-        if desc_a is None or desc_b is None or len(key_a) < 20 or len(key_b) < 20:
-            return None
-        matches = cv2.BFMatcher(cv2.NORM_HAMMING).knnMatch(desc_a, desc_b, k=2)
-        good = [m for m, n in matches if m.distance < 0.75 * n.distance]
-        if len(good) < 12:
-            return None
-        src = np.float32([key_a[m.queryIdx].pt for m in good]).reshape(-1, 1, 2)
-        dst = np.float32([key_b[m.trainIdx].pt for m in good]).reshape(-1, 1, 2)
-        matrix, _ = cv2.estimateAffinePartial2D(src, dst, method=cv2.RANSAC, ransacReprojThreshold=3.0)
-        if matrix is None:
-            return None
-        return (float(matrix[0, 2]), float(matrix[1, 2]))
-
-    # ------------------------------------------------------------------ facings
-    def _anchor_to_map(
-        self, detections: Sequence[Sequence[int]], offset_x: float, offset_y: float
-    ) -> Tuple[float, float]:
-        """Correct the chained offset by matching detections against the map.
-
-        Chaining pairwise shifts drifts: measured over a 79-step walk with *perfect*
-        pairwise registration, the accumulated x offset reached 2047 px on a 1500 px
-        shelf, the same facing landed in two places, and a shelf of ~60 facings
-        became 334 with 113 of them seen once. Drift is inherent to chaining - the
-        fix is to anchor the frame to the structure already on the map, which is
-        what the retail-stitching literature does with product correspondences.
-
-        One ICP-style step is enough: for every detection that has a facing nearby,
-        the correction it implies is a vote, and the median vote wins.
-        """
-        if not self._facings or len(detections) < 4:
-            return offset_x, offset_y
-        corrections = []
-        search = 40.0
-        for box in detections:
-            centre_x = (box[0] + box[2]) / 2.0 + offset_x
-            centre_y = (box[1] + box[3]) / 2.0 + offset_y
-            best, best_distance = None, search
-            for facing in self._facings:
-                facing_x = (facing.shelf_box[0] + facing.shelf_box[2]) / 2.0
-                facing_y = (facing.shelf_box[1] + facing.shelf_box[3]) / 2.0
-                distance = max(abs(facing_x - centre_x), abs(facing_y - centre_y))
-                if distance < best_distance:
-                    best, best_distance = facing, distance
-            if best is not None:
-                corrections.append(
-                    (
-                        (best.shelf_box[0] + best.shelf_box[2]) / 2.0 - ((box[0] + box[2]) / 2.0 + offset_x),
-                        (best.shelf_box[1] + best.shelf_box[3]) / 2.0 - ((box[1] + box[3]) / 2.0 + offset_y),
-                    )
-                )
-        if len(corrections) < 4:
-            return offset_x, offset_y
-        dx = sorted(c[0] for c in corrections)[len(corrections) // 2]
-        dy = sorted(c[1] for c in corrections)[len(corrections) // 2]
-        self.anchors += 1
-        return offset_x + dx, offset_y + dy
-
-    def update(self, seq: int, frame: Any, tracks: Sequence[Dict[str, Any]]) -> None:
-        """Register the frame, then place or merge every detection on the shelf."""
-        shift = self.register(frame)
-        boxes = [
-            [int(v) for v in (track.get("box") or track.get("raw_box"))]
-            for track in tracks
-            if (track.get("box") or track.get("raw_box")) and len(track.get("box") or track.get("raw_box")) == 4
-        ]
-        with self._lock:
-            if shift is None:
-                # A discontinuous frame: keep the last known offset rather than
-                # inventing one, so its detections land where the shelf is.
-                shift = (0.0, 0.0)
-            # `shift` is how far the CONTENT moved in the frame, so the frame's
-            # origin moves the opposite way along the shelf. Adding it instead
-            # (measured) walks the map backwards: the same facing lands 50 px away
-            # in the next frame and a shelf of ~60 facings becomes 857.
-            self._offset[0] -= shift[0]
-            self._offset[1] -= shift[1]
-            if self._facings:
-                self._offset[0], self._offset[1] = self._anchor_to_map(
-                    boxes, self._offset[0], self._offset[1]
-                )
-            offset_x, offset_y = self._offset
-            for track in tracks:
-                box = track.get("box") or track.get("raw_box")
-                if not box or len(box) != 4:
-                    continue
-                self.detections += 1
-                shelf_box = [
-                    int(round(box[0] + offset_x)), int(round(box[1] + offset_y)),
-                    int(round(box[2] + offset_x)), int(round(box[3] + offset_y)),
-                ]
-                track_id = track.get("track_id")
-                facing = self._find(shelf_box, int(track_id) if track_id is not None else None)
-                if facing is None:
-                    facing = Facing(
-                        facing_id=f"face_{uuid.uuid4().hex[:8]}",
-                        shelf_box=shelf_box,
-                        first_seq=seq,
-                    )
-                    self._facings.append(facing)
-                else:
-                    self.merges += 1
-                    # Average the box: repeated observations of a static object
-                    # should converge, and the average is what the eye expects.
-                    for index in range(4):
-                        facing.shelf_box[index] = int(round((facing.shelf_box[index] * facing.observations + shelf_box[index]) / (facing.observations + 1)))
-                facing.observations += 1
-                facing.last_seq = seq
-                identity = track.get("identity") or {}
-                if identity.get("state") == "recognized":
-                    facing.vote_identity(identity.get("sku_id"), float(identity.get("score") or 1.0))
-                tag_identity = track.get("tag_identity") or {}
-                tag = track.get("price_tag") or {}
-                if tag_identity.get("sku_id") or tag.get("state") == "readable":
-                    facing.vote_tag(tag_identity.get("sku_id"), tag if tag else None)
-                if track.get("track_id") is not None:
-                    facing.seen_track_ids.add(int(track["track_id"]))
-
-    def _find(self, shelf_box: Sequence[int], track_id: Optional[int] = None) -> Optional[Facing]:
-        """The facing a detection belongs to.
-
-        Continuity first: if this track id was already placed nearby, it is the same
-        facing, and no overlap test is needed - the tracker already decided that.
-        Geometry only when the id is new (a pan that outran the tracker, or the
-        tracker renumbering). Measured: with geometry alone, box jitter split the
-        same facing often enough that the map held 210 facings where the tracker
-        held 156 ids - it was adding duplicates, not removing them.
-        """
-        width = max(1, shelf_box[2] - shelf_box[0])
-        height = max(1, shelf_box[3] - shelf_box[1])
-        if track_id is not None:
-            radius = 0.75 * max(width, height)
-            best, best_distance = None, radius
-            for facing in self._facings:
-                if track_id not in facing.seen_track_ids:
-                    continue
-                distance = max(
-                    abs((facing.shelf_box[0] + facing.shelf_box[2]) / 2.0 - (shelf_box[0] + shelf_box[2]) / 2.0),
-                    abs((facing.shelf_box[1] + facing.shelf_box[3]) / 2.0 - (shelf_box[1] + shelf_box[3]) / 2.0),
-                )
-                if distance < best_distance:
-                    best, best_distance = facing, distance
-            if best is not None:
-                self.continuations += 1
-                return best
-        best, best_overlap = None, MERGE_IOU
-        for facing in self._facings:
-            overlap = _iou(shelf_box, facing.shelf_box)
-            if overlap > best_overlap:
-                best, best_overlap = facing, overlap
-        return best
-
-    # ------------------------------------------------------------------ reporting
-    def facings(self) -> List[Facing]:
-        with self._lock:
-            return list(self._facings)
-
-    def stats(self) -> Dict[str, Any]:
-        with self._lock:
-            facings = list(self._facings)
-            responses = list(self.responses[-200:])
-        consistent = 0
-        for facing in facings:
-            sku_id, agreement = facing.best_identity()
-            if sku_id and agreement >= 0.8:
-                consistent += 1
-        return {
-            "facings": len(facings),
-            "detections": self.detections,
-            "merges": self.merges,
-            "continuations": self.continuations,
-            "registrations": self.registrations,
-            "anchored": self.anchors,
-            "registration_failures": self.registration_failures,
-            "median_response": round(sorted(responses)[len(responses) // 2], 3) if responses else None,
-            "identity_consistent": consistent,
-            "offset": [round(value, 1) for value in self._offset],
-        }
diff --git a/recognization/code/stream_client.py b/recognization/code/stream_client.py
deleted file mode 100644
index cbf4d7f..0000000
--- a/recognization/code/stream_client.py
+++ /dev/null
@@ -1,365 +0,0 @@
-"""
-Reference live client for the Dorayaki recognition server.
-
-Streams frames over WebSocket /v1/stream and prints what the server sees. This
-is the documented example for capture sources that are not a browser: a cart
-camera, a wearable camera, a rail camera, a robot.
-
-Usage (from recognization/):
-    .venv/bin/python code/stream_client.py --source image.jpg --pan 12 --seconds 20
-    .venv/bin/python code/stream_client.py --source 0                  # webcam index
-    .venv/bin/python code/stream_client.py --source clip.mp4 --seconds 30
-    .venv/bin/python code/stream_client.py --url ws://host:8080/v1/stream --token SECRET
-
-Frame protocol: 4-byte big-endian frame id + JPEG bytes.
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import os
-import statistics
-import sys
-import time
-from typing import Any, Dict, List, Optional
-
-current_dir = os.path.dirname(os.path.abspath(__file__))
-project_root = os.path.dirname(current_dir)
-if os.path.exists(os.path.join(project_root, "stock")):
-    os.chdir(project_root)
-if current_dir not in sys.path:
-    sys.path.insert(0, current_dir)
-
-import cv2  # noqa: E402
-
-from websockets.sync.client import connect  # noqa: E402
-
-
-def percentile(values: List[float], pct: float) -> float:
-    if not values:
-        return 0.0
-    ordered = sorted(values)
-    index = min(len(ordered) - 1, max(0, int(round((pct / 100.0) * (len(ordered) - 1)))))
-    return ordered[index]
-
-
-class FrameSource:
-    """Yields BGR frames from a camera, a video file, or a still image pan."""
-
-    def __init__(self, source: str, *, width: int, pan_step: int, pan_width: int = 1280):
-        self.source = source
-        self.width = width
-        self.pan_step = pan_step
-        self.pan_width = pan_width
-        self.capture: Optional[cv2.VideoCapture] = None
-        self.image = None
-        self.pan_x = 0
-        self.pan_direction = 1
-        # The downscale `frame()` applied to the window. A box the server
-        # measured is in the sent frame's pixels, so mapping it back to the
-        # source divides by this.
-        self.scale = 1.0
-        # Where the last frame was cut from the source image. (0, 0) for a
-        # camera or a video, whose frames are the whole image.
-        self.offset = (0, 0)
-        # The source-image rects the window showed, so an evaluator can restrict
-        # its denominator to the facings this run could actually have seen.
-        # A list, because a panned run shows a different rect each frame and the
-        # reachable set is their union. Both axes matter: a 1280-wide window
-        # over a 1500-wide photo is x-limited as well as y-limited.
-        self.windows: List[Tuple[int, int, int, int]] = []
-        if source.isdigit():
-            self.capture = cv2.VideoCapture(int(source))
-        elif os.path.isfile(source) and source.lower().endswith((".mp4", ".mov", ".avi", ".mkv", ".webm")):
-            self.capture = cv2.VideoCapture(source)
-        else:
-            self.image = cv2.imread(source)
-            if self.image is None:
-                raise SystemExit(f"cannot read source {source}")
-
-    def frame(self, index: int):
-        """One frame, plus where it was cut from the source image.
-
-        The offset is what lets an evaluator map a track's box back to the
-        source image's coordinates: a label measured on a pan window is only
-        comparable to ground truth once the window's origin is added back.
-        """
-        if self.image is not None:
-            height, width = self.image.shape[:2]
-            # Full height, 9:16 wide: the same window the app's replay source
-            # sends. A window only `pan_width` tall shows one shelf row, which
-            # reads as a matcher failure when it is only the crop.
-            window_h = height
-            window_w = min(int(window_h * 9 / 16), width)
-            max_x = max(0, width - window_w)
-            x = int(max(0, min(self.pan_x, max_x)))
-            window = self.image[:, x:x + window_w]
-            # Scale to the send size here, so the frame this yields is exactly
-            # what the app sends and `encode` has nothing left to do. The pan
-            # step is in sent-frame pixels, so it is divided by this scale.
-            self.scale = min(1.0, self.width / float(window_w)) if self.width else 1.0
-            if self.scale < 1.0:
-                window = cv2.resize(
-                    window,
-                    (int(round(window.shape[1] * self.scale)), int(round(window.shape[0] * self.scale))),
-                    interpolation=cv2.INTER_AREA,
-                )
-            self.offset = (x, 0)
-            self.windows.append((x, 0, x + window_w, window_h))
-            self.pan_x += self.pan_direction * self.pan_step / self.scale
-            # Clamp the accumulator, not just the slice origin: a negative
-            # `pan_x` would satisfy `<= 0` on every later frame and flip the
-            # direction each time, parking the window at the left edge.
-            if self.pan_x >= max_x:
-                self.pan_x = float(max_x)
-                self.pan_direction = -1
-            elif self.pan_x <= 0:
-                self.pan_x = 0.0
-                self.pan_direction = 1
-            return window
-        if self.capture is not None:
-            ok, frame = self.capture.read()
-            if not ok:  # loop video files
-                self.capture.set(cv2.CAP_PROP_POS_FRAMES, 0)
-                ok, frame = self.capture.read()
-            if not ok:
-                return None
-            return frame
-        return None
-
-    def release(self) -> None:
-        if self.capture is not None:
-            self.capture.release()
-
-
-def encode(frame, width: int, quality: int) -> bytes:
-    height, current_width = frame.shape[:2]
-    if width and current_width > width:
-        scale = width / float(current_width)
-        frame = cv2.resize(frame, (int(width), int(round(height * scale))))
-    ok, buffer = cv2.imencode(".jpg", frame, [cv2.IMWRITE_JPEG_QUALITY, quality])
-    if not ok:
-        raise RuntimeError("JPEG encode failed")
-    return buffer.tobytes()
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser(description="Dorayaki live stream client")
-    parser.add_argument("--url", default="ws://127.0.0.1:8080/v1/stream")
-    parser.add_argument("--token", default=os.environ.get("DORAYAKI_API_TOKEN"))
-    parser.add_argument("--source", default="image.jpg", help="camera index, video path, or image path")
-    parser.add_argument("--pan", type=float, default=12.0, help="pixels per frame when panning a still image")
-    parser.add_argument("--seconds", type=float, default=20.0)
-    parser.add_argument("--json", action="store_true", help="print a JSON summary instead of the table")
-    parser.add_argument("--tracks", default=None,
-                        help="write per-track labels and their boxes (source-image "
-                             "coordinates) as JSON, for scoring against ground truth")
-    parser.add_argument("--quiet", action="store_true", help="only print the final summary")
-    parser.add_argument("--width", type=int, default=1280, help="send width")
-    parser.add_argument("--quality", type=int, default=75)
-    parser.add_argument("--max-inflight", type=int, default=1)
-    parser.add_argument("--pace", choices=["send", "result"], default="send",
-                        help="'send': keep the socket busy (frames drop server-side); "
-                             "'result': send the next frame only after a result, so the "
-                             "pan stays continuous (the mode to use for stability checks)")
-    parser.add_argument("--method", default=None, help="surf | orb | brisk | hybrid")
-    args = parser.parse_args()
-
-    url = args.url
-    headers = {"Authorization": f"Bearer {args.token}"} if args.token else None
-
-    source = FrameSource(args.source, width=args.width, pan_step=args.pan)
-    rtts: List[float] = []
-    detect_times: List[float] = []
-    frames_sent = 0
-    results = 0
-    drops = 0
-    first_label: Dict[int, float] = {}
-    label_latencies: List[float] = []
-    labels: Dict[int, str] = {}
-    pending_frame: Optional[int] = None
-    # Per track: the label it settled on, the box that label was measured on,
-    # and the pan offset of the frame that box lives in. Written out with
-    # --tracks so an evaluator can match labels to ground truth.
-    track_records: Dict[int, Dict[str, Any]] = {}
-    frame_offsets: Dict[int, Tuple[int, int]] = {}
-    # The scale `encode` applied to that frame. A box the server measured is in
-    # the sent frame's pixels, so mapping it back to the source needs both the
-    # pan offset and this factor; without it a `--width` below the source size
-    # shrinks every box and the evaluator matches the wrong facing.
-    frame_scales: Dict[int, float] = {}
-    label_changes = 0
-    unexplained_changes = 0
-    relabel_seen: Dict[int, int] = {}
-    restart_seen: Dict[int, int] = {}
-    inherited_seen: Dict[int, bool] = {}
-    seen_tracks: set = set()
-    last_summary: Dict[str, Any] = {}
-    started = time.perf_counter()
-    in_flight: Dict[int, float] = {}
-    pending_frame: Optional[int] = None
-
-    with connect(url, max_size=8 * 1024 * 1024, additional_headers=headers) as socket:
-        hello = json.loads(socket.recv(timeout=10))
-        if hello.get("type") != "hello":
-            raise SystemExit(f"unexpected first message: {hello}")
-        if not args.quiet:
-            print(f"hello: session {hello['session_id']} model {hello['model']['version']} "
-                  f"device {hello['model']['device']} config {hello['config']}")
-        if args.method:
-            socket.send(json.dumps({"type": "config", "method": args.method}))
-            ack = json.loads(socket.recv(timeout=10))
-            if not args.quiet:
-                print(f"config ack: {ack.get('config')}")
-
-        def consume(message: Dict[str, Any]) -> None:
-            """Fold one result into the running metrics."""
-            nonlocal results, last_summary, label_changes, unexplained_changes
-            if message.get("type") != "result":
-                if not args.quiet:
-                    print(f"server message: {message}")
-                return
-            received_at = time.perf_counter()
-            sent_at = in_flight.pop(message["frame_id"], None)
-            if sent_at is not None:
-                rtts.append((received_at - sent_at) * 1000.0)
-            detect_times.append(message["timing_ms"]["detect"])
-            results += 1
-            last_summary = message["summary"]
-            now = time.perf_counter()
-            for track in message["tracks"]:
-                track_id = track["track_id"]
-                seen_tracks.add(track_id)
-                identity = track["identity"]
-                if identity["state"] != "recognized":
-                    continue
-                sku = identity["sku_id"]
-                relabels = track.get("relabels", 0)
-                restarts = track.get("restarts", 0)
-                if restarts > restart_seen.get(track_id, 0):
-                    # The id was restarted: earlier labels describe another object.
-                    labels.pop(track_id, None)
-                    first_label.pop(track_id, None)
-                inherited = bool(track.get("inherited"))
-                if track_id not in first_label:
-                    first_label[track_id] = now
-                    if not inherited:
-                        label_latencies.append((now - started) * 1000.0)
-                # Only a measured-to-measured change on the same object is a
-                # label change; inherited labels describe the same product
-                # under a new track id.
-                if track_id in labels and labels[track_id] != sku:
-                    if not inherited and not inherited_seen.get(track_id):
-                        label_changes += 1
-                        if relabels <= relabel_seen.get(track_id, 0):
-                            unexplained_changes += 1
-                labels[track_id] = sku
-                # The box the label is about, in source-image coordinates. The
-                # offset comes from the frame the box was measured on, not the
-                # frame this result arrived with: the pan has moved on since.
-                offset = frame_offsets.get(track.get("label_frame_id"), (0, 0))
-                inv = 1.0 / frame_scales.get(track.get("label_frame_id"), 1.0)
-                label_box = track.get("label_box") or track.get("box") or []
-                if len(label_box) == 4:
-                    track_records[track_id] = {
-                        "track_id": track_id,
-                        "sku_id": sku,
-                        "label_box": [
-                            label_box[0] * inv + offset[0], label_box[1] * inv + offset[1],
-                            label_box[2] * inv + offset[0], label_box[3] * inv + offset[1],
-                        ],
-                        "label_frame_id": track.get("label_frame_id"),
-                        "result_frame_id": message["frame_id"],
-                        "inherited": inherited,
-                        "restarts": restarts,
-                    }
-                inherited_seen[track_id] = inherited
-                relabel_seen[track_id] = relabels
-                restart_seen[track_id] = restarts
-            if not args.quiet and results % 10 == 0:
-                print(f"f{message['seq']:04d} tracks={len(message['tracks']):3d} "
-                      f"recognized={last_summary['recognized']:3d} "
-                      f"unconfirmed={last_summary.get('unconfirmed', 0):3d} "
-                      f"detect={message['timing_ms']['detect']:5.1f}ms "
-                      f"rtt={rtts[-1]:6.1f}ms fps={results / (now - started):5.2f}")
-        frame_id = 0
-        index = 0
-        while time.perf_counter() - started < args.seconds:
-            frame = source.frame(index)
-            if frame is None:
-                break
-            index += 1
-            frame_id = (frame_id + 1) & 0xFFFFFFFF
-            frame_offsets[frame_id] = source.offset
-            frame_scales[frame_id] = source.scale
-            payload = encode(frame, args.width, args.quality)
-            if len(in_flight) >= args.max_inflight:
-                if pending_frame is not None:
-                    drops += 1
-                pending_frame = frame_id
-                socket.send(frame_id.to_bytes(4, "big") + payload, text=False)
-                frames_sent += 1
-            else:
-                socket.send(frame_id.to_bytes(4, "big") + payload, text=False)
-                frames_sent += 1
-            in_flight[frame_id] = time.perf_counter()
-            if args.pace == "result":
-                # One frame in flight: the next frame is captured only after this
-                # one was answered, so the pan stays continuous.
-                while in_flight:
-                    try:
-                        consume(json.loads(socket.recv(timeout=5)))
-                    except TimeoutError:
-                        print("timeout waiting for a result")
-                        break
-                continue
-
-            try:
-                consume(json.loads(socket.recv(timeout=5)))
-            except TimeoutError:
-                print("timeout waiting for a result")
-                break
-
-    source.release()
-    elapsed = time.perf_counter() - started
-    summary = {
-        "seconds": round(elapsed, 1),
-        "frames_sent": frames_sent,
-        "results": results,
-        "results_per_s": round(results / elapsed, 2) if elapsed else 0,
-        "frames_dropped_by_client": drops,
-        "rtt_ms_p50": round(statistics.median(rtts), 1) if rtts else None,
-        "rtt_ms_p95": round(percentile(rtts, 95), 1) if rtts else None,
-        "server_detect_ms_p50": round(statistics.median(detect_times), 1) if detect_times else None,
-        "server_detect_ms_p95": round(percentile(detect_times, 95), 1) if detect_times else None,
-        "tracks_seen": len(seen_tracks),
-        "tracks_labelled": len(labels),
-        "label_changes": label_changes,
-        "unexplained_label_changes": unexplained_changes,
-        "first_label_ms_p50": round(statistics.median(label_latencies), 0) if label_latencies else None,
-        "final_summary": last_summary,
-    }
-    if args.tracks:
-        with open(args.tracks, "w") as handle:
-            json.dump({
-                "windows": [list(w) for w in source.windows] or None,
-                "source": args.source,
-                "pace": args.pace,
-                "seconds": round(elapsed, 1),
-                "tracks": sorted(track_records.values(), key=lambda row: row["track_id"]),
-            }, handle, indent=2)
-    if args.json:
-        print(json.dumps(summary, indent=2))
-    else:
-        print("\n--- stream summary ---")
-        for key, value in summary.items():
-            if key != "final_summary":
-                print(f"{key:24s} {value}")
-        print(f"{'final_summary':24s} {json.dumps(last_summary, ensure_ascii=False)}")
-    if results == 0:
-        sys.exit(1)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/recognization/code/suft.py b/recognization/code/suft.py
new file mode 100644
index 0000000..7e78d6d
--- /dev/null
+++ b/recognization/code/suft.py
@@ -0,0 +1,84 @@
+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+
+def suft_function(image):
+    image1 = cv2.imread(image)
+    # Convert the training image to RGB
+    training_image = cv2.cvtColor(image1, cv2.COLOR_BGR2RGB)
+
+    # Convert the training image to gray scale
+    training_gray = cv2.cvtColor(training_image, cv2.COLOR_RGB2GRAY)
+
+    # Create test image by adding Scale Invariance and Rotational Invariance
+    test_image = cv2.pyrDown(training_image)
+    test_image = cv2.pyrDown(test_image)
+    num_rows, num_cols = test_image.shape[:2]
+
+    rotation_matrix = cv2.getRotationMatrix2D((num_cols/2, num_rows/2), 30, 1)
+    test_image = cv2.warpAffine(test_image, rotation_matrix, (num_cols, num_rows))
+
+    test_gray = cv2.cvtColor(test_image, cv2.COLOR_RGB2GRAY)
+
+    # Display traning image and testing image
+    fx, plots = plt.subplots(1, 2, figsize=(20,10))
+
+    plots[0].set_title("Training Image")
+    plots[0].imshow(training_image)
+
+    plots[1].set_title("Testing Image")
+    plots[1].imshow(test_image)
+
+
+    # ## Detect keypoints and Create Descriptor
+
+    surf = cv2.xfeatures2d.SURF_create(800)
+
+    train_keypoints, train_descriptor = surf.detectAndCompute(training_gray, None)
+    test_keypoints, test_descriptor = surf.detectAndCompute(test_gray, None)
+
+    keypoints_without_size = np.copy(training_image)
+    keypoints_with_size = np.copy(training_image)
+
+    cv2.drawKeypoints(training_image, train_keypoints, keypoints_without_size, color = (0, 255, 0))
+
+    cv2.drawKeypoints(training_image, train_keypoints, keypoints_with_size, flags = cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
+
+    # Display image with and without keypoints size
+    fx, plots = plt.subplots(1, 2, figsize=(20,10))
+
+    plots[0].set_title("Train keypoints With Size")
+    plots[0].imshow(keypoints_with_size, cmap='gray')
+
+    plots[1].set_title("Train keypoints Without Size")
+    plots[1].imshow(keypoints_without_size, cmap='gray')
+
+    # Print the number of keypoints detected in the training image
+    print("Number of Keypoints Detected In The Training Image: ", len(train_keypoints))
+
+    # Print the number of keypoints detected in the query image
+    print("Number of Keypoints Detected In The Query Image: ", len(test_keypoints))
+
+
+    # ## Matching Keypoints
+
+    # Create a Brute Force Matcher object.
+    bf = cv2.BFMatcher(cv2.NORM_L1, crossCheck = False)
+
+    # Perform the matching between the SURF descriptors of the training image and the test image
+    matches = bf.match(train_descriptor, test_descriptor)
+
+    # The matches with shorter distance are the ones we want.
+    matches = sorted(matches, key = lambda x : x.distance)
+
+    result = cv2.drawMatches(training_image, train_keypoints, test_gray, test_keypoints, matches, test_gray, flags = 2)
+
+    # Display the best matching points
+    plt.rcParams['figure.figsize'] = [14.0, 7.0]
+    plt.title('Best Matching Points')
+    plt.imshow(result)
+    plt.show()
+
+    # Print total number of matching points between the training and query images
+    print("\nNumber of Matching Keypoints Between The Training and Query Images: ", len(matches))
+    return keypoints_with_size
\ No newline at end of file
diff --git a/recognization/code/sweep_accept_lines.py b/recognization/code/sweep_accept_lines.py
deleted file mode 100644
index 1af7cb1..0000000
--- a/recognization/code/sweep_accept_lines.py
+++ /dev/null
@@ -1,225 +0,0 @@
-"""Joint sweep of the two open-set gates: LOCAL_ACCEPT x TEXT_MIN_SHARED.
-
-Both lines were fitted on image6, where every crop is a facing of a product the
-shelf actually holds. That set can only measure false accepts on the products it
-happens to contain, so it cannot price a line on its own - the far-unknown rate
-comes from `data/external-shelves/*`: Wikimedia Commons shelf photos that share
-nothing with the catalog, where every accept is a false accept by construction.
-
-Each cell is produced by calling the shipped `DecisionPolicy` with that cell's
-values and the catalog signatures, never by re-implementing the gate in the
-sweep: a sweep that scores its own copy of the policy reports on a machine the
-server does not run. `TEXT_MIN_SHARED` is a floor on the evidence behind the
-text lane's *answer*. It is deliberately not a floor on the score - zeroing a
-row reorders the ranking, so the margin gate would compare a different pair -
-and deliberately not a floor on the veto, which only ever refuses. The constant
-in `identity_lanes.py` says the same thing next to the code that does it.
-
-The distractor side is measured the way the phone measures it - a 1280-long-side
-frame at `LiveSettings.conf`, the path `code/eval_distractors.py` runs. The
-shelf is scored at both resolutions: the full-resolution column is the one the
-published table quotes, the live column is what the phone sees.
-
-RootSIFT and OCR dominate the runtime and neither depends on the gates, so the
-per-crop evidence is cached. Without `--cache` the sweep rebuilds it every run
-and prints the same numbers, only slower.
-
-    .venv/bin/python code/sweep_accept_lines.py \\
-        --cache data/evaluations/2026-09-29-session/scratch/accept_line_cache.pkl
-"""
-
-from __future__ import annotations
-
-import argparse
-import glob
-import os
-import pickle
-import sys
-from typing import Any, Dict, List, Optional, Sequence, Tuple
-
-current_dir = os.path.dirname(os.path.abspath(__file__))
-if current_dir not in sys.path:
-    sys.path.insert(0, current_dir)
-
-import cv2  # noqa: E402
-
-from eval_identity import (  # noqa: E402
-    EXCLUDE,
-    LIVE_LONG_SIDE,
-    UNKNOWN,
-    LocalLaneAdapter,
-    at_live_resolution,
-    crop_of,
-    load_labels,
-)
-from identity_lanes import LOCAL_ACCEPT, TEXT_MIN_SHARED, DecisionPolicy  # noqa: E402
-
-SHELF_LABELS = "eval/labels/image6.json"
-SHELF_IMAGE = "add/image6.jpg"
-DISTRACTOR_DIRS = (
-    "data/external-shelves/wikimedia-commons",
-    "data/external-shelves/wikimedia-commons-2",
-)
-LOCAL_ACCEPTS = (2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0)
-TEXT_MIN_SHAREDS = (2, 3, 4, 5, 6)
-CACHE_VERSION = 3
-
-# One crop's evidence: the local lane's ranking and the text lane's raw read.
-Evidence = Tuple[Any, str]
-# One facing: its ground-truth label plus that evidence.
-Facing = Tuple[str, Any, str]
-
-
-def _evidence(lane: LocalLaneAdapter, frame: Any, box: Sequence[int]) -> Optional[Evidence]:
-    """Rank and read one crop, or None when the box is off the frame."""
-    crop = crop_of(frame, box)
-    if crop.size == 0:
-        return None
-    return lane.rank(crop), lane.text_lane.read(crop)
-
-
-def build_cache(stock_dir: str) -> Dict[str, Any]:
-    """Every crop's evidence, gathered once and swept many times."""
-    from engine import EngineSettings, build_default_engine
-    from live import LiveSettings
-
-    lane = LocalLaneAdapter(stock_dir)
-    image = cv2.imread(SHELF_IMAGE)
-    live = at_live_resolution(image)
-    factor = live.shape[1] / float(image.shape[1])
-
-    shelf_full: List[Facing] = []
-    shelf_live: List[Facing] = []
-    for facing in load_labels(SHELF_LABELS)["facings"]:
-        label = facing.get("label")
-        if label in (None, EXCLUDE):
-            continue
-        full = _evidence(lane, image, facing["box"])
-        if full is None:
-            continue
-        shelf_full.append((label, full[0], full[1]))
-        # A facing the live frame cuts to nothing is dropped rather than
-        # scored at full resolution: the two columns have to be the same crops
-        # at two sizes, or the resolution is not the only variable.
-        small = _evidence(lane, live, [int(round(v * factor)) for v in facing["box"]])
-        if small is not None:
-            shelf_live.append((label, small[0], small[1]))
-
-    engine = build_default_engine(EngineSettings(stock_dir=stock_dir))
-    conf = LiveSettings().conf
-    distractors: List[Evidence] = []
-    photos = 0
-    for directory in DISTRACTOR_DIRS:
-        paths = sorted(
-            path for path in glob.glob(os.path.join(directory, "*"))
-            if os.path.splitext(path)[1].lower() in (".jpg", ".jpeg", ".png")
-        )
-        for path in paths:
-            shelf_photo = cv2.imread(path)
-            if shelf_photo is None:
-                continue
-            photos += 1
-            frame = at_live_resolution(shelf_photo)
-            for detection in engine.detect(frame, conf=conf):
-                box = detection.get("box") or detection.get("bbox")
-                if not box:
-                    continue
-                item = _evidence(lane, frame, [int(v) for v in box])
-                if item is not None:
-                    distractors.append(item)
-    return {
-        "version": CACHE_VERSION,
-        "conf": conf,
-        "photos": photos,
-        "shelf_full": shelf_full,
-        "shelf_live": shelf_live,
-        "distractors": distractors,
-    }
-
-
-def _score(lane: LocalLaneAdapter, policy: DecisionPolicy, facings: Sequence[Facing]) -> Tuple[int, int]:
-    """Shown and correct labels for one shelf column under one policy."""
-    shown = correct = 0
-    for label, ranked, read in facings:
-        text = sorted(
-            lane.text_lane.scores_from_text(read).items(), key=lambda kv: kv[1], reverse=True
-        )
-        answer = policy.decide(ranked, text, text_read=read)["best_sku"]
-        if answer:
-            shown += 1
-            correct += answer == label
-    return shown, correct
-
-
-def _false_accepts(lane: LocalLaneAdapter, policy: DecisionPolicy,
-                   distractors: Sequence[Evidence]) -> int:
-    """Every accept on an unrelated shelf is a false accept by construction."""
-    accepts = 0
-    for ranked, read in distractors:
-        text = sorted(
-            lane.text_lane.scores_from_text(read).items(), key=lambda kv: kv[1], reverse=True
-        )
-        if policy.decide(ranked, text, text_read=read)["best_sku"]:
-            accepts += 1
-    return accepts
-
-
-def main(argv: Optional[List[str]] = None) -> int:
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("--stock", default="stock-lopia-clean")
-    parser.add_argument("--cache", default="", help="build once, reuse on later runs")
-    parser.add_argument("--rebuild", action="store_true", help="ignore an existing cache")
-    args = parser.parse_args(argv)
-
-    cache: Optional[Dict[str, Any]] = None
-    if args.cache and os.path.exists(args.cache) and not args.rebuild:
-        with open(args.cache, "rb") as handle:
-            cache = pickle.load(handle)
-        if cache.get("version") != CACHE_VERSION:
-            cache = None
-    if cache is None:
-        cache = build_cache(args.stock)
-        if args.cache:
-            os.makedirs(os.path.dirname(os.path.abspath(args.cache)), exist_ok=True)
-            with open(args.cache, "wb") as handle:
-                pickle.dump(cache, handle)
-
-    lane = LocalLaneAdapter(args.stock)
-    shelf_full: List[Facing] = cache["shelf_full"]
-    shelf_live: List[Facing] = cache["shelf_live"]
-    distractors: List[Evidence] = cache["distractors"]
-    known = sum(1 for label, _, _ in shelf_full if label != UNKNOWN)
-
-    print(f"catalog {args.stock}, {LIVE_LONG_SIDE}-long-side frames at detect_conf {cache['conf']}")
-    print(f"image6: {len(shelf_full)} facings, {known} holding a catalog row, "
-          f"{len(shelf_live)} of them cut from the live frame")
-    print(f"distractors: {len(distractors)} crops from {cache['photos']} unrelated shelf photos")
-    print()
-    print(f"{'accept':>6} {'floor':>5} | {'shown':>5} {'corr':>4} {'prec':>6} {'cover':>6} "
-          f"| {'shown':>5} {'corr':>4} {'prec':>6} | {'false':>5} {'rate':>6}")
-    print(f"{'':>6} {'':>5} | {'--- full resolution ---':^25} "
-          f"| {'---- live resolution ----':^25} | {'-- unknown shelves --':^14}")
-    for accept in LOCAL_ACCEPTS:
-        for floor in TEXT_MIN_SHAREDS:
-            policy = DecisionPolicy(
-                local_accept=accept, text_min_shared=floor,
-                signatures=lane.text_lane.signatures,
-            )
-            full_shown, full_correct = _score(lane, policy, shelf_full)
-            live_shown, live_correct = _score(lane, policy, shelf_live)
-            false = _false_accepts(lane, policy, distractors)
-            shown, correct = full_shown, full_correct
-            rate = false / len(distractors) * 100
-            shipped = "  <- shipped" if (accept, floor) == (LOCAL_ACCEPT, TEXT_MIN_SHARED) else ""
-            print(
-                f"{accept:>6.1f} {floor:>5} | {shown:>5} {correct:>4} "
-                f"{correct / shown * 100 if shown else 0:>5.1f}% {correct / known * 100:>5.1f}% "
-                f"| {live_shown:>5} {live_correct:>4} "
-                f"{live_correct / live_shown * 100 if live_shown else 0:>5.1f}% "
-                f"| {false:>5} {rate:>5.2f}%{shipped}"
-            )
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/recognization/code/tag_cards.py b/recognization/code/tag_cards.py
deleted file mode 100644
index 20f55c2..0000000
--- a/recognization/code/tag_cards.py
+++ /dev/null
@@ -1,860 +0,0 @@
-"""
-Price tags as cards with fields, not rail bands.
-
-The old tag pass OCR'd a whole shelf rail and then tried to split the text into
-tags. Two things went wrong, both measured:
-
-* the reported box was the *band*, so a tag box looked like a strip spanning four
-  or five tags and lined up with nothing;
-* a band mixes several tags' text, so values were assigned to the wrong tag
-  (measured: a tag reading 649/754.9 came back as price 2630, name
-  "コプサラダトレッシング" - text from a different tag).
-
-This module takes the opposite route: the OCR engine's own line boxes define the
-cards. Lines are grouped into a card by horizontal proximity, the card's box is
-the union of its lines (so it lines up with the printed tag by construction), and
-the fields are read with the tag's layout as a prior.
-
-The layout of a Japanese shelf-edge tag, top to bottom: a 毎日お買得-style header,
-the product name, the JAN's last three digits beside a discount, the sale price in
-large digits, and a small tax-included or unit price. Measured on a LOPIA tag:
-
-    269 x 1.08 = 290.52     379 x 1.08 = 409.32     199 x 1.08 = 214.92
-
-so the small clean number and the big stylised one are linked by the consumption
-tax. That relation is used as a *check*: a price is only trusted when the two
-numbers agree, and when the big digits cannot be read at all the price is derived
-from the small one and marked as derived rather than measured.
-"""
-
-from __future__ import annotations
-
-import json
-import math
-import os
-import re
-from dataclasses import dataclass, field
-from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple
-
-# Defaults when a store has no store.json. Measured on the demo LOPIA tags:
-# 8% consumption tax, brick-red rails, and shelf prices from ¥50. A store file
-# replaces these for that store only.
-DEFAULT_TAX_RATIOS = (1.08, 1.10)
-DEFAULT_MIN_PRICE = 50
-# OpenCV hue is 0-180. Brick red wraps past 0, so a pixel is rail-coloured when
-# its hue is below hue_low or above hue_high.
-DEFAULT_RAIL_HSV = {"hue_low": 14, "hue_high": 166, "sat_min": 70, "val_min": 70}
-# Aliases the parser reads. load_store_policy() republishes them per store.
-TAX_RATIOS = DEFAULT_TAX_RATIOS
-MIN_PRICE = DEFAULT_MIN_PRICE
-
-_RAIL: Optional["StorePolicy"] = None
-
-
-def _remember_rail(policy: "StorePolicy") -> None:
-    global _RAIL
-    _RAIL = policy
-
-
-
-class StorePolicyError(ValueError):
-    """store.json exists but cannot be used. Never borrow another store's file."""
-
-
-@dataclass(frozen=True)
-class StorePolicy:
-    tax_ratios: Tuple[float, ...] = DEFAULT_TAX_RATIOS
-    min_price: int = DEFAULT_MIN_PRICE
-    rail_hue_low: int = 14
-    rail_hue_high: int = 166
-    rail_sat_min: int = 70
-    rail_val_min: int = 70
-    store_id: Optional[str] = None
-
-    def active(self) -> None:
-        global TAX_RATIOS, MIN_PRICE
-        TAX_RATIOS = self.tax_ratios
-        MIN_PRICE = self.min_price
-        _remember_rail(self)
-
-def default_policy() -> StorePolicy:
-    policy = StorePolicy()
-    policy.active()
-    return policy
-
-
-def _as_ratio(value: Any) -> float:
-    number = float(value)
-    if not 1.0 < number <= 1.5:
-        raise StorePolicyError(f"tax ratio {value!r} is outside 1-1.5")
-    return number
-
-
-def _as_bound(value: Any, name: str, low: int, high: int) -> int:
-    number = int(value)
-    if not low <= number <= high:
-        raise StorePolicyError(f"{name} {value!r} is outside {low}-{high}")
-    return number
-
-
-def load_store_policy(stock_dir: Optional[str]) -> StorePolicy:
-    """Read `<stock_dir>/store.json`. A missing file uses the defaults.
-
-    A file that is present but invalid raises StorePolicyError naming that path.
-    """
-    if not stock_dir:
-        return StorePolicy()
-    path = os.path.join(stock_dir, "store.json")
-    if not os.path.isfile(path):
-        return StorePolicy()
-    try:
-        with open(path, encoding="utf-8") as handle:
-            raw = json.load(handle)
-    except (OSError, json.JSONDecodeError) as exc:
-        raise StorePolicyError(f"{path}: {exc}") from exc
-    if not isinstance(raw, dict):
-        raise StorePolicyError(f"{path}: expected an object")
-    try:
-        ratios = raw.get("tax_ratios", list(DEFAULT_TAX_RATIOS))
-        if not isinstance(ratios, list) or not ratios:
-            raise StorePolicyError(f"{path}: tax_ratios must be a non-empty list")
-        rail = raw.get("rail_hsv", DEFAULT_RAIL_HSV)
-        if not isinstance(rail, dict):
-            raise StorePolicyError(f"{path}: rail_hsv must be an object")
-        policy = StorePolicy(
-            tax_ratios=tuple(_as_ratio(item) for item in ratios),
-            min_price=_as_bound(raw.get("min_price", DEFAULT_MIN_PRICE), "min_price", 1, 100000),
-            rail_hue_low=_as_bound(rail.get("hue_low", 14), "hue_low", 0, 180),
-            rail_hue_high=_as_bound(rail.get("hue_high", 166), "hue_high", 0, 180),
-            rail_sat_min=_as_bound(rail.get("sat_min", 70), "sat_min", 0, 255),
-            rail_val_min=_as_bound(rail.get("val_min", 70), "val_min", 0, 255),
-            store_id=str(raw["store_id"]) if raw.get("store_id") else None,
-        )
-    except (TypeError, ValueError) as exc:
-        if isinstance(exc, StorePolicyError):
-            raise
-        raise StorePolicyError(f"{path}: {exc}") from exc
-    policy.active()
-    return policy
-
-HEADER_PATTERN = re.compile(r"お買得|お買い得|特価|価格|セール")
-
-# The tag truncates its discount to a tenth of a 割, so agreement is one step.
-DISCOUNT_TOLERANCE = 0.05
-# A regular price above this multiple of the sale price is a misread, not a discount.
-REGULAR_PRICE_CEILING = 1.8
-DISCOUNT_PATTERN = re.compile(r"(\d+(?:\.\d)?)\s*割\s*引")
-# A decimal whose point the OCR dropped or replaced with a space (measured: a
-# tax-included 409.32 came back as "409 3").
-LOOSE_DECIMAL_PATTERN = re.compile(r"(\d{2,4})[.,\s](\d{1,2})(?!\d)")
-DECIMAL_PATTERN = re.compile(r"(\d{1,4})[.,](\d{2})(?!\d)")
-INTEGER_PATTERN = re.compile(r"(?<!\d)(\d{2,4})(?!\d)")
-JAN3_PATTERN = re.compile(r"(?<!\d)(\d{3})(?!\d)")
-# Package sizes and other numbers that are not prices or JANs.
-SIZE_PATTERN = re.compile(r"\d+\s*(?:g|kg|ml|mL|ℓ|L|個|枚|袋|本|入)", re.IGNORECASE)
-NOISE_NUMBERS = {"108", "110"}
-
-
-@dataclass
-class OcrLine:
-    """One OCR line: its text and its box in the image the OCR ran on."""
-
-    text: str
-    box: Tuple[int, int, int, int]
-    confidence: float = 0.0
-
-    @property
-    def width(self) -> int:
-        return self.box[2] - self.box[0]
-
-    @property
-    def height(self) -> int:
-        return self.box[3] - self.box[1]
-
-
-@dataclass
-class TagFields:
-    """What a tag says, with the evidence for each value.
-
-    Field names match the rest of the tag pipeline (`tags.TagFields`) so a card's
-    reading can be folded into the session cache unchanged; `price_source` and the
-    corroboration flags are the additions this module brings.
-
-    `regular_price` is the number a LOPIA tag prints large beside the sale price
-    (357 beside 269). It is the price *before* the discount, not a product code:
-    checked against the manufacturer, this product's JAN is 4901577092381, whose
-    last three digits are 381. All three tags on the demo shelf agree with their
-    printed discount instead - (357-269)/357 = 24.6 % -> 2.4割引 - which makes the
-    discount a second arithmetic check alongside the tax-included price.
-    """
-
-    price: Optional[int] = None
-    regular_price: Optional[int] = None
-    discount: Optional[float] = None
-    unit_price: Optional[float] = None
-    name: Optional[str] = None
-    # A full JAN read from the tag's barcode. Only decodable at close range
-    # (measured: 0.63 px per module at ~1 m needs ~3x more pixels), so it is
-    # normally None and the barcode stays a deliberate close-up step.
-    barcode: Optional[str] = None
-    # measured (the big digits agreed with the tax relation) | derived (taken from
-    # the tax relation because the big digits could not be read) | none
-    price_source: str = "none"
-    header: Optional[str] = None
-    confidence: float = 0.0
-    flags: List[str] = field(default_factory=list)
-
-    def corroborated(self) -> bool:
-        """Did the tag's own arithmetic agree anywhere?
-
-        Measured on a live walk: single uncorroborated reads of the big stylised
-        digits produced prices of 20, 23, 72, 2002 and 4508. A price is only worth
-        publishing when the tag's arithmetic confirms it.
-
-        Three strengths, weakest to strongest: the tax-included price and the sale
-        price form an exact pair (`tax_pair_consistent`); the printed discount
-        agrees with the pair (`discount_consistent`); the stylised digits agree with
-        the pair as well (`tax_consistent`). The compliance rules demand one of the
-        two stronger ones before a tag may accuse anything.
-        """
-        return bool(
-            {"tax_consistent", "tax_pair_consistent", "discount_consistent"} & set(self.flags)
-        )
-
-    def is_readable(self) -> bool:
-        """Readable = an anchor, a price, and arithmetic that agrees."""
-        if self.price is None or self.price < MIN_PRICE:
-            return False
-        if not self.corroborated():
-            return False
-        return self.regular_price is not None or self.name is not None
-
-    def as_dict(self) -> Dict[str, Any]:
-        return {
-            "price": self.price,
-            "regular_price": self.regular_price,
-            "discount": self.discount,
-            "unit_price": self.unit_price,
-            "name": self.name,
-            "barcode": self.barcode,
-            "price_source": self.price_source,
-            "flags": list(self.flags),
-        }
-
-    def as_payload(self) -> Dict[str, Any]:
-        payload = self.as_dict()
-        payload["confidence"] = round(self.confidence, 3)
-        return payload
-
-
-@dataclass
-class TagCard:
-    """A price tag: its box in the frame, and what it says."""
-
-    box: Tuple[int, int, int, int]
-    fields: TagFields
-    lines: List[OcrLine] = field(default_factory=list)
-
-    @property
-    def centre_x(self) -> float:
-        return (self.box[0] + self.box[2]) / 2.0
-
-    def as_payload(self) -> Dict[str, Any]:
-        payload = {"box": list(self.box), "state": "readable" if self.fields.is_readable() else "unreadable"}
-        payload.update(self.fields.as_payload())
-        return payload
-
-
-# --------------------------------------------------------------------------- grouping
-
-
-def cluster_rows(lines: Sequence[OcrLine], max_gap: int = 26) -> List[List[OcrLine]]:
-    """Split OCR lines into horizontal rows.
-
-    A rail band's crop also catches the packaging text of the rows above and
-    below, which is what made an earlier grouping mix a product's text into a
-    tag. Rows are separated first, so a tag row can be chosen on its own.
-
-    The separator is a vertical *gap*, not strict overlap: a tag stacks small
-    lines (header, name, JAN, price) with space between them, so requiring
-    overlap split one tag across several rows. Shelf rows sit hundreds of pixels
-    apart, so a small gap threshold still separates them.
-    """
-    if not lines:
-        return []
-    rows: List[List[OcrLine]] = []
-    for line in sorted(lines, key=lambda l: l.box[1]):
-        placed = False
-        for row in rows:
-            top = min(l.box[1] for l in row)
-            bottom = max(l.box[3] for l in row)
-            gap = max(0, max(top - line.box[3], line.box[1] - bottom))
-            if gap <= max_gap:
-                row.append(line)
-                placed = True
-                break
-        if not placed:
-            rows.append([line])
-    return rows
-
-
-def tag_row_score(row: Sequence[OcrLine], header_pattern: Any = None) -> float:
-    """How much a row looks like a line of price tags.
-
-    Tags carry a header, a discount, and a decimal (tax-included) price; product
-    packaging rarely carries all three on one line. The header pattern is bound
-    here: a missing module global must not fail the tag pass.
-    """
-    def pattern(name: str, fallback: str) -> Any:
-        found = globals().get(name)
-        return found if found is not None else re.compile(fallback)
-
-    header = header_pattern if header_pattern is not None else pattern(
-        "HEADER_PATTERN", r"お買得|お買い得|特価|価格|セール"
-    )
-    discount = pattern("DISCOUNT_PATTERN", r"(\d+(?:\.\d)?)\s*割\s*引")
-    decimal = pattern("DECIMAL_PATTERN", r"(\d{1,4})[.,](\d{2})(?!\d)")
-    jan = pattern("JAN3_PATTERN", r"(?<!\d)(\d{3})(?!\d)")
-    score = 0.0
-    for line in row:
-        text = line.text
-        if header.search(text):
-            score += 2.0
-        if discount.search(text):
-            score += 1.5
-        if decimal.search(text):
-            score += 1.5
-        if jan.search(text) and not decimal.search(text):
-            score += 0.5
-    return score
-
-
-def best_tag_rows(lines: Sequence[OcrLine], keep: int = 2) -> List[List[OcrLine]]:
-    rows = cluster_rows(lines)
-    scored = sorted(rows, key=tag_row_score, reverse=True)
-    return [row for row in scored[:keep] if tag_row_score(row) > 0]
-
-
-def group_lines_into_cards(
-    lines: Sequence[OcrLine],
-    max_gap: int = 60,
-    max_card_width: int = 420,
-) -> List[List[OcrLine]]:
-    """Group OCR lines into cards by horizontal proximity.
-
-    Tags sit side by side with a visible gap between their text blocks, and the
-    decorative part of the card carries no text, so a horizontal gap is a
-    reliable separator. Lines are first bucketed into rough columns, then split
-    where the gap is too wide or the column is wider than a tag.
-    """
-    if not lines:
-        return []
-    ordered = sorted(lines, key=lambda line: (line.box[0], line.box[1]))
-    groups: List[List[OcrLine]] = []
-    current: List[OcrLine] = []
-    current_span: Optional[Tuple[int, int]] = None
-    for line in ordered:
-        start, end = line.box[0], line.box[2]
-        if not current:
-            current, current_span = [line], (start, end)
-            continue
-        assert current_span is not None
-        overlaps = start <= current_span[1] + max_gap
-        wide_enough = max(current_span[1], end) - min(current_span[0], start) <= max_card_width
-        if overlaps and wide_enough:
-            current.append(line)
-            current_span = (min(current_span[0], start), max(current_span[1], end))
-        else:
-            groups.append(current)
-            current, current_span = [line], (start, end)
-    if current:
-        groups.append(current)
-    return groups
-
-
-def card_box(lines: Iterable[OcrLine], pad_x: int = 6, pad_y: int = 4) -> Tuple[int, int, int, int]:
-    """The card's box: the union of its lines, padded to the printed edge."""
-    xs1 = [line.box[0] for line in lines]
-    ys1 = [line.box[1] for line in lines]
-    xs2 = [line.box[2] for line in lines]
-    ys2 = [line.box[3] for line in lines]
-    return (max(0, min(xs1) - pad_x), max(0, min(ys1) - pad_y), max(xs2) + pad_x, max(ys2) + pad_y)
-
-
-# --------------------------------------------------------------------------- parsing
-
-
-def _to_float(whole: str, fraction: str) -> float:
-    return float(f"{whole}.{fraction}")
-
-
-def _line_height(line: Any) -> int:
-    """Line height, whichever line type the caller passes in.
-
-    `tag_cards` has its own line type, but the tag pipeline hands over the OCR
-    engine's lines, which carry only a box.
-    """
-    height = getattr(line, "height", None)
-    if height is not None:
-        return int(height)
-    return int(line.box[3] - line.box[1])
-
-
-def _vote(candidates: Sequence[Tuple[Any, float]]) -> Optional[Any]:
-    """Pick a value by agreement across reads, not by one read's optimism.
-
-    Several passes over the same card produce several reads of the same field.
-    OCR confidence alone is a poor guide (measured: a wrong 12.4割引 came back
-    more confidently than the right 2.4割引), so agreement wins, and confidence
-    only breaks ties.
-    """
-    if not candidates:
-        return None
-    scores: Dict[Any, List[float]] = {}
-    for value, confidence in candidates:
-        scores.setdefault(value, []).append(confidence)
-    return max(scores.items(), key=lambda item: (len(item[1]), sum(item[1]) / len(item[1])))[0]
-
-
-# Every flag that describes the published field set. They are recomputed whenever
-# the fields change (after voting), so a stale one - "no_unit_price" surviving a
-# frame that did read the unit price - cannot linger.
-CORROBORATION_FLAGS = {
-    "tax_consistent",
-    "tax_pair_consistent",
-    "digit_read_disagrees",
-    "price_from_unit",
-    "discount_consistent",
-    "discount_derived",
-    "discount_mismatch",
-    "regular_price_implausible",
-    "price_implausible",
-    "no_price",
-    "no_unit_price",
-    "no_regular_price",
-}
-
-
-def corroborate(fields: TagFields) -> List[str]:
-    """Run the tag's arithmetic on a field set and return the flags it earns.
-
-    Used twice: while parsing one read, and again after the session has voted
-    field by field. Voting picks each field's value independently, so the winner
-    can be a price from one frame and a tax-included price from another - measured:
-    a record published price 149 beside unit price 214.92, a pair that never
-    appeared together (214.92 / 1.08 = 199). Re-running the arithmetic on the
-    values actually being published is what keeps the claim coherent.
-    """
-    flags: List[str] = []
-    price = fields.price
-    unit = fields.unit_price
-
-    if unit:
-        derived = None
-        for ratio in TAX_RATIOS:
-            candidate = unit / ratio
-            rounded = round(candidate)
-            if abs(candidate - rounded) <= 0.02 and MIN_PRICE <= rounded <= 100000:
-                derived = float(rounded)
-                break
-        if derived is not None:
-            if price is None:
-                fields.price = int(round(derived))
-                fields.price_source = "derived"
-                flags.append("price_from_unit")
-            elif abs(price - derived) <= max(2.0, 0.01 * derived):
-                fields.price = int(round(derived))
-                flags.append("tax_consistent")
-            else:
-                # The pair is exact but the digits disagree; the pair wins.
-                fields.price = int(round(derived))
-                fields.price_source = "derived"
-                flags.extend(["tax_pair_consistent", "digit_read_disagrees"])
-
-    if price is None:
-        flags.append("no_price")
-    elif price < MIN_PRICE:
-        flags.append("price_implausible")
-    if fields.unit_price is None:
-        flags.append("no_unit_price")
-
-    if fields.regular_price and fields.price:
-        plausible = 1.02 * fields.price <= fields.regular_price <= REGULAR_PRICE_CEILING * fields.price
-        if not plausible:
-            flags.append("regular_price_implausible")
-            fields.regular_price = None
-        else:
-            ratio = (fields.regular_price - fields.price) / float(fields.regular_price)
-            expected = math.floor(ratio * 100.0) / 10.0
-            if fields.discount is None:
-                fields.discount = expected
-                flags.append("discount_derived")
-            elif abs(fields.discount - expected) <= DISCOUNT_TOLERANCE:
-                flags.append("discount_consistent")
-            else:
-                # The printed discount disagrees with the two prices. The prices
-                # corroborate each other, so publish the discount they imply and
-                # keep the disagreement on the record.
-                fields.discount = expected
-                flags.append("discount_mismatch")
-    if fields.regular_price is None:
-        flags.append("no_regular_price")
-    return flags
-
-
-def parse_tag_fields(lines: Sequence[OcrLine]) -> TagFields:
-    """Read a tag's fields from its OCR lines, using the tag layout as a prior."""
-    fields = TagFields()
-    if not lines:
-        return fields
-    ordered = sorted(lines, key=lambda line: (line.box[1], line.box[0]))
-    texts = [(line.text.strip(), line) for line in ordered if line.text.strip()]
-    if not texts:
-        return fields
-
-    header = next((text for text, _ in texts if HEADER_PATTERN.search(text)), None)
-    if header:
-        fields.header = header
-
-    # Discounts: vote across reads, and keep only a plausible rate (a 12.4割引
-    # came from a spurious leading 1 in one pass).
-    discount_candidates: List[Tuple[float, float]] = []
-    for text, line in texts:
-        match = DISCOUNT_PATTERN.search(text)
-        if match:
-            value = float(match.group(1))
-            if 0.1 <= value <= 9.9:
-                discount_candidates.append((value, line.confidence))
-    fields.discount = _vote(discount_candidates)
-
-    decimal_candidates: List[Tuple[float, float]] = []   # (value, line confidence)
-    for text, line in texts:
-        strict = DECIMAL_PATTERN.findall(text)
-        for whole, fraction in strict:
-            value = _to_float(whole, fraction)
-            if 10.0 <= value <= 100000.0:
-                decimal_candidates.append((value, line.confidence))
-        if not strict and not SIZE_PATTERN.search(text):
-            for whole, fraction in LOOSE_DECIMAL_PATTERN.findall(text):
-                value = _to_float(whole, fraction if len(fraction) == 2 else fraction + "0")
-                if 10.0 <= value <= 100000.0:
-                    decimal_candidates.append((value, line.confidence * 0.8))
-    fields.unit_price = _vote(decimal_candidates)
-
-    # The name: the longest line that is not a number, a header, or a price.
-    def looks_like_name(text: str) -> bool:
-        if HEADER_PATTERN.search(text) or DISCOUNT_PATTERN.search(text):
-            return False
-        if DECIMAL_PATTERN.search(text):
-            return False
-        letters = sum(1 for char in text if not char.isdigit() and char not in " .,()（）<>＜＞%¥円")
-        return letters >= 4
-
-    name_candidates = [(text, line) for text, line in texts if looks_like_name(text)]
-    if name_candidates:
-        # The longest read is the most complete one; ties go to the more confident.
-        name_candidates.sort(key=lambda item: (-len(item[0]), -item[1].confidence))
-        fields.name = name_candidates[0][0]
-
-    # LARGEST text on the tag (its line is roughly twice as tall as the small
-    # print), and the JAN's last three digits sit on a small line of their own.
-    # Getting this order wrong made a 269 price look like a JAN and disappear.
-    integer_lines: List[Tuple[Any, List[str]]] = []
-    for text, line in texts:
-        if DECIMAL_PATTERN.search(text) or SIZE_PATTERN.search(text):
-            continue
-        numbers = [n for n in INTEGER_PATTERN.findall(text) if n not in NOISE_NUMBERS]
-        if numbers:
-            integer_lines.append((line, numbers))
-
-    price_line = None
-    measured = None
-    if integer_lines:
-        price_line, numbers = max(integer_lines, key=lambda item: _line_height(item[0]))
-        tall = max(_line_height(line) for line, _ in integer_lines)
-        # Only accept a price from a line that is actually the big print.
-        if _line_height(price_line) >= 0.75 * tall:
-            measured = float(max(numbers, key=int))
-
-    # The regular price: a small-line number that is *higher* than the sale price
-    # (it is the price before the discount), with package sizes excluded.
-    regular_candidates: List[Tuple[int, float]] = []
-    for line, numbers in integer_lines:
-        if line is price_line:
-            continue
-        for candidate in numbers:
-            value = int(candidate)
-            if value < MIN_PRICE:
-                continue
-            if measured is not None and value <= measured:
-                continue
-            regular_candidates.append((value, line.confidence))
-    if regular_candidates:
-        regular_candidates.sort(key=lambda item: -item[1])
-        fields.regular_price = regular_candidates[0][0]
-
-    # The price starts as what the big digits said; the arithmetic below may
-    # correct it from the tax-included price, which is the more reliable read.
-    if measured is not None:
-        fields.price = int(round(measured))
-        fields.price_source = "measured"
-
-    # The tag's arithmetic: the tax relation between the sale price and the
-    # tax-included price, and the discount between the regular price and the sale
-    # price. Both are checks the tag carries with it.
-    fields.flags = [flag for flag in fields.flags if flag not in CORROBORATION_FLAGS]
-    fields.flags.extend(corroborate(fields))
-
-    # Confidence: the anchors we actually trust, not the OCR's own optimism.
-    score = 0.0
-    if fields.price is not None:
-        score += 0.5 if fields.price_source == "measured" else 0.35
-    if fields.regular_price is not None:
-        score += 0.15
-    if fields.corroborated():
-        score += 0.2
-    if fields.name is not None:
-        score += 0.15
-    if fields.discount is not None:
-        score += 0.1
-    fields.confidence = min(1.0, score)
-    return fields
-
-
-def card_from_lines(lines: Sequence[OcrLine]) -> TagCard:
-    return TagCard(box=card_box(lines), fields=parse_tag_fields(lines), lines=list(lines))
-
-
-# --------------------------------------------------------------------------- finding unreadable tags
-
-
-def find_rails(image: Any, min_fraction: float = 0.25) -> List[Tuple[int, int, int, int]]:
-    """Find the shelf's tag rails.
-
-    In the stores in these images the price tags are printed *on* the rail: a
-    red brick-patterned strip that runs the width of the shelf, one tag per
-    product column. The rail is therefore the place to look for tags, and its
-    brick red is what makes it findable without reading anything.
-    """
-    import cv2
-    import numpy as np
-
-    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
-    hue, sat, val = cv2.split(hsv)
-    rail = _RAIL or StorePolicy()
-    brick = (
-        ((hue < rail.rail_hue_low) | (hue > rail.rail_hue_high))
-        & (sat > rail.rail_sat_min)
-        & (val > rail.rail_val_min)
-    ).astype(np.uint8)
-    row_fraction = brick.mean(axis=1)
-    height, width = image.shape[:2]
-    rails: List[Tuple[int, int, int, int]] = []
-    start: Optional[int] = None
-    for y, fraction in enumerate(row_fraction):
-        if fraction >= min_fraction and start is None:
-            start = y
-        elif fraction < min_fraction and start is not None:
-            if y - start >= 20:
-                rails.append((0, start, width, y))
-            start = None
-    if start is not None and height - start >= 20:
-        rails.append((0, start, width, height))
-    return rails
-
-
-def find_tag_blocks_on_rail(
-    image: Any,
-    rail: Tuple[int, int, int, int],
-    min_height_ratio: float = 0.35,
-) -> List[Tuple[int, int, int, int]]:
-    """Find the tags printed on a rail: bright text blocks against the brick.
-
-    Every facing has a tag, so this is what separates "this product has no tag"
-    from "this tag could not be read" - the distinction the compliance rules need
-    before they accuse a shelf of anything.
-    """
-    import cv2
-    import numpy as np
-
-    x1, y1, x2, y2 = rail
-    strip = image[y1:y2, x1:x2]
-    if strip.size == 0:
-        return []
-    hsv = cv2.cvtColor(strip, cv2.COLOR_BGR2HSV)
-    hue, sat, val = cv2.split(hsv)
-    # The tag's text block is paper: brighter and much less saturated than the
-    # brick behind it. Tag text splits the paper into pieces, so the tag is found
-    # by projecting onto x (a tag is a run of columns that carry paper) rather
-    # than by connected components, which fragment at every printed line.
-    paper = ((val > 120) & (sat < 145)).astype(np.uint8)
-    column_fraction = paper.mean(axis=0)
-    rail_height = y2 - y1
-    blocks: List[Tuple[int, int, int, int]] = []
-    start: Optional[int] = None
-    for index, fraction in enumerate(column_fraction):
-        if fraction >= 0.18 and start is None:
-            start = index
-        elif fraction < 0.18 and start is not None:
-            blocks.append((start, index))
-            start = None
-    if start is not None:
-        blocks.append((start, len(column_fraction)))
-
-    tags: List[Tuple[int, int, int, int]] = []
-    for start_x, end_x in blocks:
-        if end_x - start_x < 40:
-            continue
-        window = paper[:, start_x:end_x]
-        rows = np.where(window.mean(axis=1) >= 0.15)[0]
-        if len(rows) < min_height_ratio * rail_height:
-            continue
-        tags.append((x1 + start_x, y1 + int(rows.min()), x1 + end_x, y1 + int(rows.max())))
-    return tags
-
-
-def learn_header_template(image: Any, cards: Sequence[TagCard]) -> Optional[Any]:
-    """Learn what this store's tag header looks like, from a tag we could read.
-
-    A tag whose text cannot be read still has to be *found*, or "no tag here" and
-    "I could not read this tag" become the same answer and the compliance rules
-    have nothing honest to say. The header is the one part of a tag that repeats
-    identically across the shelf, so a single readable tag teaches the detector
-    what to look for - no per-chain template to maintain.
-    """
-    import cv2
-
-    for card in cards:
-        for line in card.lines:
-            if not HEADER_PATTERN.search(line.text or ""):
-                continue
-            x1, y1, x2, y2 = line.box
-            if x2 - x1 < 40 or y2 - y1 < 8:
-                continue
-            height, width = image.shape[:2]
-            # The header bar is wider than the text on it (measured: a 79 px text
-            # line sits on a ~196 px bar), so the template is padded out to the bar
-            # - a bare text template matches far fewer tags.
-            pad_x = max(6, int((x2 - x1) * 0.75))
-            pad_y = max(2, int((y2 - y1) * 0.35))
-            x1, x2 = max(0, x1 - pad_x), min(width, x2 + pad_x)
-            y1, y2 = max(0, y1 - pad_y), min(height, y2 + pad_y)
-            template = image[y1:y2, x1:x2]
-            if template.size:
-                return template
-    return None
-
-
-def find_tags_by_template(
-    image: Any,
-    template: Any,
-    region: Optional[Tuple[int, int, int, int]] = None,
-    threshold: float = 0.55,
-    scales: Sequence[float] = (0.72, 0.82, 0.92, 1.0, 1.1, 1.2, 1.32),
-) -> List[Tuple[int, int, int, int]]:
-    """Find tag headers across an image (or one region) by matching the learned header.
-
-    Returns tag boxes: the header plus the text block beneath it, which is where
-    the name, the JAN and the price are printed.
-    """
-    import cv2
-    import numpy as np
-
-    if template is None or not getattr(template, "size", 0):
-        return []
-    height, width = image.shape[:2]
-    x1, y1, x2, y2 = region or (0, 0, width, height)
-    x1, y1 = max(0, x1), max(0, y1)
-    x2, y2 = min(width, x2), min(height, y2)
-    crop = image[y1:y2, x1:x2]
-    if crop.size == 0:
-        return []
-    grey = cv2.cvtColor(crop, cv2.COLOR_BGR2GRAY)
-    hits: List[Tuple[float, int, int, int, int]] = []
-    for scale in scales:
-        scaled = template if scale == 1.0 else cv2.resize(
-            template, None, fx=scale, fy=scale, interpolation=cv2.INTER_AREA
-        )
-        if scaled.shape[0] >= grey.shape[0] or scaled.shape[1] >= grey.shape[1]:
-            continue
-        response = cv2.matchTemplate(grey, cv2.cvtColor(scaled, cv2.COLOR_BGR2GRAY), cv2.TM_CCOEFF_NORMED)
-        ys, xs = np.where(response >= threshold)
-        for x, y in zip(xs, ys):
-            hits.append((float(response[y, x]), int(x), int(y), scaled.shape[1], scaled.shape[0]))
-    hits.sort(reverse=True)
-    kept: List[Tuple[float, int, int, int, int]] = []
-    for hit in hits:
-        _, x, y, w, h = hit
-        if all(abs(x - kx) > w * 0.5 or abs(y - ky) > h for _, kx, ky, _, _ in kept):
-            kept.append(hit)
-    boxes = []
-    for _, x, y, w, h in kept:
-        # The tag continues below its header: the text block is about five header
-        # heights tall and a little wider than the header itself.
-        tag_x1 = x1 + x - 4
-        tag_y1 = y1 + y - 4
-        tag_x2 = min(width, tag_x1 + int(w * 1.30))
-        tag_y2 = min(height, tag_y1 + int(h * 5.0))
-        boxes.append((tag_x1, tag_y1, tag_x2, tag_y2))
-    return boxes
-
-
-# --------------------------------------------------------------------------- reading
-
-
-def read_card_lines(
-    engine: Any,
-    image: Any,
-    box: Tuple[int, int, int, int],
-    margin: int = 10,
-    scales: Sequence[float] = (1.0, 2.0, 3.0),
-    variants: Sequence[str] = ("gray", "red"),
-) -> List[OcrLine]:
-    """OCR one card several ways and pool the reads.
-
-    The fields on a tag are printed at very different sizes: the price is huge
-    and stylised, the JAN and the tax-included price are small and clean. A single
-    pass at one scale reads one of them well and the other badly, so the card is
-    read at three scales and in the red channel (where red-on-white text becomes
-    high contrast), and the results are pooled for voting.
-    """
-    import cv2
-
-    height, width = image.shape[:2]
-    x1 = max(0, int(box[0]) - margin)
-    y1 = max(0, int(box[1]) - margin)
-    x2 = min(width, int(box[2]) + margin)
-    y2 = min(height, int(box[3]) + margin)
-    crop = image[y1:y2, x1:x2]
-    if crop.size == 0:
-        return []
-    pooled: List[OcrLine] = []
-    for variant in variants:
-        if variant == "red":
-            prepared = cv2.cvtColor(255 - crop[:, :, 2], cv2.COLOR_GRAY2BGR)
-        elif variant == "green":
-            prepared = cv2.cvtColor(crop[:, :, 1], cv2.COLOR_GRAY2BGR)
-        else:
-            prepared = crop
-        for scale in scales:
-            scaled = prepared if scale == 1.0 else cv2.resize(
-                prepared, None, fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC
-            )
-            result = engine.read(scaled)
-            for line in result.lines:
-                text = (line.text or "").strip()
-                if not text:
-                    continue
-                pooled.append(
-                    OcrLine(
-                        text=text,
-                        box=(
-                            x1 + int(line.box[0] / scale),
-                            y1 + int(line.box[1] / scale),
-                            x1 + int(line.box[2] / scale),
-                            y1 + int(line.box[3] / scale),
-                        ),
-                        confidence=float(line.confidence),
-                    )
-                )
-    return pooled
diff --git a/recognization/code/tags.py b/recognization/code/tags.py
deleted file mode 100644
index 0a97c5f..0000000
--- a/recognization/code/tags.py
+++ /dev/null
@@ -1,1043 +0,0 @@
-"""
-Price-tag geometry, parsing, and the cached tag pass.
-
-Three jobs, all independent of which OCR engine runs:
-
-1. Where the tags can be: rail bands derived from the product rows the detector
-   already produces (the same row clustering the empty-slot detector uses).
-2. Which text is a tag: cluster OCR lines inside a band into tag-shaped groups
-   and parse the Japanese price-tag fields deterministically.
-3. What we currently believe: a per-session cache with multi-pass voting, so a
-   static tag seen in many frames becomes one settled reading instead of thirty
-   noisy ones - and so the frame loop never waits for OCR.
-
-The service is designed to be driven from the live session: `plan()` decides what
-is worth reading now, `run_pass()` does the OCR (on a worker thread), and
-`attach()` gives each track its tag for the current frame.
-"""
-
-from __future__ import annotations
-
-import os
-import re
-import sys
-import threading
-import time
-import unicodedata
-import uuid
-from dataclasses import dataclass, field
-from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple
-
-current_dir = os.path.dirname(os.path.abspath(__file__))
-if current_dir not in sys.path:
-    sys.path.insert(0, current_dir)
-
-from ocr import OcrEngine, OcrLine, OcrResult, OcrUnavailable  # noqa: E402
-from tag_cards import (  # noqa: E402
-    CORROBORATION_FLAGS,
-    MIN_PRICE,
-    StorePolicy,
-    TagFields as TagFieldsModel,
-    corroborate,
-    best_tag_rows,
-    card_from_lines,
-    find_rails,
-    find_tag_blocks_on_rail,
-    group_lines_into_cards,
-    read_card_lines,
-)
-
-TAG_STATE_READABLE = "readable"
-TAG_STATE_UNREADABLE = "unreadable"
-TAG_STATE_ABSENT = "absent"
-# Seen on the rail, OCR returned nothing. Not the same as absent (no tag) or
-# unreadable (text came back but did not corroborate).
-TAG_STATE_DETECTED = "detected"
-
-# Two shelf rows closer than this are the same rail. A screen row index is not
-# an identity: panning a new shelf into view reuses row 0.
-SHELF_ROW_BIN_PX = 80
-
-# Measured tag shape (add/image6.jpg): ~300x80 px, aspect ~3.75:1, digits ~22 px.
-TAG_MIN_WIDTH_RATIO = 0.035
-# Measured real tags cluster at 1.8-3.8 aspect. The floor is deliberately below
-# that: a tag whose name line was not read is still a tag (price line only).
-TAG_MIN_ASPECT = 1.2
-TAG_MAX_ASPECT = 14.0
-BAND_HEIGHT_RATIO = 0.30       # of the row's median product height, below the row
-BAND_OVERLAP_RATIO = 0.03      # start slightly inside the product row
-SETTLE_OBSERVATIONS = 3
-SETTLE_PASSES = 8
-
-_PRICE_RE = re.compile(r"(?<!\d)(\d{2,5})(?!\d)")
-# A unit price is a decimal (290.52). The 割 lookahead keeps "2.4割引" - a discount -
-# from being read as a unit price.
-_UNIT_PRICE_RE = re.compile(r"(?<![\d.])(\d{1,5}\.\d{1,2})(?![\d.])(?!\s*割)")
-_DISCOUNT_RE = re.compile(r"(\d{1,2}(?:\.\d)?)\s*割")
-# Packaging sizes ("<310g>", "24枚", "500ml") are not prices.
-_SIZE_RE = re.compile(r"<?\d{1,5}(?:\.\d)?\s*(?:g|kg|ml|mL|L|枚|個|袋|入|本|包|缶|ℓ)>?")
-_KANA_KANJI_RE = re.compile(r"[ぁ-んァ-ヶ一-龥ー]{2,}")
-_SALE_WORDS = ("お買得", "お買い得", "特価", "セール", "割引", "値引", "毎日", "新発売", "税込", "税抜", "本体", "円")
-# Tax and format phrases are not part of a product name ("エッグケア税不使用").
-_TAX_RE = re.compile(r"税(込|抜|不|別)")
-
-
-@dataclass
-class RailBand:
-    """One horizontal strip of shelf rail, derived from a product row.
-
-    `box` is this frame's pixels (the crop `run_pass` reads). `origin` is the
-    shelf position of the frame origin at the moment `plan` chose the band, and
-    `key` is the rail that origin names. Both travel with the band: a later
-    frame's origin must not relabel a pass that is still queued.
-    """
-
-    box: List[int]
-    row_index: int
-    product_ids: List[int] = field(default_factory=list)
-    origin: Optional[Tuple[float, float]] = None
-    key: Optional[Tuple[str, int]] = None
-
-
-# The field model lives with the card reader; the pipeline consumes it unchanged.
-# It adds the JAN's last three digits (the identity anchor printed on a Japanese
-# tag) and records whether a price was measured or derived from the tax relation.
-TagFields = TagFieldsModel
-
-
-@dataclass
-class TagObservation:
-    """One OCR pass over one tag-shaped region."""
-
-    box: List[int]
-    lines: List[OcrLine]
-    fields: TagFields
-    band_index: int
-    pass_id: int
-    observed_at: float = 0.0
-
-    @property
-    def confidence(self) -> float:
-        if not self.lines:
-            return 0.0
-        return sum(line.confidence for line in self.lines) / len(self.lines)
-
-
-@dataclass
-class TagRecord:
-    """The session's current belief about one physical tag."""
-
-    tag_id: str
-    box: List[int]
-    band_index: int
-    shelf_key: Tuple[str, int] = ("screen", 0)
-    observations: List[TagObservation] = field(default_factory=list)
-    first_seen: float = 0.0
-    last_seen: float = 0.0
-    settled: bool = False
-    passes: int = 0
-
-    # voted values
-    price: Optional[int] = None
-    regular_price: Optional[int] = None
-    discount: Optional[float] = None
-    unit_price: Optional[float] = None
-    name: Optional[str] = None
-    price_source: str = "none"
-    flags: List[str] = field(default_factory=list)
-    confidence: float = 0.0
-
-    @property
-    def state(self) -> str:
-        """Readable only when the tag's own arithmetic corroborates the price.
-
-        Measured live: without this, single uncorroborated reads of the stylised
-        digits were published as readable tags with prices of 20, 23, 72 and 4508.
-        A block seen with no text is `detected`; text that does not parse is
-        `unreadable`. Neither is `absent`.
-        """
-        if self.price is None:
-            saw_text = any(observation.lines for observation in self.observations)
-            return TAG_STATE_UNREADABLE if saw_text else TAG_STATE_DETECTED
-        if self.price < MIN_PRICE:
-            return TAG_STATE_UNREADABLE
-        corroborated = bool(
-            {"tax_consistent", "tax_pair_consistent", "discount_consistent"} & set(self.flags)
-        )
-        if not corroborated or (self.regular_price is None and self.name is None):
-            return TAG_STATE_UNREADABLE
-        return TAG_STATE_READABLE
-
-    def as_dict(self) -> Dict[str, Any]:
-        return {
-            "id": self.tag_id,
-            "box": list(self.box),
-            "state": self.state,
-            "price": self.price,
-            "regular_price": self.regular_price,
-            "discount": self.discount,
-            "unit_price": self.unit_price,
-            "name": self.name,
-            "flags": list(self.flags),
-            "confidence": round(float(self.confidence), 3),
-            "observations": len(self.observations),
-            "settled": self.settled,
-        }
-
-
-# ---------------------------------------------------------------------------
-# geometry
-# ---------------------------------------------------------------------------
-
-
-def _median(values: Sequence[float]) -> float:
-    if not values:
-        return 0.0
-    ordered = sorted(values)
-    middle = len(ordered) // 2
-    if len(ordered) % 2:
-        return float(ordered[middle])
-    return (ordered[middle - 1] + ordered[middle]) / 2.0
-
-
-def rail_bands(boxes: Sequence[Dict[str, Any]], image_shape: Sequence[int]) -> List[RailBand]:
-    """Rail strips below each product row, using the detector's own row clustering."""
-    from empty_shelf_detector import EmptyShelfDetector
-
-    height, width = int(image_shape[0]), int(image_shape[1])
-    if not boxes or height <= 0 or width <= 0:
-        return []
-
-    detector = EmptyShelfDetector(min_row_products=2)
-    rows = detector.cluster_rows([[int(v) for v in box["box"]] for box in boxes])
-    bands: List[RailBand] = []
-    for row_index, row in enumerate(rows):
-        bottoms = [box[3] for box in row]
-        heights = [box[3] - box[1] for box in row]
-        bottom = _median(bottoms)
-        median_height = max(1.0, _median(heights))
-        x1 = max(0, min(box[0] for box in row))
-        x2 = min(width, max(box[2] for box in row))
-        top = int(max(0, bottom - median_height * BAND_OVERLAP_RATIO))
-        band_bottom = int(min(height, bottom + median_height * BAND_HEIGHT_RATIO))
-        if band_bottom - top < 8 or x2 - x1 < 16:
-            continue
-        bands.append(
-            RailBand(box=[x1, top, x2, band_bottom], row_index=row_index + 1, product_ids=[])
-        )
-    return bands
-
-
-def lines_in_band(lines: Sequence[OcrLine], band: RailBand) -> List[OcrLine]:
-    x1, y1, x2, y2 = band.box
-    inside: List[OcrLine] = []
-    for line in lines:
-        cx = (line.box[0] + line.box[2]) / 2.0
-        cy = (line.box[1] + line.box[3]) / 2.0
-        if x1 <= cx <= x2 and y1 <= cy <= y2:
-            inside.append(line)
-    return inside
-
-
-def cluster_tags(lines: Sequence[OcrLine], band: RailBand, image_shape: Sequence[int]) -> List[Dict[str, Any]]:
-    """Group OCR lines into tag-shaped regions inside one band."""
-    height, width = int(image_shape[0]), int(image_shape[1])
-    candidates = sorted(lines_in_band(lines, band), key=lambda line: line.box[0])
-    if not candidates:
-        return []
-
-    heights = [line.box[3] - line.box[1] for line in candidates]
-    median_height = max(4.0, _median(heights))
-    gap_limit = 1.5 * median_height
-
-    groups: List[List[OcrLine]] = [[candidates[0]]]
-    for line in candidates[1:]:
-        previous = groups[-1][-1]
-        gap = line.box[0] - previous.box[2]
-        if gap <= gap_limit:
-            groups[-1].append(line)
-        else:
-            groups.append([line])
-
-    tags: List[Dict[str, Any]] = []
-    for group in groups:
-        x1 = min(line.box[0] for line in group)
-        y1 = min(line.box[1] for line in group)
-        x2 = max(line.box[2] for line in group)
-        y2 = max(line.box[3] for line in group)
-        pad_x = max(2, int((x2 - x1) * 0.06))
-        pad_y = max(2, int((y2 - y1) * 0.15))
-        box = [max(0, x1 - pad_x), max(0, y1 - pad_y), min(width, x2 + pad_x), min(height, y2 + pad_y)]
-        box_width = box[2] - box[0]
-        box_height = max(1, box[3] - box[1])
-        aspect = box_width / box_height
-        if box_width < TAG_MIN_WIDTH_RATIO * width:
-            continue
-        if not (TAG_MIN_ASPECT <= aspect <= TAG_MAX_ASPECT):
-            continue
-        # Pass the line boxes: the price preference is by printed area, so the big
-        # sale price beats the small regular price.
-        fields = parse_price_tag([line.text for line in group], group)
-        if len(group) < 2 and fields.price is None:
-            continue
-        tags.append({"box": box, "lines": group, "fields": fields, "band_index": band.row_index})
-    return tags
-
-
-# ---------------------------------------------------------------------------
-# parsing
-# ---------------------------------------------------------------------------
-
-
-def _normalize(text: str) -> str:
-    return unicodedata.normalize("NFKC", text)
-
-
-def _text_area(line: OcrLine) -> int:
-    return max(0, line.box[2] - line.box[0]) * max(0, line.box[3] - line.box[1])
-
-
-def parse_price_tag(texts: Sequence[str], lines: Optional[Sequence[OcrLine]] = None) -> TagFields:
-    """Deterministic field extraction from a Japanese shelf price tag.
-
-    Rejects a parse outright when the values cannot be sane; the caller must treat
-    a missing field as "not read", never as zero.
-    """
-    fields = TagFields()
-    normalized = [_normalize(text) for text in texts]
-    joined = " ".join(normalized)
-
-    discount_match = _DISCOUNT_RE.search(joined)
-    if discount_match:
-        try:
-            value = float(discount_match.group(1))
-            if 0 < value <= 9:
-                fields.discount = round(value / 10.0, 4)
-                fields.flags.append("sale")
-        except ValueError:
-            pass
-
-    unit_match = _UNIT_PRICE_RE.search(joined)
-    if unit_match:
-        try:
-            fields.unit_price = float(unit_match.group(1))
-        except ValueError:
-            pass
-
-    # Prices: prefer the largest printed integer by text area (the sale price is
-    # the big number on a Japanese tag), then order the rest as the regular price.
-    # Decimals and discount percentages are removed first so "290.52" cannot be
-    # read as 290 and "2.4割引" cannot be read as a price.
-    price_candidates: List[Tuple[int, float]] = []
-    for text, line in zip(normalized, lines or [None] * len(normalized)):
-        text = _UNIT_PRICE_RE.sub(" ", text)
-        text = _DISCOUNT_RE.sub(" ", text)
-        text = _SIZE_RE.sub(" ", text)
-        for match in _PRICE_RE.finditer(text):
-            try:
-                value = int(match.group(1))
-            except ValueError:
-                continue
-            # A Japanese shelf tag is ¥10-¥9,999. Five-digit values are misreads
-            # (a packaging size, a unit price, a barcode fragment).
-            if not (10 <= value <= 9999):
-                continue
-            area = float(_text_area(line)) if line is not None else float(len(match.group(1)))
-            price_candidates.append((value, area))
-    if not price_candidates:
-        return fields
-
-    price_candidates.sort(key=lambda item: item[1], reverse=True)
-    fields.price = price_candidates[0][0]
-    if len(price_candidates) > 1:
-        runner_up = price_candidates[1][0]
-        if runner_up != fields.price:
-            fields.regular_price = runner_up
-
-    # Name: the longest kana/kanji text that is not a price or a sale banner.
-    # Runs are joined per line, because OCR often splits a name across boxes
-    # ("ユービーゼロ" + "ノンコレステロール" is one product name).
-    best_name = None
-    for text in normalized:
-        cleaned = _SIZE_RE.sub(" ", text)
-        cleaned = re.sub(r"[＜＞<>（）()「」【】]", " ", cleaned)
-        runs = [
-            match.group(0)
-            for match in _KANA_KANJI_RE.finditer(cleaned)
-            if len(match.group(0)) >= 2
-            and not any(word in match.group(0) for word in _SALE_WORDS)
-            and not _TAX_RE.search(match.group(0))
-        ]
-        candidate = "".join(runs).strip()
-        if len(candidate) < 3:
-            continue
-        if best_name is None or len(candidate) > len(best_name):
-            best_name = candidate[:40]
-    fields.name = best_name
-
-    if "税込" in joined:
-        fields.flags.append("tax_included")
-    if "新発売" in joined or "NEW" in joined.upper():
-        fields.flags.append("new")
-    if fields.discount:
-        fields.flags.append("discounted")
-    return fields
-
-
-# ---------------------------------------------------------------------------
-# the cached tag pass
-# ---------------------------------------------------------------------------
-
-
-@dataclass
-class TagSettings:
-    enabled: bool = True
-    # OCR is the most expensive thing the server does (~124 ms per rail band with
-    # macOS Vision, and pyobjc holds the GIL for the duration), so in live mode it
-    # must not run often. Measured: at 15 frames / 2 bands a walking session
-    # dropped to ~1.5 results/s; 30 frames / 1 band keeps detection real-time
-    # while tags still settle within a second or two.
-    pass_interval_frames: int = 30
-    max_bands_per_pass: int = 1
-    settle_observations: int = SETTLE_OBSERVATIONS
-    settle_passes: int = SETTLE_PASSES
-    min_band_width_ratio: float = 0.10
-    max_tags: int = 120
-    policy: Optional[StorePolicy] = None
-
-
-def band_key(band: RailBand, shelf_offset: Optional[Sequence[float]] = None) -> Tuple[str, int]:
-    """Which rail this band is, stable across a pan.
-
-    No shelf origin: the screen row, which is all a still has. With one (the
-    shelf position of the frame origin), the row's shelf y, so a new shelf that
-    lands on screen row 0 is not the row that already settled.
-    """
-    if not shelf_offset or len(shelf_offset) < 2:
-        return ("screen", int(band.row_index))
-    shelf_y = (band.box[1] + band.box[3]) / 2.0 + float(shelf_offset[1])
-    return ("shelf", int(round(shelf_y / SHELF_ROW_BIN_PX)))
-
-
-class TagService:
-    """Per-session tag state: cache, voting, and pass scheduling.
-
-    Thread-safe. `plan()` and `attach()` are called from the frame loop (cheap,
-    under the session's own lock); `run_pass()` runs on a worker thread and may
-    take as long as the OCR engine needs.
-    """
-
-    def __init__(
-        self,
-        engine: OcrEngine,
-        *,
-        settings: Optional[TagSettings] = None,
-        executor: Any = None,
-    ):
-        self.engine = engine
-        self.settings = settings or TagSettings()
-        self._executor = executor
-        self._lock = threading.Lock()
-        self._tags: Dict[str, TagRecord] = {}
-        self._pass_id = 0
-        self._frame_seq = 0
-        self._last_pass_seq: Dict[Tuple[str, int], int] = {}
-        self._pending_bands: set = set()
-        self.passes_run = 0
-        self.passes_failed = 0
-        self.last_pass_error: Optional[str] = None
-        self.passes_skipped = 0
-        # Latest frame only. A queued pass must not read these; its origin is on the band.
-        self._view_origin: Optional[Tuple[float, float]] = None
-        self._view_shape: Optional[Tuple[int, int]] = None
-
-    # ------------------------------------------------------------------ frame loop
-    def plan(
-        self,
-        boxes: Sequence[Dict[str, Any]],
-        image_shape: Sequence[int],
-        shelf_offset: Optional[Sequence[float]] = None,
-    ) -> List[RailBand]:
-        """Bands worth OCR-ing right now (must be called from the frame loop).
-
-        The key is the shelf row when the caller passes the frame origin, and the
-        screen row otherwise. Keying by on-screen pixels re-read the same shelf
-        on every pan step (measured: 10x slower per crop while OCR ran); keying
-        by the screen row index instead hides a new shelf that reuses row 0.
-        """
-        if not self.settings.enabled or not self.engine.available():
-            return []
-        if self.settings.policy is not None:
-            self.settings.policy.active()
-        bands = rail_bands(boxes, image_shape)
-        width = float(image_shape[1])
-        bands = [band for band in bands if (band.box[2] - band.box[0]) >= self.settings.min_band_width_ratio * width]
-        with self._lock:
-            self._view_origin = _origin_of(shelf_offset)
-            self._view_shape = (int(image_shape[0]), int(image_shape[1]))
-            self._frame_seq += 1
-            seq = self._frame_seq
-            chosen: List[RailBand] = []
-            for band in bands:
-                key = band_key(band, shelf_offset)
-                if key in self._pending_bands:
-                    continue
-                last = self._last_pass_seq.get(key)
-                settled = self._band_settled(key)
-                if settled and last is not None:
-                    continue
-                if last is not None and seq - last < self.settings.pass_interval_frames:
-                    continue
-                chosen.append(band)
-                if len(chosen) >= self.settings.max_bands_per_pass:
-                    break
-            for band in chosen:
-                key = band_key(band, shelf_offset)
-                band.origin = _origin_of(shelf_offset)
-                band.key = key
-                self._last_pass_seq[key] = seq
-                self._pending_bands.add(key)
-        return chosen
-
-    def run_pass(self, image: Any, band: RailBand, scale_bias: float = 1.0) -> List[TagObservation]:
-        """OCR one band and fold the observations into the cache (worker thread).
-
-        `scale_bias` lets a caller read the same band at a different upscale: two
-        independent reads of the same tag are what makes the vote meaningful
-        (identical pixels would produce identical output).
-        """
-        key = _planned_key(band)
-        try:
-            x1, y1, x2, y2 = band.box
-            crop = image[y1:y2, x1:x2]
-            if crop.size == 0:
-                return []
-            # Tags are small; a modest upscale helps the recognizer (measured).
-            scale = 1.0
-            digit_estimate = crop.shape[0]
-            if digit_estimate < 90:
-                scale = min(2.5, 120.0 / max(1, digit_estimate))
-            scale = max(1.0, min(3.0, scale * max(0.5, scale_bias)))
-            if scale > 1.0:
-                import cv2
-
-                crop = cv2.resize(crop, None, fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC)
-            result: OcrResult = self.engine.read(crop)
-            lines = [
-                OcrLine(
-                    text=line.text,
-                    confidence=line.confidence,
-                    box=[
-                        int(round(line.box[0] / scale)) + x1,
-                        int(round(line.box[1] / scale)) + y1,
-                        int(round(line.box[2] / scale)) + x1,
-                        int(round(line.box[3] / scale)) + y1,
-                    ],
-                )
-                for line in result.lines
-            ]
-            candidates = self._card_candidates(image, lines, band, scale_bias)
-            candidates.extend(self._visual_candidates(image, band, candidates))
-            with self._lock:
-                self._pass_id += 1
-                pass_id = self._pass_id
-                observations = [
-                    self._absorb(candidate, band, pass_id) for candidate in candidates
-                ]
-                self.passes_run += 1
-            return observations
-        finally:
-            with self._lock:
-                self._pending_bands.discard(key)
-
-    def _card_candidates(
-        self,
-        image: Any,
-        lines: Sequence[OcrLine],
-        band: RailBand,
-        scale_bias: float,
-    ) -> List[Dict[str, Any]]:
-        """Locate tag cards in a band, then read each card on its own.
-
-        The band read only *finds* the cards (its line boxes are enough to group
-        them); each card is then re-read at several scales and in the red channel,
-        and its fields come from the pooled reads. That is what makes a tag's box
-        line up with the printed tag and its values belong to the right card.
-        """
-        rows = best_tag_rows(lines, keep=1)
-        if not rows:
-            return []
-        cards: List[Dict[str, Any]] = []
-        for group in group_lines_into_cards(rows[0]):
-            seed = card_from_lines(group)
-            box = seed.box
-            width = box[2] - box[0]
-            height = max(1, box[3] - box[1])
-            if width < TAG_MIN_WIDTH_RATIO * image.shape[1]:
-                continue
-            aspect = width / float(height)
-            if not (TAG_MIN_ASPECT <= aspect <= TAG_MAX_ASPECT):
-                continue
-            pooled = read_card_lines(
-                self.engine, image, box, scales=(1.0, 2.0, 3.0 * max(1.0, scale_bias))
-            )
-            card = card_from_lines(pooled) if pooled else seed
-            card.box = box
-            if not card.fields.is_readable() and card.fields.price is None and not group:
-                continue
-            cards.append(
-                {
-                    "box": list(box),
-                    "lines": card.lines,
-                    "fields": card.fields,
-                    "band_index": band.row_index,
-                }
-            )
-        return cards
-
-    def _visual_candidates(
-        self,
-        image: Any,
-        band: RailBand,
-        known: Sequence[Dict[str, Any]],
-    ) -> List[Dict[str, Any]]:
-        """Tags that can be seen on the rail but whose text could not be read.
-
-        Without this, "this product has no price tag" and "I could not read the
-        tag" are the same answer, and a missing-tag claim would be built on the
-        reader's failures rather than on the shelf. A tag found here is reported
-        with an empty reading, so the rules can tell the two apart.
-        """
-        try:
-            rails = find_rails(image)
-        except Exception:  # a vision helper must never break the frame loop
-            return []
-        band_box = band.box
-        rail = None
-        best_overlap = 0
-        for candidate in rails:
-            overlap = min(band_box[3], candidate[3]) - max(band_box[1], candidate[1])
-            if overlap > best_overlap:
-                best_overlap, rail = overlap, candidate
-        if rail is None or best_overlap <= 0:
-            return []
-        try:
-            blocks = find_tag_blocks_on_rail(image, rail)
-        except Exception:
-            return []
-        extra: List[Dict[str, Any]] = []
-        for block in blocks:
-            if any(_iou(block, item["box"]) > 0.25 for item in known):
-                continue
-            extra.append(
-                {
-                    "box": list(block),
-                    "lines": [],
-                    "fields": TagFields(),
-                    "band_index": band.row_index,
-                }
-            )
-        return extra
-
-    def _band_settled(self, key: Tuple[str, int]) -> bool:
-        row_tags = [tag for tag in self._tags.values() if tag.shelf_key == key]
-        if not row_tags:
-            return False
-        return all(tag.settled for tag in row_tags)
-
-    def _absorb(self, candidate: Dict[str, Any], band: RailBand, pass_id: int) -> TagObservation:
-        """Fold one clustered tag into the cache; must be called with the lock held.
-
-        The candidate box is this frame's pixels. The record keeps shelf pixels,
-        using the origin `plan` stamped on the band, so a later pan cannot make
-        this read land on a different rail.
-        """
-        box = candidate["box"]
-        lines = candidate["lines"]
-        fields = candidate["fields"]
-        shelf_key = _planned_key(band)
-        shelf_box = _to_shelf(box, band.origin)
-        observation = TagObservation(
-            box=list(shelf_box),
-            lines=list(lines),
-            fields=fields,
-            band_index=band.row_index,
-            pass_id=pass_id,
-            observed_at=time.time(),
-        )
-
-        record = self._match_record(shelf_box, shelf_key)
-        if record is None:
-            record = TagRecord(
-                tag_id=f"tag_{uuid.uuid4().hex[:8]}",
-                box=list(shelf_box),
-                band_index=band.row_index,
-                shelf_key=shelf_key,
-                first_seen=observation.observed_at,
-            )
-            if len(self._tags) >= self.settings.max_tags:
-                return observation
-            self._tags[record.tag_id] = record
-        record.box = list(shelf_box)
-        record.last_seen = observation.observed_at
-        record.observations.append(observation)
-        record.passes += 1
-        self._vote(record)
-        if (
-            len(record.observations) >= self.settings.settle_observations
-            and record.price is not None
-        ) or record.passes >= self.settings.settle_passes:
-            record.settled = True
-        return observation
-
-    def _match_record(self, box: Sequence[int], shelf_key: Tuple[str, int]) -> Optional[TagRecord]:
-        best = None
-        best_iou = 0.35
-        for record in self._tags.values():
-            if record.shelf_key != shelf_key:
-                continue
-            iou = _iou(box, record.box)
-            if iou >= best_iou:
-                best_iou = iou
-                best = record
-        return best
-
-    def _vote(self, record: TagRecord) -> None:
-        """Majority vote over observations: the accuracy lever for digit misreads."""
-        # Votes are weighted by the read's confidence: two equally common values
-        # are then decided by which read was surer, never by which number is
-        # bigger. A tie with no confident read stays unread.
-        prices: Dict[int, float] = {}
-        regulars: Dict[int, float] = {}
-        names: Dict[str, float] = {}
-        units: Dict[float, float] = {}
-        discounts: Dict[float, float] = {}
-        regulars: Dict[int, float] = {}
-        sources: Dict[str, float] = {}
-        flags: set = set()
-        confidences: List[float] = []
-        for observation in record.observations:
-            fields = observation.fields
-            weight = max(0.05, float(observation.confidence))
-            if fields.price is not None:
-                prices[fields.price] = prices.get(fields.price, 0.0) + weight
-            if fields.regular_price is not None:
-                regulars[fields.regular_price] = regulars.get(fields.regular_price, 0.0) + weight
-            if fields.name:
-                names[fields.name] = names.get(fields.name, 0.0) + weight
-            if fields.unit_price is not None:
-                units[fields.unit_price] = units.get(fields.unit_price, 0.0) + weight
-            if fields.discount is not None:
-                discounts[fields.discount] = discounts.get(fields.discount, 0.0) + weight
-            if getattr(fields, "regular_price", None):
-                regulars[fields.regular_price] = regulars.get(fields.regular_price, 0.0) + weight
-            sources[getattr(fields, "price_source", "none")] = (
-                sources.get(getattr(fields, "price_source", "none"), 0.0) + weight
-            )
-            flags.update(fields.flags)
-            confidences.append(observation.confidence)
-
-        def top(counter: Dict[Any, float]) -> Any:
-            if not counter:
-                return None
-            ranked = sorted(counter.items(), key=lambda item: item[1], reverse=True)
-            if len(ranked) > 1 and abs(ranked[0][1] - ranked[1][1]) < 1e-9:
-                # A dead heat between two different readings is not evidence.
-                return None
-            return ranked[0][0]
-
-        record.price = top(prices)
-        record.regular_price = top(regulars)
-        record.name = top(names)
-        record.unit_price = top(units)
-        record.discount = top(discounts)
-        record.regular_price = top(regulars)
-        # "measured" beats "derived" when both appear: a read is better evidence
-        # than an inference, even though the inference is usually right.
-        if sources.get("measured") and sources.get("derived"):
-            record.price_source = "measured" if sources["measured"] >= sources["derived"] else "derived"
-        else:
-            record.price_source = top(sources) or "none"
-        # Voting picks each field independently, so the winners can be incoherent
-        # (measured: a price from one frame beside a tax-included price from
-        # another). Re-run the tag's arithmetic on what is actually being published.
-        published = TagFieldsModel(
-            price=record.price,
-            regular_price=record.regular_price,
-            discount=record.discount,
-            unit_price=record.unit_price,
-            name=record.name,
-        )
-        arithmetic = set(corroborate(published))
-        record.price = published.price
-        record.regular_price = published.regular_price
-        record.discount = published.discount
-        record.price_source = published.price_source
-        record.flags = sorted((flags - CORROBORATION_FLAGS) | arithmetic)
-        record.confidence = sum(confidences) / len(confidences) if confidences else 0.0
-
-    # ------------------------------------------------------------------ attach
-    def attach(
-        self,
-        tracks: Sequence[Dict[str, Any]],
-        shelf_offset: Optional[Sequence[float]] = None,
-        image_shape: Optional[Sequence[int]] = None,
-    ) -> Tuple[Dict[int, Dict[str, Any]], List[Dict[str, Any]], Dict[str, int]]:
-        """Give each track its tag for this frame.
-
-        Records live in shelf coordinates. They are projected into this frame
-        before association, and a tag that does not overlap the frame is left
-        out, so an old tag cannot bind a new facing that reused its screen box.
-        Returned boxes stay in screen pixels. A caller that omits `shelf_offset`
-        is a still: screen coordinates, never another frame's stored origin.
-
-        Returns (tag_by_track_id, all_tags, state_counts). A track whose tag was
-        never read gets state 'unreadable' when a tag was found near it, and
-        'absent' when nothing tag-shaped was seen there.
-        """
-        with self._lock:
-            origin = _origin_of(shelf_offset)
-            shape = (
-                (int(image_shape[0]), int(image_shape[1]))
-                if image_shape is not None
-                else self._view_shape
-            )
-            visible = _visible_records(list(self._tags.values()), origin, shape)
-
-        by_track: Dict[int, Dict[str, Any]] = {}
-        used: set = set()
-        records = [record for record, _screen in visible]
-        screen_of = {record.tag_id: screen for record, screen in visible}
-        tags = [_screen_dict(record, screen_of[record.tag_id]) for record in records]
-        for track in tracks:
-            box = track.get("box") or track.get("raw_box")
-            if not box:
-                continue
-            record = _tag_below(box, records, used, screen_of)
-            if record is not None:
-                used.add(record.tag_id)
-                payload = _screen_dict(record, screen_of[record.tag_id])
-                payload["associated"] = True
-                by_track[int(track["track_id"])] = payload
-            elif self._band_had_readable(record=None, box=box, records=records, screen_of=screen_of):
-                by_track[int(track["track_id"])] = _empty_tag(TAG_STATE_UNREADABLE)
-            else:
-                by_track[int(track["track_id"])] = _empty_tag(TAG_STATE_ABSENT)
-
-        counts = {
-            TAG_STATE_READABLE: 0,
-            TAG_STATE_UNREADABLE: 0,
-            TAG_STATE_DETECTED: 0,
-            TAG_STATE_ABSENT: 0,
-        }
-        for payload in by_track.values():
-            counts[payload["state"]] = counts.get(payload["state"], 0) + 1
-        return by_track, tags, counts
-
-    def _band_had_readable(
-        self,
-        record,
-        box: Sequence[int],
-        records: Sequence[TagRecord],
-        screen_of: Optional[Dict[str, List[int]]] = None,
-    ) -> bool:
-        """Was there any tag-shaped evidence near this facing (readable or not)?"""
-        x1, y1, x2, y2 = box
-        width = max(1, x2 - x1)
-        for candidate in records:
-            shown = (screen_of or {}).get(candidate.tag_id, candidate.box)
-            cx = (shown[0] + shown[2]) / 2.0
-            cy = (shown[1] + shown[3]) / 2.0
-            if x1 - width * 0.3 <= cx <= x2 + width * 0.3 and y1 <= cy <= y2 + (y2 - y1) * 0.5:
-                return True
-        return False
-
-    # ------------------------------------------------------------------ housekeeping
-    def note_pass_failure(self, error: Any) -> None:
-        """Record a failed OCR pass.
-
-        The failure used to be swallowed entirely, so "no tags on this shelf" and
-        "the OCR is broken" looked identical from the outside.
-        """
-        with self._lock:
-            self.passes_failed += 1
-            self.last_pass_error = str(error)[:200] or error.__class__.__name__
-
-    def stats(self) -> Dict[str, Any]:
-        with self._lock:
-            return {
-                "engine": self.engine.name,
-                "available": bool(self.engine.available()),
-                "tags": len(self._tags),
-                "settled": sum(1 for tag in self._tags.values() if tag.settled),
-                "passes_run": self.passes_run,
-                "passes_failed": self.passes_failed,
-                "last_pass_error": self.last_pass_error,
-                "pending_bands": len(self._pending_bands),
-                "frame_seq": self._frame_seq,
-            }
-
-    def reset(self) -> None:
-        with self._lock:
-            self._tags.clear()
-            self._last_pass_seq.clear()
-            self._pending_bands.clear()
-            self._pass_id = 0
-            self._frame_seq = 0
-            self._view_origin = None
-            self._view_shape = None
-
-
-def _iou(a: Sequence[int], b: Sequence[int]) -> float:
-    ax1, ay1, ax2, ay2 = a
-    bx1, by1, bx2, by2 = b
-    ix1, iy1 = max(ax1, bx1), max(ay1, by1)
-    ix2, iy2 = min(ax2, bx2), min(ay2, by2)
-    iw, ih = max(0, ix2 - ix1), max(0, iy2 - iy1)
-    inter = iw * ih
-    if inter <= 0:
-        return 0.0
-    area_a = max(0, ax2 - ax1) * max(0, ay2 - ay1)
-    area_b = max(0, bx2 - bx1) * max(0, by2 - by1)
-    union = area_a + area_b - inter
-    return inter / union if union > 0 else 0.0
-
-
-def _tag_below(
-    box: Sequence[int],
-    records: Sequence[TagRecord],
-    used: set,
-    screen_of: Optional[Dict[str, List[int]]] = None,
-) -> Optional[TagRecord]:
-    """The tag that belongs to this facing: below it, horizontally overlapping.
-
-    `screen_of` is the record projected into the current frame. Without it the
-    stored shelf box is used, which is the screen box when no origin was known.
-    """
-    x1, y1, x2, y2 = box
-    width = max(1, x2 - x1)
-    height = max(1, y2 - y1)
-    best: Optional[TagRecord] = None
-    best_score = -1.0
-    for record in records:
-        if record.tag_id in used:
-            continue
-        rx1, ry1, rx2, ry2 = (screen_of or {}).get(record.tag_id, record.box)
-        center_x = (rx1 + rx2) / 2.0
-        overlap = max(0, min(x2, rx2) - max(x1, rx1))
-        overlap_ratio = overlap / max(1, rx2 - rx1)
-        vertical_ok = y2 - height * 0.4 <= ry1 <= y2 + height * 0.8
-        # A tag belongs to the facing above it. When a cluster spans several
-        # facings (two tags merged, or a wide read), the nearest center wins
-        # rather than a strict overlap test that would drop the association.
-        horizontal_ok = (
-            overlap_ratio >= 0.25
-            or x1 - width * 0.5 <= center_x <= x2 + width * 0.5
-        )
-        if vertical_ok and horizontal_ok:
-            distance = abs(center_x - (x1 + x2) / 2.0) / max(1.0, width)
-            score = overlap_ratio - 0.5 * distance
-            if score > best_score:
-                best_score = score
-                best = record
-    return best
-
-
-def _origin_of(shelf_offset: Optional[Sequence[float]]) -> Optional[Tuple[float, float]]:
-    if not shelf_offset or len(shelf_offset) < 2:
-        return None
-    return (float(shelf_offset[0]), float(shelf_offset[1]))
-
-
-def _planned_key(band: RailBand) -> Tuple[str, int]:
-    """The rail `plan` chose. A band built without `plan` falls back to its screen row."""
-    if band.key is not None:
-        return band.key
-    return band_key(band, band.origin)
-
-
-def _shift_box(box: Sequence[int], dx: float, dy: float) -> List[int]:
-    return [int(round(box[0] + dx)), int(round(box[1] + dy)), int(round(box[2] + dx)), int(round(box[3] + dy))]
-
-
-def _to_shelf(box: Sequence[int], origin: Optional[Tuple[float, float]]) -> List[int]:
-    if origin is None:
-        return [int(v) for v in box]
-    return _shift_box(box, origin[0], origin[1])
-
-
-def _to_screen(box: Sequence[int], origin: Optional[Tuple[float, float]]) -> List[int]:
-    if origin is None:
-        return [int(v) for v in box]
-    return _shift_box(box, -origin[0], -origin[1])
-
-
-def _overlaps_frame(box: Sequence[int], image_shape: Sequence[int]) -> bool:
-    height, width = int(image_shape[0]), int(image_shape[1])
-    return box[2] > 0 and box[0] < width and box[3] > 0 and box[1] < height
-
-
-def _visible_records(
-    records: Sequence[TagRecord],
-    origin: Optional[Tuple[float, float]],
-    image_shape: Optional[Sequence[int]],
-) -> List[Tuple[TagRecord, List[int]]]:
-    shown: List[Tuple[TagRecord, List[int]]] = []
-    for record in records:
-        screen = _to_screen(record.box, origin)
-        if image_shape is not None and not _overlaps_frame(screen, image_shape):
-            continue
-        shown.append((record, screen))
-    return shown
-
-
-def _screen_dict(record: TagRecord, screen_box: Sequence[int]) -> Dict[str, Any]:
-    payload = record.as_dict()
-    payload["box"] = list(screen_box)
-    return payload
-
-
-def _empty_tag(state: str) -> Dict[str, Any]:
-    return {
-        "id": None,
-        "box": None,
-        "state": state,
-        "price": None,
-        "name": None,
-        "confidence": 0.0,
-        "observations": 0,
-        "settled": False,
-    }
-
-
-if __name__ == "__main__":  # smoke: python code/tags.py <image>
-    import argparse
-
-    import cv2
-
-    from engine import EngineSettings, build_default_engine
-    from ocr import build_ocr_engine
-
-    parser = argparse.ArgumentParser(description="Tag pass smoke test")
-    parser.add_argument("image", nargs="?", default="image.jpg")
-    args = parser.parse_args()
-
-    engine = build_default_engine(EngineSettings(device="cpu"))
-    engine.warm_up()
-    ocr_engine = build_ocr_engine()
-    service = TagService(ocr_engine)
-    image = cv2.imread(args.image)
-    boxes = engine.detect(image, conf=0.25)
-    bands = rail_bands(boxes, image.shape)
-    print(f"engine={ocr_engine.name} boxes={len(boxes)} bands={len(bands)}")
-    for band in bands:
-        started = time.perf_counter()
-        observations = service.run_pass(image, band)
-        print(f"  band row={band.row_index} {band.box} -> {len(observations)} tags "
-              f"({(time.perf_counter() - started) * 1000:.0f} ms)")
-    tracks = [{"track_id": index, "box": box["box"]} for index, box in enumerate(boxes)]
-    by_track, tags, counts = service.attach(tracks)
-    print(f"tags: {len(tags)}  states: {counts}")
-    for tag in tags[:6]:
-        print(f"  {tag['id']} {tag['state']} price={tag['price']} name={tag['name']} {tag['box']}")
-    engine.shutdown()
diff --git a/recognization/code/test.py b/recognization/code/test.py
new file mode 100644
index 0000000..5eb7553
--- /dev/null
+++ b/recognization/code/test.py
@@ -0,0 +1,25 @@
+from ultralytics import YOLO
+import cv2
+
+model = YOLO("/Users/nguyenhongvan/recongnize/runs/detect/train/weights/best.pt")
+
+image_path = "/Users/nguyenhongvan/recongnize/data/add/image9.jpg"
+
+results = model(image_path)
+
+image = cv2.imread(image_path)
+
+for box in results[0].boxes.xyxy.cpu().numpy():
+    x1, y1, x2, y2 = map(int, box)
+
+    cv2.rectangle(
+        image,
+        (x1, y1),
+        (x2, y2),
+        (0, 255, 0),
+        2
+    )
+
+cv2.imshow("Bounding Boxes Only", image)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/recognization/code/test_api.py b/recognization/code/test_api.py
deleted file mode 100644
index bef9813..0000000
--- a/recognization/code/test_api.py
+++ /dev/null
@@ -1,553 +0,0 @@
-"""
-Contract tests for the recognition server API.
-
-A fake engine stands in for the model, so these run without weights. They defend
-the wire contracts a client depends on: response shapes, the shared error shape,
-auth, limits, and the WebSocket protocol (hello, frames, control messages,
-recoverable errors).
-"""
-
-from __future__ import annotations
-
-import base64
-import json
-import os
-import sys
-import unittest
-
-current_dir = os.path.dirname(os.path.abspath(__file__))
-if current_dir not in sys.path:
-    sys.path.insert(0, current_dir)
-
-import cv2  # noqa: E402
-import numpy as np  # noqa: E402
-
-from app import ServerSettings, create_app  # noqa: E402
-from engine import EngineSettings, RecognitionEngine  # noqa: E402
-from test_live import SmallBoxDetector  # noqa: E402
-from test_live import FakeDetector, FakeInventory, FakeMatcher  # noqa: E402
-
-from fastapi.testclient import TestClient  # noqa: E402
-
-
-
-def make_app(*, token=None, max_sessions=4, detector=None):
-    # The fake matcher is what these tests exercise, so the backend is pinned to
-    # `matcher`; the shipped default is the `local` lane, which reads real
-    # reference images and would ignore the fake.
-    engine = RecognitionEngine(
-        detector or FakeDetector(), FakeMatcher(), FakeInventory(),
-        settings=EngineSettings(classify_workers=2, identity_backend="matcher"),
-    )
-    settings = ServerSettings(token=token, max_sessions=max_sessions)
-    app = create_app(engine, settings, web_dir="/nonexistent")
-    return TestClient(app), engine
-
-
-def jpeg_bytes(width=640, height=480):
-    ok, buffer = cv2.imencode(".jpg", np.zeros((height, width, 3), dtype="uint8"))
-    return buffer.tobytes()
-
-
-def frame_payload(frame_id, payload=None):
-    payload = payload if payload is not None else jpeg_bytes()
-    return frame_id.to_bytes(4, "big") + payload
-
-
-class RestContractTests(unittest.TestCase):
-    def setUp(self):
-        self.client, self.engine = make_app()
-
-    def tearDown(self):
-        self.engine.shutdown()
-
-    def test_health_needs_no_token_and_reports_model(self):
-        response = self.client.get("/v1/health")
-        self.assertEqual(response.status_code, 200)
-        body = response.json()
-        self.assertEqual(body["status"], "ok")
-        self.assertIn("version", body["model"])
-        self.assertEqual(body["catalog"]["sku_count"], 2)
-
-    def test_a_panned_sample_walks_and_stays_portrait(self):
-        # The debug replay source walks a shelf by asking for a frame index. A
-        # pan that returned the same window every time would freeze the stage
-        # while the boxes moved, which is the bug this pins.
-        first = self.client.get("/v1/samples/image6?pan=0")
-        self.assertEqual(first.status_code, 200)
-        self.assertEqual(first.headers["content-type"], "image/jpeg")
-
-        later = self.client.get("/v1/samples/image6?pan=40")
-        self.assertEqual(later.status_code, 200)
-        self.assertNotEqual(first.content, later.content, "a later pan must show a different window")
-
-        import cv2
-        import numpy as np
-
-        decoded = cv2.imdecode(np.frombuffer(later.content, dtype="uint8"), cv2.IMREAD_COLOR)
-        height, width = decoded.shape[:2]
-        self.assertEqual((width, height), (720, 1280), "the window must match the phone's portrait frame")
-
-    def test_a_sample_without_pan_is_the_whole_image(self):
-        response = self.client.get("/v1/samples/image6")
-        self.assertEqual(response.status_code, 200)
-        self.assertGreater(len(response.content), 1024)
-
-    def test_catalog_shape_has_no_local_paths(self):
-        body = self.client.get("/v1/catalog").json()
-        product = body["products"][0]
-        self.assertEqual(set(product), {
-            "sku_id", "product_name", "price", "number_in", "number_out", "number_on",
-            "receipt_date", "expiration_date", "image_url",
-        })
-
-    def test_catalog_image_404_for_unknown_and_traversal(self):
-        response = self.client.get("/v1/catalog/nope/image")
-        self.assertEqual(response.status_code, 404)
-        self.assertEqual(response.json()["error"]["code"], "NOT_FOUND")
-        # A traversal-looking id is rejected before it can reach the filesystem
-        # (either by routing or by the inventory lookup).
-        for sku in ("..%2F..%2Fstock.csv", "../../etc/passwd", "sku_a%2F..%2F..%2Fstock.csv"):
-            response = self.client.get(f"/v1/catalog/{sku}/image")
-            self.assertIn(response.status_code, (400, 404), sku)
-
-    def test_stock_update_validates_and_returns_product(self):
-        before = self.client.get("/v1/catalog").json()["products"]
-        before_on = next(p["number_on"] for p in before if p["sku_id"] == "sku_a")
-        response = self.client.post("/v1/catalog/sku_a/stock", json={"delta": -1})
-        self.assertEqual(response.status_code, 200)
-        self.assertEqual(response.json()["product"]["number_on"], before_on - 1)
-        self.assertEqual(self.client.post("/v1/catalog/sku_a/stock", json={"delta": 0}).status_code, 422)
-        self.assertEqual(self.client.post("/v1/catalog/sku_a/stock", json={"delta": 500}).status_code, 422)
-        self.assertEqual(self.client.post("/v1/catalog/ghost/stock", json={"delta": -1}).status_code, 404)
-
-    def test_recognize_accepts_raw_bytes(self):
-        response = self.client.post(
-            "/v1/recognize?classify=true", content=jpeg_bytes(),
-            headers={"Content-Type": "image/jpeg"},
-        )
-        self.assertEqual(response.status_code, 200)
-        body = response.json()
-        self.assertTrue(body["image_id"].startswith("img_"))
-        self.assertEqual(body["coordinate_convention"], "pixel-xyxy-origin-top-left")
-        self.assertEqual(len(body["detections"]), 1)
-        self.assertIn(body["detections"][0]["identity"]["state"], ("recognized", "unconfirmed"))
-        for key in ("width", "height", "catalog_version", "model_version", "summary", "timing_ms"):
-            self.assertIn(key, body)
-
-    def test_recognize_accepts_multipart(self):
-        response = self.client.post(
-            "/v1/recognize?classify=false",
-            files={"file": ("shelf.jpg", jpeg_bytes(), "image/jpeg")},
-        )
-        self.assertEqual(response.status_code, 200)
-        self.assertEqual(response.json()["detections"][0]["identity"]["state"], "detected")
-
-    def test_recognize_rejects_bad_input(self):
-        self.assertEqual(self.client.post("/v1/recognize", content=b"").status_code, 400)
-        response = self.client.post(
-            "/v1/recognize", content=b"not an image", headers={"Content-Type": "image/jpeg"}
-        )
-        self.assertEqual(response.status_code, 422)
-        self.assertEqual(response.json()["error"]["code"], "UNDECODABLE_IMAGE")
-        response = self.client.post(
-            "/v1/recognize", content=b"{}", headers={"Content-Type": "application/json"}
-        )
-        self.assertEqual(response.status_code, 415)
-        self.assertEqual(response.json()["error"]["code"], "UNSUPPORTED_MEDIA_TYPE")
-
-    def test_a_lying_content_length_is_rejected_while_streaming(self):
-        """The ASGI receive stops once the body passes the cap, before decode."""
-        import anyio
-
-        from transport import BodyLimit
-
-        cap = 32
-        seen = {"bytes": 0}
-
-        async def app(scope, receive, send):
-            while True:
-                message = await receive()
-                if message["type"] != "http.request":
-                    continue
-                seen["bytes"] += len(message.get("body") or b"")
-                if not message.get("more_body"):
-                    break
-            await send({"type": "http.response.start", "status": 200, "headers": []})
-            await send({"type": "http.response.body", "body": b"ok"})
-
-        chunks = [b"x" * 20, b"y" * 20, b"z" * 20]
-
-        async def receive():
-            body = chunks.pop(0) if chunks else b""
-            return {"type": "http.request", "body": body, "more_body": bool(chunks)}
-
-        sent = []
-
-        async def send(message):
-            sent.append(message)
-
-        async def run():
-            await BodyLimit(app, cap)(
-                {"type": "http"}, receive, send
-            )
-
-        anyio.run(run)
-        self.assertLessEqual(seen["bytes"], cap + 20)
-        self.assertEqual(sent[0]["status"], 413)
-        self.assertIn(b"PAYLOAD_TOO_LARGE", sent[1]["body"])
-
-    def test_overflow_after_response_start_sends_no_second_start(self):
-        """A body that overflows after headers began must not start again."""
-        import anyio
-
-        from transport import BodyLimit
-
-        async def app(scope, receive, send):
-            await send({"type": "http.response.start", "status": 200, "headers": []})
-            await receive()
-            await send({"type": "http.response.body", "body": b"ok"})
-
-        sent = []
-
-        async def receive():
-            return {"type": "http.request", "body": b"x" * 40, "more_body": False}
-
-        async def send(message):
-            sent.append(message)
-
-        anyio.run(BodyLimit(app, 8), {"type": "http"}, receive, send)
-        starts = [message for message in sent if message.get("type") == "http.response.start"]
-        self.assertEqual(len(starts), 1)
-        self.assertEqual(starts[0]["status"], 200)
-        self.assertFalse(any(message.get("status") == 413 for message in sent))
-
-    def test_classify_and_inspect_roundtrip(self):
-        recognized = self.client.post(
-            "/v1/recognize?classify=false", content=jpeg_bytes(),
-            headers={"Content-Type": "image/jpeg"},
-        ).json()
-        image_id = recognized["image_id"]
-        box = recognized["detections"][0]["box"]
-
-        classified = self.client.post(
-            f"/v1/images/{image_id}/classify", json={"method": "hybrid"}
-        )
-        self.assertEqual(classified.status_code, 200)
-        self.assertEqual(len(classified.json()["identities"]), 1)
-
-        inspected = self.client.post(
-            f"/v1/images/{image_id}/inspect", json={"box": box, "method": "hybrid"}
-        )
-        self.assertEqual(inspected.status_code, 200)
-        body = inspected.json()
-        self.assertIn("identity", body)
-        self.assertTrue(body["crop_jpeg_b64"].startswith("data:image/jpeg;base64,"))
-
-    def test_unknown_method_is_a_400(self):
-        response = self.client.post(
-            "/v1/recognize?method=nope", content=jpeg_bytes(),
-            headers={"Content-Type": "image/jpeg"},
-        )
-        self.assertEqual(response.status_code, 400)
-        self.assertEqual(response.json()["error"]["code"], "BAD_REQUEST")
-
-    def test_expired_image_id(self):
-        response = self.client.post("/v1/images/img_missing/classify", json={"method": "hybrid"})
-        self.assertEqual(response.status_code, 404)
-        self.assertEqual(response.json()["error"]["code"], "IMAGE_EXPIRED")
-
-    def test_validation_errors_use_the_shared_shape(self):
-        response = self.client.post("/v1/images/img_x/inspect", json={"box": [1, 2, 3]})
-        self.assertEqual(response.status_code, 422)
-        self.assertIn("error", response.json())
-
-
-class AuthTests(unittest.TestCase):
-    def test_token_is_required_when_configured(self):
-        client, engine = make_app(token="s3cret")
-        try:
-            self.assertEqual(client.get("/v1/health").status_code, 200)
-            self.assertEqual(client.get("/v1/catalog").status_code, 401)
-            self.assertEqual(client.get("/v1/catalog", headers={"Authorization": "Bearer wrong"}).status_code, 401)
-            self.assertEqual(client.get("/v1/catalog", headers={"Authorization": "Bearer s3cret"}).status_code, 200)
-        finally:
-            engine.shutdown()
-
-
-class WebSocketProtocolTests(unittest.TestCase):
-    def setUp(self):
-        self.client, self.engine = make_app()
-
-    def tearDown(self):
-        self.engine.shutdown()
-
-    def test_hello_frame_result_and_control_messages(self):
-        with self.client.websocket_connect("/v1/stream") as socket:
-            hello = socket.receive_json()
-            self.assertEqual(hello["type"], "hello")
-            self.assertEqual(hello["protocol"], 1)
-            self.assertIn("session_id", hello)
-            self.assertIn("config", hello)
-            self.assertEqual(hello["limits"]["max_frame_bytes"], 4 * 1024 * 1024)
-
-            socket.send_json({"type": "ping", "t": 7})
-            self.assertEqual(socket.receive_json(), {"type": "pong", "t": 7})
-
-            socket.send_json({"type": "config", "method": "orb"})
-            ack = socket.receive_json()
-            self.assertEqual(ack["type"], "config_ack")
-            self.assertEqual(ack["config"]["method"], "orb")
-
-            socket.send_bytes(frame_payload(11))
-            result = socket.receive_json()
-            self.assertEqual(result["type"], "result")
-            self.assertEqual(result["frame_id"], 11)
-            self.assertEqual(result["seq"], 1)
-            self.assertIn("tracks", result)
-            self.assertIn("summary", result)
-            self.assertIn("quality", result)
-
-            socket.send_json({"type": "reset"})
-            self.assertEqual(socket.receive_json()["type"], "reset_ack")
-
-            socket.send_json({"type": "not-a-thing"})
-            error = socket.receive_json()
-            self.assertEqual(error["code"], "BAD_MESSAGE")
-
-    def test_short_frame_is_recoverable(self):
-        with self.client.websocket_connect("/v1/stream") as socket:
-            socket.receive_json()  # hello
-            socket.send_bytes(b"\x00\x01")
-            error = socket.receive_json()
-            self.assertEqual(error["code"], "BAD_FRAME")
-            # the connection is still usable
-            socket.send_bytes(frame_payload(1))
-            self.assertEqual(socket.receive_json()["type"], "result")
-
-    def test_undecodable_frame_is_reported_and_keeps_the_socket(self):
-        with self.client.websocket_connect("/v1/stream") as socket:
-            socket.receive_json()
-            socket.send_bytes(frame_payload(5, b"not an image"))
-            error = socket.receive_json()
-            self.assertEqual(error["code"], "UNDECODABLE_IMAGE")
-            self.assertEqual(error["frame_id"], 5)
-            socket.send_bytes(frame_payload(6))
-            self.assertEqual(socket.receive_json()["frame_id"], 6)
-
-    def test_inspect_over_the_socket(self):
-        with self.client.websocket_connect("/v1/stream") as socket:
-            socket.receive_json()
-            socket.send_bytes(frame_payload(1))
-            result = socket.receive_json()
-            track_id = result["tracks"][0]["track_id"]
-            socket.send_json({"type": "inspect", "track_id": track_id, "request_id": "r1"})
-            reply = socket.receive_json()
-            self.assertEqual(reply["type"], "inspect_result")
-            self.assertEqual(reply["request_id"], "r1")
-            self.assertIn("identity", reply)
-
-            socket.send_json({"type": "inspect", "track_id": 999999, "request_id": "r2"})
-            missing = socket.receive_json()
-            self.assertEqual(missing["code"], "TRACK_NOT_IN_VIEW")
-
-    def crop_app(self):
-        """A shelf whose only facing is too small for the frame crop to decide."""
-        client, engine = make_app(detector=SmallBoxDetector())
-        self.addCleanup(engine.shutdown)
-        return client
-
-    def test_capabilities_turn_on_the_crop_lane(self):
-        with self.client.websocket_connect("/v1/stream") as socket:
-            hello = socket.receive_json()
-            self.assertIn("max_crop_bytes", hello["limits"])
-            socket.send_json({"type": "capabilities", "crop": True, "max_crop_bytes": 65536})
-            ack = socket.receive_json()
-            self.assertEqual(ack["type"], "capabilities_ack")
-            self.assertEqual(ack["capabilities"], {"crop": True, "max_crop_bytes": 65536})
-
-    def test_a_capability_the_server_cannot_honour_is_refused(self):
-        """A half-stated or malformed capability turns nothing on."""
-        for message in (
-            {"type": "capabilities", "max_crop_bytes": 65536},
-            {"type": "capabilities", "crop": False, "max_crop_bytes": 65536},
-            {"type": "capabilities", "crop": "true", "max_crop_bytes": 65536},
-            {"type": "capabilities", "crop": True},
-            {"type": "capabilities", "crop": True, "max_crop_bytes": 0},
-            {"type": "capabilities", "crop": True, "max_crop_bytes": -4096},
-            {"type": "capabilities", "crop": True, "max_crop_bytes": True},
-            {"type": "capabilities", "crop": True, "max_crop_bytes": "65536"},
-            {"type": "capabilities", "crop": True, "max_crop_bytes": 64 * 1024 * 1024},
-        ):
-            with self.subTest(capability=message):
-                with self.crop_app().websocket_connect("/v1/stream") as socket:
-                    socket.receive_json()  # hello
-                    socket.send_json(message)
-                    self.assertEqual(socket.receive_json()["code"], "BAD_MESSAGE")
-                    # A refused capability leaves this session on protocol v1: the
-                    # facing it cannot report is never asked about.
-                    for frame_id in (1, 2, 3):
-                        socket.send_bytes(frame_payload(frame_id))
-                        result = socket.receive_json()
-                    self.assertEqual(result["stats"]["crops"]["requested"], 0)
-                    for track in result["tracks"]:
-                        self.assertNotIn("crop_request", track)
-
-    def test_a_v1_client_is_never_asked_for_a_crop(self):
-        with self.crop_app().websocket_connect("/v1/stream") as socket:
-            socket.receive_json()  # hello
-            for frame_id in (1, 2, 3):
-                socket.send_bytes(frame_payload(frame_id))
-                result = socket.receive_json()
-            for track in result["tracks"]:
-                self.assertNotIn("crop_request", track)
-            self.assertEqual(result["stats"]["crops"]["requested"], 0)
-
-    def test_a_crop_round_trip_labels_the_track(self):
-        with self.crop_app().websocket_connect("/v1/stream") as socket:
-            socket.receive_json()  # hello
-            socket.send_json({"type": "capabilities", "crop": True, "max_crop_bytes": 131072})
-            self.assertEqual(socket.receive_json()["type"], "capabilities_ack")
-            socket.send_bytes(frame_payload(1))
-            socket.receive_json()
-            socket.send_bytes(frame_payload(2))
-            result = socket.receive_json()
-            request = result["tracks"][0].get("crop_request")
-            self.assertIsNotNone(request, "a facing too small for the frame crop must be asked about")
-            self.assertEqual(request["frame_id"], 2)
-
-            socket.send_json({
-                "type": "crop",
-                "request_id": request["request_id"],
-                "track_id": request["track_id"],
-                "frame_id": request["frame_id"],
-                "jpeg": base64.b64encode(jpeg_bytes(80, 120)).decode("ascii"),
-            })
-            self.assertEqual(socket.receive_json()["type"], "crop_ack")
-            socket.send_bytes(frame_payload(3))
-            settled = socket.receive_json()
-            self.assertEqual(settled["stats"]["crops"]["accepted"], 1)
-            identities = [t["identity"] for t in settled["tracks"]]
-            self.assertTrue(any(i["source"] == "crop" for i in identities))
-
-    def test_a_crop_that_does_not_match_its_request_is_refused(self):
-        with self.crop_app().websocket_connect("/v1/stream") as socket:
-            socket.receive_json()  # hello
-            socket.send_json({"type": "capabilities", "crop": True, "max_crop_bytes": 131072})
-            socket.receive_json()
-            socket.send_bytes(frame_payload(1))
-            socket.receive_json()
-            socket.send_bytes(frame_payload(2))
-            request = socket.receive_json()["tracks"][0]["crop_request"]
-
-            socket.send_json({
-                "type": "crop",
-                "request_id": request["request_id"],
-                "track_id": request["track_id"],
-                "frame_id": 4242,
-                "jpeg": base64.b64encode(jpeg_bytes(80, 120)).decode("ascii"),
-            })
-            refused = socket.receive_json()
-            self.assertEqual(refused["type"], "crop_error")
-            self.assertEqual(refused["code"], "CROP_MISMATCH")
-            socket.send_bytes(frame_payload(3))
-            self.assertEqual(socket.receive_json()["stats"]["crops"]["accepted"], 0)
-
-    def test_a_crop_that_is_not_an_image_is_refused(self):
-        with self.crop_app().websocket_connect("/v1/stream") as socket:
-            socket.receive_json()  # hello
-            socket.send_json({"type": "capabilities", "crop": True, "max_crop_bytes": 131072})
-            socket.receive_json()
-            socket.send_bytes(frame_payload(1))
-            socket.receive_json()
-            socket.send_bytes(frame_payload(2))
-            request = socket.receive_json()["tracks"][0]["crop_request"]
-
-            socket.send_json({
-                "type": "crop",
-                "request_id": request["request_id"],
-                "track_id": request["track_id"],
-                "frame_id": request["frame_id"],
-                "jpeg": base64.b64encode(b"not an image").decode("ascii"),
-            })
-            self.assertEqual(socket.receive_json()["code"], "CROP_UNDECODABLE")
-
-    def test_a_small_jpeg_claiming_huge_pixels_is_refused_before_decode(self):
-        with self.crop_app().websocket_connect("/v1/stream") as socket:
-            socket.receive_json()
-            socket.send_json({"type": "capabilities", "crop": True, "max_crop_bytes": 131072})
-            socket.receive_json()
-            socket.send_bytes(frame_payload(1))
-            socket.receive_json()
-            socket.send_bytes(frame_payload(2))
-            request = socket.receive_json()["tracks"][0]["crop_request"]
-            jpeg = bytearray(jpeg_bytes(80, 120))
-            sof = jpeg.index(b"\xff\xc0")
-            jpeg[sof + 5:sof + 9] = (16384).to_bytes(2, "big") * 2
-            socket.send_json({
-                "type": "crop", "request_id": request["request_id"],
-                "track_id": request["track_id"], "frame_id": request["frame_id"],
-                "jpeg": base64.b64encode(jpeg).decode("ascii"),
-            })
-            refused = socket.receive_json()
-            self.assertEqual(refused["code"], "CROP_TOO_LARGE")
-            socket.send_json({"type": "ping", "t": 1})
-            self.assertEqual(socket.receive_json(), {"type": "pong", "t": 1})
-
-    def test_a_crop_without_an_image_is_refused(self):
-        with self.client.websocket_connect("/v1/stream") as socket:
-            socket.receive_json()  # hello
-            socket.send_json({"type": "crop", "request_id": "c1", "track_id": 1, "frame_id": 1})
-            refused = socket.receive_json()
-            self.assertEqual(refused["code"], "CROP_BAD_MESSAGE")
-            socket.send_json({"type": "ping", "t": 1})
-            self.assertEqual(socket.receive_json(), {"type": "pong", "t": 1})
-
-    def test_token_is_enforced_on_the_socket(self):
-        """Header or the first post-hello auth frame. A query token is rejected."""
-        client, engine = make_app(token="s3cret")
-        try:
-            with self.assertRaises(Exception):
-                with client.websocket_connect("/v1/stream") as socket:
-                    self.assertEqual(socket.receive_json()["type"], "hello")
-                    socket.send_json({"type": "ping", "t": 1})
-                    socket.receive_json()
-            with self.assertRaises(Exception):
-                with client.websocket_connect("/v1/stream?token=s3cret") as socket:
-                    socket.receive_json()
-            with client.websocket_connect(
-                "/v1/stream", headers={"Authorization": "Bearer s3cret"}
-            ) as socket:
-                self.assertEqual(socket.receive_json()["type"], "hello")
-            with client.websocket_connect("/v1/stream") as socket:
-                self.assertEqual(socket.receive_json()["type"], "hello")
-                socket.send_json({"type": "auth", "token": "s3cret"})
-                socket.send_json({"type": "ping", "t": 1})
-                self.assertEqual(socket.receive_json(), {"type": "pong", "t": 1})
-        finally:
-            engine.shutdown()
-
-    def test_session_limit_closes_extra_clients(self):
-        client, engine = make_app(max_sessions=1)
-        try:
-            with client.websocket_connect("/v1/stream") as first:
-                first.receive_json()
-                with self.assertRaises(Exception):
-                    with client.websocket_connect("/v1/stream") as second:
-                        second.receive_json()
-        finally:
-            engine.shutdown()
-
-    def test_lifespan_shutdown_skips_a_reserved_slot(self):
-        """A reservation is None until setup finishes. Shutdown must not call it."""
-        client, engine = make_app()
-        client.app.state.sessions["reserved"] = None
-        try:
-            with client:
-                pass
-        finally:
-            engine.shutdown()
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/recognization/code/test_empty.py b/recognization/code/test_empty.py
new file mode 100644
index 0000000..0781650
--- /dev/null
+++ b/recognization/code/test_empty.py
@@ -0,0 +1,20 @@
+import sys
+import os
+
+current_dir = os.path.dirname(os.path.abspath(__file__))
+if current_dir not in sys.path:
+    sys.path.insert(0, current_dir)
+
+from empty_shelf_detector import detect_and_draw_empty_slots
+
+if __name__ == "__main__":
+    image_path = "image.jpg"
+    print(f"Processing empty shelf detection on {image_path}...")
+    annotated, empty_slots = detect_and_draw_empty_slots(
+        image_path,
+        output_path="detected_empty_slots.jpg"
+    )
+    print(f"Detected {len(empty_slots)} empty shelf slots:")
+    for s in empty_slots:
+        print(f"  - Slot [{s['id']}]: Row {s['row_index']}, Box={s['box']}, Width={s['width']}px, Est. Missing Items={s['missing_items_est']}")
+    print(f"Visualization saved to: detected_empty_slots.jpg")
diff --git a/recognization/code/test_engine.py b/recognization/code/test_engine.py
deleted file mode 100644
index 96e45de..0000000
--- a/recognization/code/test_engine.py
+++ /dev/null
@@ -1,861 +0,0 @@
-"""
-Contract tests for the recognition engine.
-
-No YOLO weights and no network: a fake detector and a fake matcher stand in for
-the model, so these tests defend the engine's own contracts - normalization,
-locking, isolation between clients, empty-slot geometry, and the still-image
-store.
-"""
-
-from __future__ import annotations
-
-import os
-import sys
-import threading
-import time
-import unittest
-
-current_dir = os.path.dirname(os.path.abspath(__file__))
-if current_dir not in sys.path:
-    sys.path.insert(0, current_dir)
-
-from engine import (  # noqa: E402
-    EngineSettings,
-    ImageExpired,
-    ImageStore,
-    RecognitionEngine,
-    UndecodableImage,
-    UnknownMethod,
-    identity_payload,
-)
-
-
-class TextLaneSignatureTests(unittest.TestCase):
-    """The lane must score a crop against the row's name, not its packshot.
-
-    l09's packshot prints "マヨネーズ", which is l10's actual name. Scoring the
-    packshot's OCR as part of l09's signature let l09 explain an l10 crop's
-    text better than l10's own name could, so l09 won l10's facings. Measured
-    on image6: l10 correct went 2 -> 5 of 11, wrong 3 -> 1, precision 95% ->
-    97%, and the l09 hub alarm cleared.
-    """
-
-    ROWS = [
-        {"sku_id": "sku_l09", "product_name": "ピュアセレクト コクうま カロリーカット65% 360g"},
-        {"sku_id": "sku_l10", "product_name": "ピュアセレクト マヨネーズ 450g"},
-        {"sku_id": "sku_l03", "product_name": "キューピー からしマヨネーズ 200g"},
-    ]
-
-    def lane(self):
-        from identity_lanes import TextLane
-
-        return TextLane.from_catalog(self.ROWS, "no-such-dir", ocr_engine=None)
-
-    def test_a_read_of_the_plain_mayonnaise_names_the_plain_mayonnaise(self):
-        scores = self.lane().scores_from_text(
-            "AJINOMOTO とれて3日以内の 国産新鮮たまご ピュア セレクト マヨネーズ"
-        )
-        self.assertEqual(max(scores, key=scores.get), "sku_l10")
-
-    def test_a_read_of_the_variant_names_the_variant(self):
-        scores = self.lane().scores_from_text("ピュアセレクト コクうま カロリーカット65%")
-        self.assertEqual(max(scores, key=scores.get), "sku_l09")
-
-    def test_shared_brand_copy_alone_does_not_name_a_row(self):
-        """Every row prints マヨネーズ, so it must not decide between them.
-
-        The lane scores l10 and l03 equally on that word alone; the policy's
-        margin gate is what turns the tie into no answer.
-        """
-        from identity_lanes import DecisionPolicy
-
-        scores = self.lane().scores_from_text("マヨネーズ")
-        self.assertEqual(scores["sku_l10"], scores["sku_l03"])
-        decision = DecisionPolicy().decide([], scores, text_read="マヨネーズ")
-        self.assertIsNone(decision["best_sku"])
-
-    def test_a_signature_never_pairs_across_two_words(self):
-        """A catalog name must not claim text it does not contain.
-
-        "キューピー アマニ油" normalizes to "キユピ アマニ油"; with the space
-        dropped it was "キユピアマニ油", which contains "ピア" - a pair neither
-        word has. That pair is rare across the catalog, so IDF weighted it as
-        distinctive and the garbage read "キユピアク5C" was accepted as l02.
-        """
-        from rebuild_catalog import signature_bigrams
-
-        name = "キューピー アマニ油マヨネーズ 200g"
-        self.assertNotIn("ピア", signature_bigrams(name))
-        self.assertIn("アマ", signature_bigrams(name))
-
-    def test_a_read_keeps_matching_across_the_spaces_ocr_inserts(self):
-        """The read is the noisy side, so its spaces must not break a match.
-
-        Vision prints "ピュア セレクト" where the catalog name has
-        "ピュアセレクト". Refusing to pair across the read's spaces dropped
-        two correct l10 labels below the accept line, so the read pairs
-        across them and the signature is the side that stays strict.
-        """
-        from rebuild_catalog import bigrams
-
-        self.assertIn("アセ", bigrams("ピュア セレクト マヨネーズ"))
-
-    def test_a_garbage_read_does_not_name_a_row(self):
-        """The read that the fake pair used to accept must not name a row.
-
-        It shares only "キユ" and "ユピ" with l02's name, both of which every
-        Kewpie row prints. Bounded to the catalog's own vocabulary the read
-        keeps just those two pairs, so it scores the same against every
-        Kewpie row and the margin gate refuses it - the same mechanism that
-        already handles a read of shared brand copy alone.
-        """
-        from identity_lanes import DecisionPolicy, TextLane
-
-        rows = self.ROWS + [{"sku_id": "sku_l02", "product_name": "キューピー アマニ油マヨネーズ 200g"}]
-        read = "キユピアク5C"
-        scores = TextLane.from_catalog(rows, "no-such-dir", ocr_engine=None).scores_from_text(read)
-        ranked = sorted(scores.items(), key=lambda kv: kv[1], reverse=True)
-        self.assertEqual(ranked[0][1], ranked[1][1], "the read cannot tell the Kewpie rows apart")
-        decision = DecisionPolicy().decide([], ranked, text_read=read)
-        self.assertIsNone(decision["best_sku"])
-
-    def test_a_read_of_real_text_still_names_its_row(self):
-        """Bounding the denominator must not cost a correct read its answer.
-
-        This is the facing the strict-read variant broke: l10's own name read
-        with OCR's spacing, which has to clear the accept line by margin.
-        """
-        from identity_lanes import TEXT_ACCEPT, TEXT_MARGIN, TextLane
-
-        rows = self.ROWS + [{"sku_id": "sku_l02", "product_name": "キューピー アマニ油マヨネーズ 200g"}]
-        scores = TextLane.from_catalog(rows, "no-such-dir", ocr_engine=None).scores_from_text(
-            "213日以月A ピュア セレクト マヨネーズ"
-        )
-        ranked = sorted(scores.items(), key=lambda kv: kv[1], reverse=True)
-        self.assertEqual(ranked[0][0], "sku_l10")
-        self.assertGreaterEqual(ranked[0][1], TEXT_ACCEPT)
-        self.assertGreaterEqual(ranked[0][1] - ranked[1][1], TEXT_MARGIN)
-
-    def test_a_thin_read_is_not_enough_to_name_a_row(self):
-        """A ratio of 1.0 on a few pairs is not evidence.
-
-        "XXイス" contains exactly one pair the catalog knows, and the row that
-        contains it scores 1.0 with a full margin over every other row. On
-        image6 that shape named sku_l08 from the read "サソース". The floor asks
-        how much of the read the row actually explains, so one pair and two
-        pairs are both refused while a real read of the name is accepted.
-
-        Two pairs used to be enough. It is not: on 2297 crops from unrelated
-        Wikimedia shelves, 20 of 26 text-lane answers rested on exactly two
-        shared bigrams - "避くて おいしい" named sku_l12 through おい/いし - for
-        an 11.4% false accept rate. The floor is 4.
-        """
-        from identity_lanes import DecisionPolicy, TextLane
-
-        rows = [
-            {"sku_id": "sku_l08", "product_name": "オイスターソース"},
-            {"sku_id": "sku_l01", "product_name": "キューピー マヨネーズ"},
-        ]
-        lane = TextLane.from_catalog(rows, "no-such-dir", ocr_engine=None)
-
-        for thin_read in ("XXイス", "ZZソース"):
-            thin = sorted(lane.scores_from_text(thin_read).items(),
-                          key=lambda kv: kv[1], reverse=True)
-            self.assertEqual(thin[0][1], 1.0, "the ratio alone cannot see the difference")
-            self.assertIsNone(
-                DecisionPolicy(signatures=lane.signatures).decide(
-                    [], thin, text_read=thin_read
-                )["best_sku"],
-                f"{thin_read!r} shares too few pairs to name a row",
-            )
-
-        enough = sorted(lane.scores_from_text("オイスター").items(),
-                        key=lambda kv: kv[1], reverse=True)
-        self.assertEqual(
-            DecisionPolicy(signatures=lane.signatures).decide(
-                [], enough, text_read="オイスター"
-            )["best_sku"],
-            "sku_l08",
-        )
-
-    def test_one_shared_pair_is_refused_by_the_floor_alone(self):
-        """Everything but the evidence floor says answer, and it says no.
-
-        "アンース" holds one pair the catalog knows - "ース" - so the row scores
-        a perfect 1.0 with the whole margin over an empty runner-up, and the
-        read carries three pairs, so `carries_identity` passes as well. Only
-        `TEXT_MIN_SHARED` stands between a coincidental pair and a named
-        product, which is why the test asserts the score is 1.0: it pins the
-        floor as the only thing doing the refusing.
-        """
-        from identity_lanes import DecisionPolicy, TextLane
-
-        lane = TextLane.from_catalog(
-            [{"sku_id": "sku_x", "product_name": "オイスターソース"}],
-            "no-such-dir",
-            ocr_engine=None,
-        )
-        read = "アンース"
-        self.assertEqual(lane.scores_from_text(read)["sku_x"], 1.0)
-        self.assertIsNone(
-            DecisionPolicy(signatures=lane.signatures).decide(
-                [], [("sku_x", 1.0)], text_read=read
-            )["best_sku"],
-            "one shared pair is not enough to name a row",
-        )
-
-    def test_a_thin_read_still_vetoes_even_though_it_cannot_name(self):
-        """The floor is a floor on naming, not on contradicting.
-
-        "北海道塩" shares three bigrams with sku_b - one short of the floor -
-        so it cannot name that row. It is still the row the text prefers over
-        the local answer, and the local lane clears its own line at 5.0 against
-        a line of 4.0, so if the floor also sat under the veto this would come
-        back as sku_a: a three-bigram read would have rescued a match the
-        geometry earned.
-        """
-        from identity_lanes import DecisionPolicy, TextLane
-
-        lane = TextLane.from_catalog(
-            [
-                {"sku_id": "sku_a", "product_name": "キューピー ハーフマヨネーズ 400g"},
-                {"sku_id": "sku_b", "product_name": "北海道塩"},
-            ],
-            "no-such-dir",
-            ocr_engine=None,
-        )
-        read = "北海道塩"
-        ranked = sorted(lane.scores_from_text(read).items(),
-                        key=lambda kv: kv[1], reverse=True)
-        self.assertEqual((ranked[0][0], ranked[0][1]), ("sku_b", 1.0))
-        self.assertIsNone(
-            DecisionPolicy(signatures=lane.signatures).decide(
-                [("sku_a", 5.0)], ranked, text_read=read
-            )["best_sku"],
-            "a read too thin to name a row still refuses the local answer",
-        )
-
-
-class FakeDetector:
-    """Records concurrency and returns fixed boxes."""
-
-    weights_path = "fake.pt"
-
-    def __init__(self, boxes=None):
-        self.boxes = boxes if boxes is not None else [
-            {"id": 0, "box": [10, 10, 60, 90], "confidence": 0.9, "class_id": 0,
-             "width": 50, "height": 80, "center": [35, 50]},
-            {"id": 1, "box": [70, 10, 120, 90], "confidence": 0.8, "class_id": 0,
-             "width": 50, "height": 80, "center": [95, 50]},
-        ]
-        self.concurrent = 0
-        self.max_concurrent = 0
-        self.calls = 0
-        self.lock = threading.Lock()
-
-    def detect(self, image, conf_threshold=0.25, iou_threshold=0.45):
-        with self.lock:
-            self.calls += 1
-            self.concurrent += 1
-            self.max_concurrent = max(self.max_concurrent, self.concurrent)
-        time.sleep(0.01)
-        with self.lock:
-            self.concurrent -= 1
-        return {"status": "success", "boxes": [dict(b) for b in self.boxes]}
-
-    def detect_empty_slots(self, image_shape, boxes, min_gap_ratio=0.75):
-        # One gap between the two boxes, expressed in the frame the engine hands
-        # over. Scaling with that frame is what makes the normalization test
-        # meaningful: the engine must translate the answer back to the client's
-        # pixel space.
-        height, width = int(image_shape[0]), int(image_shape[1])
-        x1, x2 = int(width * 0.30), int(width * 0.35)
-        return [{
-            "id": "empty_1",
-            "box": [x1, int(height * 0.10), x2, int(height * 0.90)],
-            "width": x2 - x1,
-            "height": int(height * 0.80),
-            "missing_items_est": 1,
-            "center": [(x1 + x2) // 2, height // 2],
-            "row_index": 1,
-        }]
-
-
-class FakeMatcher:
-    def __init__(self):
-        self.calls = 0
-
-    def score_crop(self, crop, method="hybrid"):
-        self.calls += 1
-        time.sleep(0.01)
-        return {"sku_a": 40.0, "sku_b": 2.0}, {"sku_a": {"score": 40.0}}
-
-    def score_crop_fast(self, crop, shortlist=5):
-        return self.score_crop(crop)
-
-    def classify_crop(self, crop, method="hybrid"):
-        from identity import decide_identity
-
-        scores, details = self.score_crop(crop, method=method)
-        return decide_identity(scores, details=details, method=method)
-
-    def generate_match_visualization(self, crop, sku, method="surf", max_matches=30):
-        return crop
-
-
-class FakeInventory:
-    def __init__(self):
-        self.products = {
-            "sku_a": {"sku_id": "sku_a", "product_name": "Alpha", "price": 100.0,
-                      "number_in": 10, "number_out": 2, "number_on": 8,
-                      "receipt_date": "2026-01-01", "expiration_date": "2026-12-31",
-                      "local_image_path": None},
-            "sku_b": {"sku_id": "sku_b", "product_name": "Beta", "price": 200.0,
-                      "number_in": 5, "number_out": 1, "number_on": 4,
-                      "receipt_date": "2026-01-02", "expiration_date": "2026-12-30",
-                      "local_image_path": None},
-        }
-        self.lock = threading.Lock()
-
-    def list_all_skus(self):
-        return list(self.products)
-
-    def get_product(self, sku_id):
-        return self.products.get(sku_id)
-
-    def update_stock(self, sku_id, delta):
-        with self.lock:
-            product = self.products.get(sku_id)
-            if product is None:
-                return False
-            product["number_on"] = max(0, product["number_on"] + delta)
-            if delta < 0:
-                product["number_out"] += abs(delta)
-            else:
-                product["number_in"] += delta
-            return True
-
-
-def make_engine(**overrides):
-    # The fake matcher is what these tests exercise, so the backend is pinned to
-    # `matcher`; the shipped default is the `local` lane, which reads real
-    # reference images and would ignore the fake.
-    overrides.setdefault("identity_backend", "matcher")
-    settings = EngineSettings(device="cpu", classify_workers=4, **overrides)
-    detector = FakeDetector()
-    matcher = FakeMatcher()
-    inventory = FakeInventory()
-    engine = RecognitionEngine(detector, matcher, inventory, settings=settings)
-    return engine, detector, matcher, inventory
-
-
-def make_image(width=200, height=120):
-    import numpy as np
-
-    return np.zeros((height, width, 3), dtype="uint8")
-
-
-class EngineBasicsTests(unittest.TestCase):
-    def test_detect_normalizes_and_reindexes(self):
-        engine, _, _, _ = make_engine()
-        boxes = engine.detect(make_image())
-        self.assertEqual(len(boxes), 2)
-        self.assertEqual([b["id"] for b in boxes], [0, 1])
-        self.assertEqual(boxes[0]["width"], 50)
-        self.assertEqual(boxes[0]["center"], [35, 50])
-
-    def test_detector_calls_never_overlap(self):
-        engine, detector, _, _ = make_engine()
-        threads = [threading.Thread(target=engine.detect, args=(make_image(),)) for _ in range(8)]
-        for thread in threads:
-            thread.start()
-        for thread in threads:
-            thread.join()
-        self.assertEqual(detector.calls, 8)
-        self.assertEqual(detector.max_concurrent, 1, "the model must be used by one caller at a time")
-
-    def test_unknown_method_is_rejected(self):
-        engine, _, _, _ = make_engine()
-        with self.assertRaises(UnknownMethod):
-            engine.score_crop(make_image(), method="not-a-method")
-
-    def test_undecodable_payload_raises(self):
-        engine, _, _, _ = make_engine()
-        with self.assertRaises(UndecodableImage):
-            engine.decode_image(b"this is not an image")
-
-    def test_crop_is_a_copy(self):
-        engine, _, _, _ = make_engine()
-        image = make_image()
-        crop = engine.crop(image, [0, 0, 10, 10])
-        crop[0, 0, 0] = 255
-        self.assertEqual(int(image[0, 0, 0]), 0)
-
-
-class EngineClassificationTests(unittest.TestCase):
-    def test_classify_boxes_accepts_detection_dicts_without_size_fields(self):
-        """The API hands classify_boxes stored detections, which carry no width/height."""
-        engine, _, _, _ = make_engine()
-        detections = [
-            {"id": 0, "box": [10, 10, 60, 90], "confidence": 0.9, "identity": identity_payload(None)},
-            {"id": 1, "box": [70, 10, 120, 90], "confidence": 0.8, "identity": identity_payload(None)},
-        ]
-        rows = engine.classify_boxes(make_image(), detections, method="hybrid")
-        self.assertEqual(len(rows), 2)
-        self.assertEqual(rows[0]["identity"]["state"], "recognized")
-        self.assertEqual(rows[0]["identity"]["sku_id"], "sku_a")
-
-    def test_an_accepted_detection_carries_its_catalog_row(self):
-        """The phone must not have to look up a name and price from a bare SKU.
-
-        `classify_boxes` returned only the identity, so every accepted
-        still-image answer reached the client as an id with no product row:
-        measured through the real server, 41 of 41 accepted detections had no
-        `product`. The live path attached it; the still path did not.
-        """
-        engine, _, _, _ = make_engine()
-        detections = [
-            {"id": 0, "box": [10, 10, 60, 90], "confidence": 0.9,
-             "identity": identity_payload(None)},
-        ]
-        rows = engine.classify_boxes(make_image(), detections, method="hybrid")
-        self.assertEqual(rows[0]["identity"]["state"], "recognized")
-        self.assertEqual(rows[0]["product"]["product_name"], "Alpha")
-        self.assertEqual(rows[0]["product"]["price"], 100.0)
-
-        result = engine.recognize(make_image(), method="hybrid")
-        accepted = [
-            row for row in result["detections"]
-            if row["identity"]["identity_level"] == "accepted"
-        ]
-        self.assertTrue(accepted, "the fake detector and matcher accept a box")
-        for row in accepted:
-            self.assertIn("product", row, "an accepted answer must carry its row")
-
-    def test_a_failing_crop_reports_unrecognized_not_pending(self):
-        """One bad crop must not fail the batch, and must not read as pending.
-
-        `identity_payload(None)` is the "not yet classified" shape; an
-        operational failure has to stay distinguishable from absence.
-        """
-        engine, _, matcher, _ = make_engine()
-
-        def explode(crop, method=None):
-            raise RuntimeError("classifier blew up")
-
-        matcher.score_crop = explode
-        rows = engine.classify_boxes(make_image(), [
-            {"id": 0, "box": [10, 10, 60, 90], "confidence": 0.9},
-        ], method="hybrid")
-        self.assertEqual(len(rows), 1)
-        self.assertEqual(rows[0]["identity"]["state"], "unrecognized")
-        self.assertIsNone(rows[0]["identity"]["sku_id"])
-
-    def test_parallel_classification_matches_sequential(self):
-        engine, _, matcher, _ = make_engine()
-        boxes = [
-            {"id": index, "box": [10 + index, 10, 60 + index, 90], "confidence": 0.9,
-             "width": 50, "height": 80, "center": [35, 50]}
-            for index in range(12)
-        ]
-        parallel = engine.classify_boxes(make_image(), boxes, method="hybrid")
-        sequential = [engine.score_crop(engine.crop(make_image(), box["box"]), "hybrid") for box in boxes]
-        self.assertEqual(len(parallel), 12)
-        self.assertEqual(len(sequential), 12)
-        for row in parallel:
-            self.assertEqual(row["identity"]["sku_id"], "sku_a")
-
-    def test_summary_counts_states(self):
-        engine, _, _, _ = make_engine()
-        detections = [
-            {"identity": {"state": "recognized", "sku_id": "sku_a"}},
-            {"identity": {"state": "recognized", "sku_id": "sku_a"}},
-            {"identity": {"state": "needs_confirmation", "sku_id": None}},
-            {"identity": {"state": "unrecognized", "sku_id": None}},
-        ]
-        summary = engine.summarize(detections, [])
-        self.assertEqual(summary["recognized"], 2)
-        self.assertEqual(summary["needs_confirmation"], 1)
-        self.assertEqual(summary["unrecognized"], 1)
-        self.assertEqual(summary["by_sku"], [{"sku_id": "sku_a", "facings": 2}])
-
-
-class EmptySlotNormalizationTests(unittest.TestCase):
-    def test_slots_scale_with_the_frame(self):
-        """The detector's thresholds are absolute pixels, so a half-size frame must
-        produce the same slots (scaled) as the reference-size frame."""
-        engine, _, _, _ = make_engine()
-        boxes_small = [
-            {"box": [5, 5, 30, 45]},
-            {"box": [35, 5, 60, 45]},
-        ]
-        slots_small = engine.empty_slots((60, 100), boxes_small)
-        boxes_big = [
-            {"box": [10, 10, 60, 90]},
-            {"box": [70, 10, 120, 90]},
-        ]
-        slots_big = engine.empty_slots((120, 200), boxes_big)
-        self.assertEqual(len(slots_small), 1)
-        self.assertEqual(len(slots_big), 1)
-        # the small frame is scaled up to the reference width, so the returned
-        # slot comes back in the small frame's own pixels
-        self.assertLess(slots_small[0]["box"][2], slots_big[0]["box"][2])
-
-
-class ImageStoreTests(unittest.TestCase):
-    def test_roundtrip_and_expiry(self):
-        store = ImageStore(max_items=2, ttl_s=0.05, max_bytes=10_000_000)
-        stored = store.put(make_image())
-        self.assertEqual(store.get(stored.image_id).width, 200)
-        time.sleep(0.08)
-        with self.assertRaises(ImageExpired):
-            store.get(stored.image_id)
-
-    def test_bounded_by_count(self):
-        store = ImageStore(max_items=2, ttl_s=60, max_bytes=10_000_000)
-        ids = [store.put(make_image()).image_id for _ in range(4)]
-        with self.assertRaises(ImageExpired):
-            store.get(ids[0])
-        self.assertEqual(store.get(ids[-1]).width, 200)
-
-
-class CatalogTests(unittest.TestCase):
-    def test_catalog_and_stock_update(self):
-        engine, _, _, _ = make_engine()
-        catalog = engine.catalog()
-        self.assertEqual(len(catalog), 2)
-        self.assertEqual(catalog[0]["image_url"], "/v1/catalog/sku_a/image")
-        product = engine.update_stock("sku_a", -1)
-        self.assertEqual(product["number_on"], 7)
-        self.assertEqual(product["number_out"], 3)
-        self.assertIsNone(engine.update_stock("nope", -1))
-
-
-class GalleryLoadTests(unittest.TestCase):
-    def test_a_failed_gallery_load_returns_none_and_is_not_retried(self):
-        engine, _, _, _ = make_engine(identity_backend="gallery", stock_dir="does-not-exist")
-
-        class BrokenGallery:
-            def __init__(self, *args, **kwargs):
-                pass
-
-            def load(self):
-                raise FileNotFoundError("DINOv2 weights not found")
-
-        import gallery as gallery_module
-
-        original = gallery_module.ProductGallery
-        gallery_module.ProductGallery = BrokenGallery
-        try:
-            self.assertIsNone(engine.identity_gallery())
-            self.assertIsNone(engine.identity_gallery())
-        finally:
-            gallery_module.ProductGallery = original
-            engine.shutdown()
-        self.assertIs(engine._gallery, False)
-
-
-class LocalLaneTests(unittest.TestCase):
-    """The shipped default backend, which the fake matcher cannot exercise.
-
-    A two-SKU catalog of real images is built in a temp dir, so the lane reads
-    genuine reference files and the decision is the one production makes.
-    """
-
-    def make_catalog(self):
-        import tempfile
-
-        import cv2
-        import numpy as np
-
-        root = tempfile.mkdtemp(prefix="lane-catalog-")
-        rng = np.random.default_rng(7)
-        for sku, base in (("sku_a", (200, 60, 60)), ("sku_b", (60, 60, 200))):
-            image = np.zeros((400, 300, 3), dtype="uint8")
-            image[:, :] = base
-            # Structured, high-contrast marks: SIFT needs corners, not noise.
-            for index in range(12):
-                x = int(rng.integers(10, 280))
-                y = int(rng.integers(10, 380))
-                cv2.rectangle(image, (x, y), (x + 40, y + 40), (255, 255, 255), 3)
-                cv2.putText(image, sku[-1].upper(), (x + 5, y + 30),
-                            cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 0, 0), 2)
-            cv2.imwrite(os.path.join(root, f"{sku}.jpg"), image)
-        with open(os.path.join(root, "stock.csv"), "w", encoding="utf-8") as handle:
-            handle.write("SKU_ID,Product_Name,Price,Number_In,Number_Out,Number_On\n")
-            handle.write("sku_a,Alpha,100,1,0,1\n")
-            handle.write("sku_b,Beta,200,1,0,1\n")
-        return root
-
-    def test_decide_crop_names_the_matching_sku(self):
-        import cv2
-
-        root = self.make_catalog()
-        engine, _, _, _ = make_engine(identity_backend="local", stock_dir=root)
-        try:
-            self.assertEqual(engine.identity_scale, "local")
-            self.assertIsNotNone(engine.identity_lane())
-            # The reference itself is the easiest possible query for its own lane.
-            crop = cv2.imread(os.path.join(root, "sku_a.jpg"))
-            decision = engine.decide_crop(crop)
-            self.assertEqual(decision.get("best_sku"), "sku_a")
-            self.assertEqual(decision.get("identity_level"), "accepted")
-        finally:
-            engine.shutdown()
-
-    def test_score_crop_is_keyed_by_canonical_sku(self):
-        import cv2
-
-        root = self.make_catalog()
-        engine, _, _, _ = make_engine(identity_backend="local", stock_dir=root)
-        try:
-            scores = engine.score_crop(cv2.imread(os.path.join(root, "sku_b.jpg")))
-            self.assertTrue(scores)
-            # No view-suffixed ids: every key is a catalog SKU.
-            self.assertTrue(all("_" not in sku[4:] for sku in scores), scores)
-        finally:
-            engine.shutdown()
-
-    def test_a_featureless_crop_is_not_named(self):
-        import numpy as np
-
-        root = self.make_catalog()
-        engine, _, _, _ = make_engine(identity_backend="local", stock_dir=root)
-        try:
-            blank = np.zeros((200, 150, 3), dtype="uint8")
-            decision = engine.decide_crop(blank)
-            self.assertIsNone(decision.get("best_sku"))
-        finally:
-            engine.shutdown()
-
-    def test_warm_up_builds_both_lanes(self):
-        root = self.make_catalog()
-        engine, _, _, _ = make_engine(identity_backend="local", stock_dir=root)
-        try:
-            engine.warm_up()
-            # Built eagerly, so the first live frame never pays for them.
-            self.assertIsNotNone(engine._lane)
-            self.assertIsNotNone(engine._text_lane)
-        finally:
-            engine.shutdown()
-
-
-class DecisionPolicyTests(unittest.TestCase):
-    """The two jobs the text lane does, and the boundary between them."""
-
-    def policy(self):
-        from identity_lanes import DecisionPolicy
-
-        return DecisionPolicy()
-
-    def test_local_answer_stands_when_the_text_agrees(self):
-        decision = self.policy().decide([("sku_a", 5.0)], [("sku_a", 0.9)])
-        self.assertEqual(decision["best_sku"], "sku_a")
-        self.assertEqual(decision["source"], "local")
-
-    def test_text_vetoes_a_local_answer_naming_another_row(self):
-        """The near-unknown case: real geometry, different words.
-
-        The veto blocks the local answer, and the text lane then names its own
-        row - the two jobs compose, so the result is the text's SKU, never the
-        vetoed local one.
-        """
-        decision = self.policy().decide(
-            [("sku_a", 5.0)], [("sku_b", 0.9)], text_read="コクうま 65%"
-        )
-        self.assertEqual(decision["best_sku"], "sku_b")
-        self.assertEqual(decision["source"], "text")
-
-    def test_text_mentioning_the_local_candidate_is_not_a_veto(self):
-        """A crop reading "マヨネーズ" scores every mayonnaise row.
-
-        The local candidate is usually one of them, so a veto needs the text to
-        *prefer* another row, not merely to mention it: here the text mentions
-        sku_b but explains sku_a better, so the local answer stands.
-        """
-        decision = self.policy().decide([("sku_a", 5.0)], [("sku_b", 0.4), ("sku_a", 0.45)])
-        self.assertEqual(decision["best_sku"], "sku_a")
-        self.assertEqual(decision["source"], "local")
-
-    def test_the_lanes_own_order_wins_over_the_value_order(self):
-        """Order and value are different quantities on the local lane.
-
-        The order comes from `rank_key` (inliers weighted by hue); the value is
-        the margin over the runner-up. Re-sorting by value names a different SKU
-        than the lane chose - here sku_b has the larger value but sku_a is the
-        lane's own first choice, and sku_a is what must be answered.
-
-        Both values clear LOCAL_ACCEPT, so the test is about the ordering and
-        not about the gate.
-        """
-        decision = self.policy().decide([("sku_a", 4.5), ("sku_b", 5.0)], [])
-        self.assertEqual(decision["best_sku"], "sku_a")
-
-    def test_a_size_only_read_does_not_fill_in(self):
-        """Size and number tokens are not identity text."""
-        decision = self.policy().decide([], [("sku_b", 0.9)], text_read="400g")
-        self.assertIsNone(decision["best_sku"])
-
-    def test_a_size_only_read_still_vetoes(self):
-        """The veto keeps "65%" usable: the local lane supplied the evidence."""
-        decision = self.policy().decide(
-            [("sku_a", 5.0)], [("sku_b", 0.9)], text_read="65%"
-        )
-        self.assertIsNone(decision["best_sku"])
-
-    def test_a_read_printing_another_size_vetoes_the_local_answer(self):
-        """The documented near-unknown: a Kewpie 450 g bag the catalog lacks.
-
-        The crop reads "マヨネーズ 450g", which every mayonnaise row explains
-        equally, so the text ranking ties at 1.0 and the strict-greater veto
-        cannot fire. The size is what differs, and the catalog's Kewpie rows
-        are 200 g, so the read contradicts the row the local lane chose.
-        """
-        from identity_lanes import DecisionPolicy
-
-        policy = DecisionPolicy(signatures={"sku_a": "キューピー ハーフマヨネーズ 400g"})
-        decision = policy.decide(
-            [("sku_a", 5.0)], [("sku_a", 1.0)], text_read="ピーマヨネーズ 450g"
-        )
-        self.assertIsNone(decision["best_sku"])
-
-    def test_a_read_agreeing_on_size_does_not_veto(self):
-        """The same read with the row's own size must still answer."""
-        from identity_lanes import DecisionPolicy
-
-        policy = DecisionPolicy(signatures={"sku_a": "キューピー ハーフマヨネーズ 400g"})
-        decision = policy.decide(
-            [("sku_a", 5.0)], [("sku_a", 1.0)], text_read="ピーマヨネーズ 400g"
-        )
-        self.assertEqual(decision["best_sku"], "sku_a")
-
-    def test_ocr_noise_around_a_size_does_not_veto(self):
-        """A fragment or a run-together number is noise, not a package size.
-
-        Live reads of a correct 200 g row came back as "20" and "5210"; both
-        are outside the range a package of this class falls in, and vetoing on
-        either would cost a correct answer.
-        """
-        from identity_lanes import DecisionPolicy
-
-        policy = DecisionPolicy(signatures={"sku_a": "キューピー ハーフマヨネーズ 200g"})
-        for read in ("アマニ油 20gs", "内臓脂訪を 滅与ず ッデュービーフィ5 210g", "n0gベ% 50"):
-            decision = policy.decide([("sku_a", 5.0)], [("sku_a", 1.0)], text_read=read)
-            self.assertEqual(decision["best_sku"], "sku_a", read)
-
-    def test_a_percentage_is_not_a_package_size(self):
-        """A low-calorie claim must not read as a 50 g package."""
-        from identity_lanes import DecisionPolicy
-
-        policy = DecisionPolicy(signatures={"sku_a": "キューピー ハーフマヨネーズ 400g"})
-        decision = policy.decide(
-            [("sku_a", 5.0)], [("sku_a", 1.0)], text_read="カロリー 50% カット 400g"
-        )
-        self.assertEqual(decision["best_sku"], "sku_a")
-
-    def test_a_named_read_with_a_size_still_fills_in(self):
-        decision = self.policy().decide([], [("sku_b", 0.9)], text_read="コクうま 65%")
-        self.assertEqual(decision["best_sku"], "sku_b")
-
-    def test_text_fills_in_when_local_has_nothing(self):
-        decision = self.policy().decide([], [("sku_b", 0.9)], text_read="コクうま 65%")
-        self.assertEqual(decision["best_sku"], "sku_b")
-        self.assertEqual(decision["source"], "text")
-
-    def test_a_ranking_without_a_read_does_not_fill_in(self):
-        """Omitting the read is the safe default, not an ungated one.
-
-        A caller that has a ranking and no read has not shown the text can name
-        anything, so the fill-in job stays closed.
-        """
-        decision = self.policy().decide([], [("sku_b", 0.9)])
-        self.assertIsNone(decision["best_sku"])
-
-    def test_quantity_strings_are_not_names(self):
-        """Every pure quantity string, including the ones a digits-only rule let through.
-
-        "400g 50%" and "450g カロリー50%" survived the first version of this
-        gate: it counted non-digit bigrams, and the unit's own letters ("0G",
-        "0%") are non-digits. Cutting the whole quantity token is what closes
-        it. "450g カロリー50%" still passes, and correctly so - "カロリー" is
-        four characters of real text, and it is the text lane's job to find
-        that no catalog row is named by it.
-        """
-        from identity_lanes import carries_identity
-
-        for read in ("400g", "400g 50%", "500ml", "65%", "3日", "中辛"):
-            with self.subTest(read=read):
-                self.assertFalse(carries_identity(read), f"{read!r} is not a name")
-        for read in ("コクうま 65%", "450g カロリー50%"):
-            with self.subTest(read=read):
-                self.assertTrue(carries_identity(read), f"{read!r} carries name text")
-
-    def test_text_below_its_own_line_does_not_fill_in(self):
-        decision = self.policy().decide([], [("sku_b", 0.3)])
-        self.assertIsNone(decision["best_sku"])
-
-    def test_a_close_text_pair_does_not_fill_in(self):
-        """The margin gate: two rows the text explains equally must not name one."""
-        decision = self.policy().decide([], [("sku_b", 0.9), ("sku_c", 0.85)])
-        self.assertIsNone(decision["best_sku"])
-
-    def test_no_evidence_is_rejected(self):
-        decision = self.policy().decide([], [])
-        self.assertIsNone(decision["best_sku"])
-        self.assertEqual(decision["identity_level"], "rejected")
-
-
-class LocalLaneConcurrencyTests(unittest.TestCase):
-    """The lane is shared by the classify pool, so `rank` must be thread-safe.
-
-    One `cv2.BFMatcher` is reused across calls. The classify pool runs up to
-    eight workers, so a lane that mutated matcher state per call would return
-    another crop's answer under load - the failure would look like a
-    recognition error, not a crash.
-    """
-
-    def test_concurrent_ranking_matches_solo_ranking(self):
-        import threading
-
-        import cv2
-
-        from identity_lanes import LocalLane
-
-        stock_dir = os.path.join(os.path.dirname(current_dir), "stock-lopia-clean")
-        if not os.path.isdir(stock_dir):
-            self.skipTest("no catalog checked out")
-        image_path = os.path.join(os.path.dirname(current_dir), "add", "image6.jpg")
-        if not os.path.isfile(image_path):
-            self.skipTest("no evaluation photo checked out")
-        lane = LocalLane(stock_dir)
-        image = cv2.imread(image_path)
-        crops = [image[700:1000, 600 + 40 * i:800 + 40 * i] for i in range(8)]
-        solo = lane.rank(crops[0])
-        results = {}
-        errors = []
-
-        def work(index: int) -> None:
-            try:
-                for _ in range(6):
-                    results[index] = lane.rank(crops[index])
-            except Exception as error:  # noqa: BLE001 - reported below
-                errors.append(repr(error))
-
-        threads = [threading.Thread(target=work, args=(i,)) for i in range(8)]
-        for thread in threads:
-            thread.start()
-        for thread in threads:
-            thread.join()
-        self.assertEqual(errors, [])
-        self.assertEqual(results[0], solo, "a shared matcher must not cross crops")
-
-
-class CatalogImageTests(unittest.TestCase):
-    def test_catalog_image_path_is_resolved_through_inventory(self):
-        engine, _, _, _ = make_engine()
-        self.assertIsNone(engine.catalog_image_path("../../etc/passwd"))
-        self.assertIsNone(engine.catalog_image_path("sku_a"))
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/recognization/code/test_eval_labels.py b/recognization/code/test_eval_labels.py
deleted file mode 100644
index 1797c0d..0000000
--- a/recognization/code/test_eval_labels.py
+++ /dev/null
@@ -1,58 +0,0 @@
-"""
-The evaluator's label-file contract.
-
-F-01: the v1 labels were wrong on 23% of facings and the file recorded nothing
-about where any of them came from, so a bad label could not be told from a good
-one. `load_labels` now refuses a file whose rows carry no provenance.
-"""
-
-from __future__ import annotations
-
-import json
-import os
-import sys
-import tempfile
-import unittest
-
-current_dir = os.path.dirname(os.path.abspath(__file__))
-if current_dir not in sys.path:
-    sys.path.insert(0, current_dir)
-
-from eval_identity import load_labels  # noqa: E402
-
-
-def write_labels(payload) -> str:
-    handle = tempfile.NamedTemporaryFile("w", suffix=".json", delete=False)
-    json.dump(payload, handle)
-    handle.close()
-    return handle.name
-
-
-class LoadLabelsTests(unittest.TestCase):
-    def test_a_traced_label_file_loads(self):
-        path = write_labels({
-            "image": "add/image6.jpg",
-            "facings": [{"index": 0, "label": "sku_l01", "source": "visual-audit"}],
-        })
-        self.addCleanup(os.unlink, path)
-        self.assertEqual(len(load_labels(path)["facings"]), 1)
-
-    def test_a_label_without_provenance_is_refused(self):
-        path = write_labels({
-            "image": "add/image6.jpg",
-            "facings": [{"index": 0, "label": "sku_l01"}],
-        })
-        self.addCleanup(os.unlink, path)
-        with self.assertRaises(SystemExit) as caught:
-            load_labels(path)
-        self.assertIn("source", str(caught.exception))
-
-    def test_a_file_without_facings_is_refused(self):
-        path = write_labels({"image": "add/image6.jpg"})
-        self.addCleanup(os.unlink, path)
-        with self.assertRaises(SystemExit):
-            load_labels(path)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/recognization/code/test_eval_live.py b/recognization/code/test_eval_live.py
deleted file mode 100644
index 9fc8a49..0000000
--- a/recognization/code/test_eval_live.py
+++ /dev/null
@@ -1,338 +0,0 @@
-"""Metric contracts for eval_live. No model, no real walk, no fabricated accuracy."""
-
-from __future__ import annotations
-
-import json
-import os
-import shutil
-import subprocess
-import sys
-import tempfile
-import unittest
-
-current_dir = os.path.dirname(os.path.abspath(__file__))
-if current_dir not in sys.path:
-    sys.path.insert(0, current_dir)
-
-import cv2  # noqa: E402
-import numpy as np  # noqa: E402
-
-from eval_live import (  # noqa: E402
-    IDENTITY_BACKENDS,
-    EvalInputError,
-    identity_level,
-    labelled_top1,
-    leaked_labels,
-    load_index,
-    recorded_identity,
-    resolve_identity,
-    score_recording,
-    score_tracks,
-    track_churn,
-)
-
-
-def track(level, sku=None, track_id=1, box=None, relabels=0, restarts=0, tag=None):
-    identity = {"identity_level": level, "state": "recognized" if level == "accepted" else "unrecognized"}
-    if level == "accepted":
-        identity["sku_id"] = sku
-    row = {
-        "track_id": track_id,
-        "box": box or [0, 0, 10, 10],
-        "identity": identity,
-        "relabels": relabels,
-        "restarts": restarts,
-    }
-    if tag is not None:
-        row["price_tag"] = tag
-    return row
-
-
-def write_jpeg(path, image):
-    ok, encoded = cv2.imencode(".jpg", image)
-    assert ok
-    with open(path, "wb") as handle:
-        handle.write(encoded.tobytes())
-
-
-class DecisionDenominatorTests(unittest.TestCase):
-    def test_settled_excludes_unsettled_and_rates_use_that_denominator(self):
-        scored = score_tracks(
-            [
-                track("accepted", "sku_a"),
-                track("ambiguous"),
-                track("rejected"),
-                track("unknown"),
-                {"identity": {"state": "unconfirmed", "identity_level": None}},
-            ]
-        )
-        self.assertEqual(scored["settled"], 4)
-        self.assertEqual(scored["unsettled"], 1)
-        self.assertEqual(scored["accepted_rate"], 0.25)
-        self.assertIsNone(score_tracks([{"identity": {"state": "unconfirmed"}}])["accepted_rate"])
-
-    def test_wire_state_fills_a_missing_level(self):
-        self.assertEqual(
-            identity_level({"identity": {"state": "needs_confirmation"}}), "ambiguous"
-        )
-        self.assertEqual(identity_level({"identity": {"state": "unconfirmed"}}), "unsettled")
-
-    def test_readable_tags_are_counted_apart_from_detected(self):
-        scored = score_tracks(
-            [
-                track("accepted", "sku_a", tag={"state": "readable"}),
-                track("rejected", tag={"state": "unreadable"}),
-                track("rejected", tag={"state": "absent"}),
-                track("rejected"),
-            ]
-        )
-        self.assertEqual(scored["tags_readable"], 1)
-        self.assertEqual(scored["tags_detected"], 2)
-        self.assertEqual(scored["tags_absent"], 2)
-
-
-class Top1AndChurnTests(unittest.TestCase):
-    def test_top1_counts_only_accepted_matches_over_overlapping_labels(self):
-        frames = [
-            {
-                "frame": 1,
-                "tracks": [
-                    track("accepted", "sku_a", box=[0, 0, 10, 10]),
-                    track("accepted", "sku_b", track_id=2, box=[100, 100, 110, 110]),
-                ],
-            },
-            {"frame": 2, "tracks": [track("rejected", box=[0, 0, 10, 10])]},
-            {"frame": 3, "tracks": []},
-        ]
-        labels = [
-            {"frame": 1, "box": [0, 0, 10, 10], "sku_id": "sku_a"},
-            {"frame": 1, "box": [100, 100, 110, 110], "sku_id": "sku_a"},
-            {"frame": 2, "box": [0, 0, 10, 10], "sku_id": "sku_a"},
-            {"frame": 3, "box": [0, 0, 10, 10], "sku_id": "sku_a"},
-        ]
-        scored = labelled_top1(frames, labels, iou_min=0.5)
-        self.assertEqual((scored["correct"], scored["wrong"], scored["rejected"], scored["unmatched"]), (1, 1, 1, 1))
-        self.assertEqual(scored["top1"], 0.3333)
-
-    def test_sku_churn_counts_a_renamed_track_not_the_first_name(self):
-        frames = [
-            {"tracks": [track("accepted", "sku_a", track_id=7)]},
-            {"tracks": [track("accepted", "sku_a", track_id=7)]},
-            {"tracks": [track("accepted", "sku_b", track_id=7, relabels=1)]},
-            {"tracks": [track("rejected", track_id=8, restarts=2)]},
-        ]
-        churn = track_churn(frames)
-        self.assertEqual(churn["sku_changes"], 1)
-        self.assertEqual(churn["tracks_with_sku_change"], 1)
-        self.assertEqual(churn["relabels"], 1)
-        self.assertEqual(churn["restarts"], 2)
-        self.assertEqual(churn["per_track"][0]["track_id"], 7)
-
-
-class LeakageTests(unittest.TestCase):
-    def setUp(self):
-        self.root = tempfile.mkdtemp()
-        self.addCleanup(shutil.rmtree, self.root, ignore_errors=True)
-
-    def test_a_pasted_catalog_crop_is_excluded_and_a_real_facing_is_not(self):
-        stock = os.path.join(self.root, "stock")
-        os.makedirs(stock)
-        template = np.full((20, 16, 3), 40, dtype=np.uint8)
-        template[2:8, 2:8] = (255, 0, 0)
-        write_jpeg(os.path.join(stock, "sku_a.jpeg"), template)
-        frame = np.zeros((40, 40, 3), dtype=np.uint8)
-        frame[5:25, 10:26] = template
-        recording = os.path.join(self.root, "walk")
-        os.makedirs(recording)
-        write_jpeg(os.path.join(recording, "000000.jpg"), frame)
-        with open(os.path.join(recording, "index.jsonl"), "w", encoding="utf-8") as handle:
-            handle.write(json.dumps({"frame": 0, "path": "000000.jpg", "tracks": []}) + "\n")
-        labels = [
-            {"frame": 0, "box": [10, 5, 26, 25], "sku_id": "sku_a"},
-            {"frame": 0, "box": [0, 0, 8, 8], "sku_id": "sku_b"},
-        ]
-        held, leaked = leaked_labels(recording, load_index(recording), labels, stock)
-        self.assertEqual(len(leaked), 1)
-        self.assertEqual(leaked[0]["leak"], "crop_is_template")
-        self.assertEqual([row["sku_id"] for row in held], ["sku_b"])
-
-    def test_score_recording_names_the_leak_and_refuses_it_as_top1(self):
-        stock = os.path.join(self.root, "stock")
-        os.makedirs(stock)
-        image = np.full((12, 12, 3), 9, dtype=np.uint8)
-        write_jpeg(os.path.join(stock, "sku_a.jpg"), image)
-        with open(os.path.join(stock, "stock.csv"), "w", encoding="utf-8") as handle:
-            handle.write("SKU_ID,product_name\nsku_a,A\n")
-        recording = os.path.join(self.root, "session")
-        os.makedirs(recording)
-        shutil.copy(os.path.join(stock, "sku_a.jpg"), os.path.join(recording, "000000.jpg"))
-        with open(os.path.join(recording, "index.jsonl"), "w", encoding="utf-8") as handle:
-            handle.write(
-                json.dumps(
-                    {
-                        "frame": 1,
-                        "path": "000000.jpg",
-                        "tracks": [track("accepted", "sku_a", box=[0, 0, 12, 12])],
-                    }
-                )
-                + "\n"
-            )
-        with open(os.path.join(recording, "labels.json"), "w", encoding="utf-8") as handle:
-            json.dump({"labels": [{"frame": 1, "box": [0, 0, 12, 12], "sku_id": "sku_a"}]}, handle)
-        report = score_recording(recording, stock_dir=stock, identity="matcher")
-        self.assertEqual(report["leakage"]["excluded"], 1)
-        self.assertEqual(report["labelled_top1"]["labels"], 0)
-        self.assertEqual(report["labelled_top1"]["top1"], None)
-        self.assertEqual(report["decisions"]["accepted"], 1)
-        self.assertTrue(report["fingerprints"]["sample"]["sha256"])
-        self.assertEqual(len(report["fingerprints"]["catalog"]["version"]), 12)
-        self.assertTrue(any("template-derived" in gap for gap in report["evidence"]))
-
-
-class CliTests(unittest.TestCase):
-    def test_missing_recording_is_an_explicit_error(self):
-        completed = subprocess.run(
-            [sys.executable, os.path.join(current_dir, "eval_live.py"), "/no/such/recording"],
-            capture_output=True,
-            text=True,
-            check=False,
-        )
-        self.assertEqual(completed.returncode, 2)
-        self.assertIn("error", json.loads(completed.stdout))
-
-    def test_the_same_pinned_recording_scores_twice_the_same(self):
-        root = tempfile.mkdtemp()
-        self.addCleanup(shutil.rmtree, root, ignore_errors=True)
-        stock = os.path.join(root, "stock")
-        os.makedirs(stock)
-        with open(os.path.join(stock, "stock.csv"), "w", encoding="utf-8") as handle:
-            handle.write("SKU_ID\nsku_a\n")
-        recording = os.path.join(root, "session")
-        os.makedirs(recording)
-        payload = np.zeros((8, 8, 3), dtype=np.uint8)
-        write_jpeg(os.path.join(recording, "000000.jpg"), payload)
-        line = {
-            "frame": 4,
-            "path": "000000.jpg",
-            "tracks": [
-                track("accepted", "sku_a", tag={"state": "readable"}),
-                track("ambiguous", track_id=2),
-            ],
-        }
-        with open(os.path.join(recording, "index.jsonl"), "w", encoding="utf-8") as handle:
-            handle.write(json.dumps(line) + "\n")
-        with open(os.path.join(recording, "labels.json"), "w", encoding="utf-8") as handle:
-            json.dump({"labels": [{"frame": 4, "box": [0, 0, 8, 8], "sku_id": "sku_a"}]}, handle)
-        first = score_recording(recording, stock_dir=stock, identity="matcher")
-        second = score_recording(recording, stock_dir=stock, identity="matcher")
-        self.assertEqual(first, second)
-        self.assertEqual(first["decisions"]["accepted"], 1)
-        self.assertEqual(first["decisions"]["ambiguous"], 1)
-        self.assertEqual(first["labelled_top1"]["correct"], 1)
-        self.assertIsNone(first["fingerprints"]["model"]["weights_sha256"])
-
-    def test_a_broken_index_line_is_an_input_error(self):
-        root = tempfile.mkdtemp()
-        self.addCleanup(shutil.rmtree, root, ignore_errors=True)
-        with open(os.path.join(root, "index.jsonl"), "w", encoding="utf-8") as handle:
-            handle.write("{not json}\n")
-        with self.assertRaises(EvalInputError):
-            load_index(root)
-
-
-class IdentityContractTests(unittest.TestCase):
-    def test_missing_gallery_weights_exit_without_a_matcher_score(self):
-        root = tempfile.mkdtemp()
-        self.addCleanup(shutil.rmtree, root, ignore_errors=True)
-        recording = os.path.join(root, "session")
-        os.makedirs(recording)
-        write_jpeg(os.path.join(recording, "000000.jpg"), np.zeros((8, 8, 3), dtype=np.uint8))
-        with open(os.path.join(recording, "index.jsonl"), "w", encoding="utf-8") as handle:
-            handle.write(json.dumps({"frame": 1, "path": "000000.jpg", "tracks": []}) + "\n")
-        missing = os.path.join(root, "no-such-dinov2.safetensors")
-        report_path = os.path.join(recording, "eval_live.json")
-        env = os.environ.copy()
-        env["DORAYAKI_DINOV2_WEIGHTS"] = missing
-        completed = subprocess.run(
-            [
-                sys.executable,
-                os.path.join(current_dir, "eval_live.py"),
-                recording,
-                "--replay",
-                "--identity",
-                "gallery",
-                "--json",
-                report_path,
-            ],
-            capture_output=True,
-            text=True,
-            check=False,
-            env=env,
-        )
-        self.assertEqual(completed.returncode, 2)
-        payload = json.loads(completed.stdout)
-        self.assertIn("DINOv2", payload["error"])
-        self.assertIn("matcher", payload["error"])
-        self.assertFalse(os.path.exists(report_path))
-        self.assertNotIn("accepted_rate", completed.stdout)
-
-    def test_a_silent_recording_does_not_become_the_matcher(self):
-        with self.assertRaises(EvalInputError):
-            resolve_identity(None, None)
-        self.assertEqual(resolve_identity("matcher", None), "matcher")
-        self.assertEqual(resolve_identity(None, "gallery"), "gallery")
-        with self.assertRaises(EvalInputError):
-            resolve_identity("matcher", "gallery")
-
-    def test_recorded_backend_is_read_and_a_mix_is_refused(self):
-        self.assertEqual(recorded_identity([{"identity_backend": "Gallery"}]), "gallery")
-        self.assertIsNone(recorded_identity([{"frame": 1}]))
-        self.assertEqual(
-            recorded_identity([{"identity": {"backend": "matcher", "sku_id": "sku_a"}}]),
-            "matcher",
-        )
-        with self.assertRaises(EvalInputError):
-            recorded_identity([
-                {"identity_backend": "gallery"},
-                {"identity_backend": "matcher"},
-            ])
-
-    def test_van_is_accepted_by_the_cli_and_by_a_recording_that_names_it(self):
-        self.assertIn("van", IDENTITY_BACKENDS)
-        self.assertEqual(resolve_identity("van", None), "van")
-        self.assertEqual(recorded_identity([{"identity_backend": "van"}]), "van")
-        root = tempfile.mkdtemp()
-        self.addCleanup(shutil.rmtree, root, ignore_errors=True)
-        stock = os.path.join(root, "stock")
-        os.makedirs(stock)
-        recording = os.path.join(root, "session")
-        os.makedirs(recording)
-        write_jpeg(os.path.join(recording, "000000.jpg"), np.zeros((8, 8, 3), dtype=np.uint8))
-        line = {"frame": 1, "path": "000000.jpg", "tracks": [], "identity_backend": "van"}
-        with open(os.path.join(recording, "index.jsonl"), "w", encoding="utf-8") as handle:
-            handle.write(json.dumps(line) + "\n")
-        for extra in ([], ["--identity", "van"]):
-            report_path = os.path.join(root, "eval_live.json")
-            completed = subprocess.run(
-                [
-                    sys.executable,
-                    os.path.join(current_dir, "eval_live.py"),
-                    recording,
-                    "--stock-dir",
-                    stock,
-                    "--json",
-                    report_path,
-                    *extra,
-                ],
-                capture_output=True,
-                text=True,
-                check=False,
-            )
-            self.assertEqual(completed.returncode, 0, completed.stdout + completed.stderr)
-            with open(report_path, "r", encoding="utf-8") as handle:
-                report = json.load(handle)
-            self.assertEqual(report["identity"]["backend"], "van")
-            self.assertEqual(report["fingerprints"]["model"]["identity"], "van")
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/recognization/code/test_gallery.py b/recognization/code/test_gallery.py
deleted file mode 100644
index 1154d29..0000000
--- a/recognization/code/test_gallery.py
+++ /dev/null
@@ -1,631 +0,0 @@
-"""
-Tests for the product gallery's matching logic.
-
-The embedding model is injected, so these run without torch or a GPU: what is
-being tested is the retrieval contract - best view wins, several views per SKU
-count as one answer, and a stranger is rejected rather than named.
-"""
-
-from __future__ import annotations
-
-import os
-import sys
-import unittest
-
-import numpy as np
-
-current_dir = os.path.dirname(os.path.abspath(__file__))
-project_root = os.path.dirname(current_dir)
-if os.path.exists(os.path.join(project_root, "stock")):
-    os.chdir(project_root)
-if current_dir not in sys.path:
-    sys.path.insert(0, current_dir)
-
-from gallery import (  # noqa: E402
-    DINOV2_MODEL,
-    DINOV2_SHA256,
-    GalleryEntry,
-    ProductGallery,
-    catalog_digest,
-    dinov2_weights_path,
-    head_class_set,
-    load_dinov2,
-)
-
-import gallery as gallery_module  # noqa: E402
-
-
-def a_gallery(views, threshold=0.55):
-    """A gallery with hand-made embeddings: no model, no files."""
-    skus = sorted({sku for sku, _ in views})
-    gallery = ProductGallery("does-not-exist", skus, threshold=threshold)
-    embeddings = []
-    entries = []
-    for index, (sku, vector) in enumerate(views):
-        vector = np.asarray(vector, dtype=np.float32)
-        vector /= np.linalg.norm(vector)
-        embeddings.append(vector)
-        entries.append(GalleryEntry(sku_id=sku, view=f"{sku}_{index}", embedding=vector))
-    gallery._entries = entries
-    gallery._matrix = np.stack(embeddings)
-    gallery._available = True
-    return gallery
-
-
-def unit(vector):
-    vector = np.asarray(vector, dtype=np.float32)
-    return (vector / np.linalg.norm(vector)).tolist()
-
-
-class Backbone:
-    """A deterministic stand-in for DINOv2: the prepared crop's channel means,
-    tiled to the width the real backbone returns."""
-
-    def __init__(self):
-        self.calls = 0
-
-    def __call__(self, tensor):
-        self.calls += 1
-        return tensor.mean(dim=(2, 3)).repeat(1, gallery_module.DINOV2_EMBED_DIM // 3)
-
-
-def a_query():
-    """A unit vector in the backbone's space, for match() without a model."""
-    query = np.zeros(gallery_module.DINOV2_EMBED_DIM, dtype=np.float32)
-    query[0] = 1.0
-    query[1] = 0.05
-    return query / np.linalg.norm(query)
-
-
-class UnreachableBackbone:
-    def __call__(self, tensor):
-        raise AssertionError("the gallery loaded a backbone it should have cached")
-
-
-def write_view(path, seed):
-    import cv2
-
-    image = np.random.RandomState(seed).randint(0, 256, (24, 32, 3), dtype=np.uint8)
-    cv2.imwrite(path, image)
-
-
-def a_store(directory, names=("sku_a", "sku_a_1", "sku_b")):
-    """A store dir: one JPEG per view plus the stock.csv the catalog is read from."""
-    with open(os.path.join(directory, "stock.csv"), "w", encoding="utf-8") as handle:
-        handle.write("SKU_ID,product_name\n")
-    for index, name in enumerate(names):
-        write_view(os.path.join(directory, f"{name}.jpeg"), index)
-
-
-def a_stored_gallery(directory, names, backbone, image_size=32):
-    gallery = ProductGallery(directory, list(names), device="cpu", image_size=image_size)
-    gallery._load_model = lambda: backbone
-    return gallery
-
-
-class MatchingTests(unittest.TestCase):
-    def test_the_nearest_view_names_the_product(self):
-        gallery = a_gallery([("sku_a", unit([1, 0, 0])), ("sku_b", unit([0, 1, 0]))])
-        gallery.embed = lambda crop: np.array([0.9, 0.1, 0.0], dtype=np.float32)
-        match = gallery.match(object())
-        self.assertFalse(match.rejected)
-        self.assertEqual(match.sku_id, "sku_a")
-        self.assertAlmostEqual(match.similarity, 0.9, places=3)
-
-    def test_several_views_of_one_sku_are_one_answer(self):
-        # The second view of sku_b is closer than any view of sku_a, but sku_a must
-        # still win on its best view: a SKU is not outvoted by having more photos.
-        gallery = a_gallery([
-            ("sku_a", unit([1, 0, 0])),
-            ("sku_b", unit([0, 1, 0])),
-            ("sku_b", unit([0.2, 0.98, 0])),
-        ])
-        gallery.embed = lambda crop: np.array([1.0, 0.05, 0.0], dtype=np.float32)
-        match = gallery.match(object())
-        self.assertEqual(match.sku_id, "sku_a")
-        self.assertEqual(match.runner_up, "sku_b")
-
-    def test_a_stranger_is_rejected_not_named(self):
-        gallery = a_gallery([("sku_a", unit([1, 0, 0])), ("sku_b", unit([0, 1, 0]))])
-        gallery.embed = lambda crop: np.array([0.0, 0.0, 1.0], dtype=np.float32)
-        match = gallery.match(object())
-        self.assertTrue(match.rejected)
-        self.assertIsNone(match.sku_id)
-
-    def test_the_threshold_moves_the_rejection_line(self):
-        gallery = a_gallery([("sku_a", unit([1, 0, 0]))], threshold=0.99)
-        gallery.embed = lambda crop: np.array([0.9, 0.4, 0.0], dtype=np.float32)
-        self.assertTrue(gallery.match(object()).rejected)
-        self.assertFalse(gallery.match(object(), threshold=0.5).rejected)
-
-    def test_margin_separates_a_confident_match_from_a_close_one(self):
-        gallery = a_gallery([("sku_a", unit([1, 0, 0])), ("sku_b", unit([0.9, 0.44, 0]))])
-        gallery.embed = lambda crop: np.array([1.0, 0.1, 0.0], dtype=np.float32)
-        confident = gallery.match(object())
-        gallery.embed = lambda crop: np.array([0.95, 0.3, 0.0], dtype=np.float32)
-        close = gallery.match(object())
-        self.assertGreater(confident.margin, close.margin)
-
-    def test_the_per_sku_max_does_not_depend_on_entry_order(self):
-        # Three SKUs, several views each, enrolled out of order: a SKU is scored
-        # by its best view, and the answer cannot move because a row moved.
-        gallery = a_gallery([
-            ("sku_b", unit([0, 1, 0])),
-            ("sku_a", unit([1, 0, 0])),
-            ("sku_c", unit([0, 0, 1])),
-            ("sku_b", unit([0.3, 0.95, 0])),
-            ("sku_a", unit([0.98, 0.2, 0])),
-        ])
-        gallery.embed = lambda crop: np.array([1.0, 0.05, 0.0], dtype=np.float32)
-        straight = gallery.match(object())
-        self.assertEqual(straight.sku_id, "sku_a")
-        self.assertEqual(straight.runner_up, "sku_b")
-
-        for order in ([4, 0, 3, 1, 2], [2, 3, 1, 4, 0]):
-            gallery._entries = [gallery._entries[index] for index in order]
-            gallery._matrix = gallery._matrix[order]
-            shuffled = gallery.match(object())
-            self.assertEqual(shuffled.sku_id, straight.sku_id)
-            self.assertEqual(shuffled.runner_up, straight.runner_up)
-            self.assertAlmostEqual(shuffled.similarity, straight.similarity, places=6)
-            self.assertAlmostEqual(shuffled.runner_up_similarity, straight.runner_up_similarity, places=6)
-
-    def test_a_tie_keeps_the_first_enrolled_sku(self):
-        # Exactly equidistant: first appearance decides, as it did before the
-        # per-SKU maximum became a segmented reduction.
-        gallery = a_gallery([("sku_a", unit([1, 0, 0])), ("sku_b", unit([0, 1, 0]))])
-        gallery.embed = lambda crop: np.array([1.0, 1.0, 0.0], dtype=np.float32)
-        match = gallery.match(object())
-        self.assertFalse(match.rejected)
-        self.assertEqual(match.sku_id, "sku_a")
-        self.assertEqual(match.runner_up, "sku_b")
-        self.assertAlmostEqual(match.similarity, match.runner_up_similarity, places=6)
-        self.assertAlmostEqual(match.margin, 0.0, places=6)
-
-    def test_an_unloaded_gallery_rejects_everything(self):
-        gallery = ProductGallery("does-not-exist", ["sku_a"])
-        match = gallery.match(object())
-        self.assertTrue(match.rejected)
-
-
-class EnrollmentTests(unittest.TestCase):
-    def test_views_are_found_by_sku_prefix(self):
-        import tempfile
-
-        with tempfile.TemporaryDirectory() as directory:
-            for name in ("sku_a.jpeg", "sku_a_1.jpeg", "sku_a-2.jpeg", "sku_ab.jpeg", "notes.txt"):
-                open(os.path.join(directory, name), "wb").close()
-            gallery = ProductGallery(directory, ["sku_a", "sku_ab"])
-            self.assertEqual(len(gallery._views_for("sku_a")), 3,
-                             "one view, two extra views, and not sku_ab")
-            self.assertEqual(len(gallery._views_for("sku_ab")), 1)
-
-
-class FeatureCacheTests(unittest.TestCase):
-    """The cached matrix must be this store's, or it must not be used."""
-
-    def setUp(self):
-        import hashlib
-        import tempfile
-
-        self.directory = tempfile.TemporaryDirectory()
-        self.addCleanup(self.directory.cleanup)
-        a_store(self.directory.name)
-        # A cache is only served when the pinned backbone is on disk, so point the
-        # pin at a stand-in file the module's expected digest is told about. The
-        # check itself - hash this file, compare - is what is under test.
-        self.weights = os.path.join(self.directory.name, "dinov2.safetensors")
-        self.write(self.weights, b"not really a checkpoint")
-        os.environ["DORAYAKI_DINOV2_WEIGHTS"] = self.weights
-        self.addCleanup(os.environ.pop, "DORAYAKI_DINOV2_WEIGHTS", None)
-        self.addCleanup(setattr, gallery_module, "DINOV2_SHA256", gallery_module.DINOV2_SHA256)
-        gallery_module.DINOV2_SHA256 = hashlib.sha256(b"not really a checkpoint").hexdigest()
-
-    def write(self, path, content):
-        with open(path, "wb") as handle:
-            handle.write(content)
-        return path
-
-    def real_gallery(self):
-        """A gallery with the real _load_model: the server's own readiness path."""
-        return ProductGallery(self.directory.name, ["sku_a", "sku_b"], device="cpu", image_size=32)
-
-    def loaded(self, names=("sku_a", "sku_b"), image_size=32):
-        backbone = Backbone()
-        gallery = a_stored_gallery(self.directory.name, names, backbone, image_size=image_size)
-        self.assertTrue(gallery.load())
-        return gallery, backbone
-
-    def cache_payload(self, gallery):
-        """The rows and matrix a loaded gallery left on disk."""
-        with np.load(gallery._cache_path(), allow_pickle=False) as payload:
-            return (
-                [str(value) for value in payload["skus"].tolist()],
-                [str(value) for value in payload["views"].tolist()],
-                payload["matrix"],
-            )
-
-    def rewrite_cache(self, gallery, skus, views, matrix):
-        """The same valid key with different contents: only the rows can catch it."""
-        path = gallery._cache_path()
-        with np.load(path, allow_pickle=False) as payload:
-            key = str(payload["key"].item())
-        np.savez(path, key=key, skus=np.array(skus), views=np.array(views), matrix=matrix)
-
-    def re_embeds(self, first, why):
-        """The one right answer to a rewritten cache: build it from the pixels."""
-        backbone = Backbone()
-        gallery = a_stored_gallery(self.directory.name, ("sku_a", "sku_b"), backbone)
-        self.assertTrue(gallery.load(), why)
-        self.assertEqual(backbone.calls, 1, f"{why} must not be served from cache")
-        np.testing.assert_array_equal(gallery._matrix, first._matrix)
-        self.assertEqual([entry.sku_id for entry in gallery._entries], ["sku_a", "sku_a", "sku_b"])
-
-    def test_a_second_start_answers_the_same_from_the_cache(self):
-        first, backbone = self.loaded()
-        self.assertEqual(backbone.calls, 1)
-        self.assertEqual(first.view_count(), 3)
-        self.assertTrue(os.path.isfile(first._cache_path()))
-
-        cached = a_stored_gallery(self.directory.name, ("sku_a", "sku_b"), UnreachableBackbone())
-        self.assertTrue(cached.load())
-        self.assertEqual(cached.view_count(), first.view_count())
-        np.testing.assert_array_equal(cached._matrix, first._matrix)
-        self.assertEqual([entry.view for entry in cached._entries],
-                         [entry.view for entry in first._entries])
-        query = a_query()
-        first.embed = cached.embed = lambda crop: query
-        self.assertEqual(vars(cached.match(object())), vars(first.match(object())))
-
-    def test_changed_view_bytes_re_embed(self):
-        first, _ = self.loaded()
-        before = first._matrix.copy()
-        write_view(os.path.join(self.directory.name, "sku_b.jpeg"), seed=99)
-
-        second, backbone = self.loaded()
-        self.assertEqual(backbone.calls, 1, "a replaced view must not be served from cache")
-        self.assertFalse(np.array_equal(second._matrix, before))
-
-    def test_a_reordered_catalog_does_not_borrow_another_mapping(self):
-        self.loaded()
-        swapped, backbone = self.loaded(names=("sku_b", "sku_a"))
-        self.assertEqual(backbone.calls, 1)
-        self.assertEqual([entry.sku_id for entry in swapped._entries], ["sku_b", "sku_a", "sku_a"])
-
-    def test_another_image_size_is_never_served(self):
-        small, _ = self.loaded(image_size=32)
-        large, backbone = self.loaded(image_size=64)
-        self.assertEqual(backbone.calls, 1)
-        self.assertNotEqual(small._cache_path(), large._cache_path())
-
-    def test_a_corrupt_cache_re_embeds_the_same_matrix(self):
-        first, _ = self.loaded()
-        with open(first._cache_path(), "wb") as handle:
-            handle.write(b"not an npz at all")
-
-        second, backbone = self.loaded()
-        self.assertEqual(backbone.calls, 1)
-        np.testing.assert_array_equal(second._matrix, first._matrix)
-
-    def test_swapped_cache_rows_are_not_served(self):
-        # A current key says nothing about the rows inside the file. The two
-        # sku_a views are adjacent, so a swap has to cross SKUs to mean
-        # anything - and the matrix that moves with them is the dangerous part:
-        # a vector that would then be filed under the wrong product.
-        first, _ = self.loaded()
-        skus, views, matrix = self.cache_payload(first)
-        self.assertEqual(list(zip(skus, views)),
-                         [("sku_a", "sku_a.jpeg"), ("sku_a", "sku_a_1.jpeg"), ("sku_b", "sku_b.jpeg")])
-        self.rewrite_cache(first, skus[::-1], views[::-1], matrix[::-1])
-        self.re_embeds(first, "rows swapped with their vectors")
-
-    def test_mislabelled_cache_rows_are_not_served(self):
-        # The views and the matrix stay where they are, so every row names a
-        # product it was not embedded for.
-        first, _ = self.loaded()
-        skus, views, matrix = self.cache_payload(first)
-        self.rewrite_cache(first, [skus[2], skus[1], skus[0]], views, matrix)
-        self.re_embeds(first, "labels swapped around the views")
-
-    def test_a_duplicated_cache_row_is_not_served(self):
-        # The first view written twice: one image would answer for two products.
-        first, _ = self.loaded()
-        skus, views, matrix = self.cache_payload(first)
-        self.rewrite_cache(first, skus[:1] * 2 + skus[2:], views[:1] * 2 + views[2:], matrix)
-        self.re_embeds(first, "a row repeated")
-
-    def test_a_foreign_sku_row_is_not_served(self):
-        # A row from another store's catalog: a SKU this gallery never enrolled.
-        first, _ = self.loaded()
-        skus, views, matrix = self.cache_payload(first)
-        self.rewrite_cache(first, skus[1:] + ["other_store"], views[1:] + ["other_store.jpeg"], matrix)
-        self.re_embeds(first, "another store's row")
-
-    def test_a_cache_may_omit_a_view_it_could_not_decode(self):
-        # The writer drops a view it could not read and keeps everyone else's
-        # order, so such a cache is still this store's - and still worth serving
-        # rather than re-embedding the whole catalog on every start.
-        first, _ = self.loaded()
-        skus, views, matrix = self.cache_payload(first)
-        self.rewrite_cache(first, skus[:1] + skus[2:], views[:1] + views[2:], matrix[[0, 2]])
-
-        backbone = Backbone()
-        gallery = a_stored_gallery(self.directory.name, ("sku_a", "sku_b"), backbone)
-        self.assertTrue(gallery.load())
-        self.assertEqual(backbone.calls, 0, "a readable shorter cache is still a hit")
-        self.assertEqual([entry.sku_id for entry in gallery._entries], ["sku_a", "sku_b"])
-        np.testing.assert_array_equal(gallery._matrix, first._matrix[[0, 2]])
-
-    def test_a_nonnumeric_cache_matrix_is_not_served(self):
-        # Only the float matrix _cache_write produces is a cache. A complex
-        # matrix is finite and casts without complaint, so it answers with a
-        # different matrix than the one on disk; integer, boolean and text
-        # matrices are not embeddings at all.
-        first, _ = self.loaded()
-        skus, views, matrix = self.cache_payload(first)
-        others = {
-            "complex": matrix.astype(np.complex128),
-            "timedelta": np.zeros_like(matrix, dtype="timedelta64[s]"),
-            "integer": (matrix * 8).astype(np.int32),
-            "boolean": np.ones_like(matrix, dtype=bool),
-            "text": np.zeros(matrix.shape, dtype="<U4"),
-        }
-        for name, other in others.items():
-            with self.subTest(matrix=name):
-                self.rewrite_cache(first, skus, views, other)
-                self.re_embeds(first, f"a {name} matrix")
-
-    def test_a_loaded_gallery_reloads_when_its_source_moves(self):
-        gallery, backbone = self.loaded()
-        self.assertTrue(gallery.load())
-        self.assertEqual(backbone.calls, 1, "an unchanged store must not re-embed")
-
-        write_view(os.path.join(self.directory.name, "sku_b.jpeg"), seed=99)
-        self.assertTrue(gallery.load())
-        self.assertEqual(backbone.calls, 2)
-
-        with open(os.path.join(self.directory.name, "stock.csv"), "a", encoding="utf-8") as handle:
-            handle.write("sku_c,new product\n")
-        self.assertTrue(gallery.load())
-        self.assertEqual(backbone.calls, 3, "stock.csv is part of what an embedding is pinned to")
-        self.assertEqual(gallery.view_count(), 3)
-
-    def test_a_valid_cache_needs_the_pinned_backbone_on_disk(self):
-        # The regression: a cache hit never opens load_dinov2, so a deleted or
-        # altered checkpoint used to read as a loaded gallery and then raise on
-        # the first match - after the server had reported itself ready.
-        self.loaded()
-        gallery = self.real_gallery()
-        self.assertTrue(gallery.load(), "the cache serves while the pin holds")
-
-        os.environ["DORAYAKI_DINOV2_WEIGHTS"] = os.path.join(self.directory.name, "gone.safetensors")
-        missing = self.real_gallery()
-        self.assertFalse(missing.load())
-        self.assertFalse(missing.available())
-
-        os.environ["DORAYAKI_DINOV2_WEIGHTS"] = self.write(
-            os.path.join(self.directory.name, "altered.safetensors"), b"a different checkpoint"
-        )
-        altered = self.real_gallery()
-        self.assertFalse(altered.load())
-        self.assertFalse(altered.available())
-
-    def test_an_altered_backbone_re_embeds_rather_than_serves(self):
-        # With a backbone that can still load, an altered file must re-embed.
-        self.loaded()
-        self.assertTrue(
-            a_stored_gallery(self.directory.name, ("sku_a", "sku_b"), UnreachableBackbone()).load(),
-            "the cache still serves on the pinned file",
-        )
-        self.write(self.weights, b"a different checkpoint")
-        backbone = Backbone()
-        rebuilt = a_stored_gallery(self.directory.name, ("sku_a", "sku_b"), backbone)
-        self.assertTrue(rebuilt.load())
-        self.assertEqual(backbone.calls, 1, "an unpinned cache must not be served")
-
-
-class OfflineWeightsTests(unittest.TestCase):
-    def test_a_missing_weights_file_names_the_path_and_does_not_fetch(self):
-        previous = os.environ.get("DORAYAKI_DINOV2_WEIGHTS")
-        os.environ["DORAYAKI_DINOV2_WEIGHTS"] = os.path.join(
-            "does-not-exist", "dinov2_vits14_lvd142m.safetensors"
-        )
-        try:
-            with self.assertRaises(FileNotFoundError) as raised:
-                load_dinov2(224)
-        finally:
-            if previous is None:
-                os.environ.pop("DORAYAKI_DINOV2_WEIGHTS", None)
-            else:
-                os.environ["DORAYAKI_DINOV2_WEIGHTS"] = previous
-        message = str(raised.exception)
-        self.assertIn("does-not-exist", message)
-        self.assertIn(DINOV2_SHA256, message)
-        self.assertIn("does not download", message)
-
-    def test_the_default_path_stays_inside_the_repo(self):
-        previous = os.environ.pop("DORAYAKI_DINOV2_WEIGHTS", None)
-        try:
-            self.assertTrue(dinov2_weights_path().endswith(
-                os.path.join("weights", "dinov2_vits14_lvd142m.safetensors")
-            ))
-        finally:
-            if previous is not None:
-                os.environ["DORAYAKI_DINOV2_WEIGHTS"] = previous
-
-
-def _head_payload(class_ids, torch):
-    projection = torch.nn.Linear(384, 128)
-    prototypes = torch.nn.functional.normalize(torch.randn(len(class_ids), 128), dim=1)
-    return {
-        "class_ids": list(class_ids),
-        "image_size": 518,
-        "base_model": DINOV2_MODEL,
-        "projection": {key: value.detach().cpu() for key, value in projection.state_dict().items()},
-        "prototypes": prototypes.detach().cpu(),
-    }
-
-
-class HeadPinTests(unittest.TestCase):
-    def test_a_stale_head_cannot_label_this_catalog(self):
-        import tempfile
-
-        import torch
-
-        with tempfile.TemporaryDirectory() as directory:
-            with open(os.path.join(directory, "stock.csv"), "w", encoding="utf-8") as handle:
-                handle.write("SKU_ID,product_name\nsku_a,A\nsku_b,B\nsku_c,C\n")
-            gallery = ProductGallery(directory, ["sku_a", "sku_b", "sku_c"])
-            gallery._available = True
-            path = gallery._head_path
-            subset = ["sku_c", "sku_a"]
-            digest = catalog_digest(directory, subset)
-
-            foreign = _head_payload(["other_store", "sku_a"], torch)
-            torch.save(foreign, path)
-            self.assertFalse(gallery.load_head())
-            self.assertIsNone(gallery._head)
-            self.assertIn("catalog_mismatch", gallery.head_reason)
-            self.assertTrue(gallery.available())
-
-            legacy = _head_payload(subset, torch)
-            torch.save(legacy, path)
-            self.assertTrue(gallery.load_head())
-            self.assertEqual(gallery.head_reason, "legacy_unpinned")
-            self.assertEqual(gallery._head["class_ids"], subset)
-            self.assertNotIn("other_store", gallery._head["class_ids"])
-
-            stale = _head_payload(subset, torch)
-            stale["catalog_digest"] = "0" * 64
-            torch.save(stale, path)
-            self.assertFalse(gallery.load_head())
-            self.assertIsNone(gallery._head)
-            self.assertIn("catalog_mismatch", gallery.head_reason)
-            self.assertTrue(gallery.available())
-
-            wrong_backbone = _head_payload(subset, torch)
-            wrong_backbone["catalog_digest"] = digest
-            wrong_backbone["base_model"] = "vit_base_patch14_dinov2.lvd142m"
-            torch.save(wrong_backbone, path)
-            self.assertFalse(gallery.load_head())
-            self.assertIn("backbone_mismatch", gallery.head_reason)
-
-            wrong_size = _head_payload(subset, torch)
-            wrong_size["catalog_digest"] = digest
-            wrong_size["image_size"] = 224
-            torch.save(wrong_size, path)
-            self.assertFalse(gallery.load_head())
-            self.assertIn("image_size_mismatch", gallery.head_reason)
-
-            torch.save(["not", "a", "head"], path)
-            self.assertFalse(gallery.load_head())
-            self.assertTrue(gallery.head_reason.startswith("malformed"))
-            self.assertTrue(gallery.available())
-            self.assertIsNone(gallery._head)
-
-            current = _head_payload(subset, torch)
-            current["catalog_digest"] = digest
-            torch.save(current, path)
-            self.assertTrue(gallery.load_head())
-            self.assertEqual(gallery.head_reason, "ok")
-            self.assertEqual(gallery._head["class_ids"], subset)
-            self.assertIsNone(head_class_set(["sku_a", "sku_a", ""]))
-
-
-
-class ConcurrentInferenceTests(unittest.TestCase):
-    """The backbone is never entered by two threads at once.
-
-    Metal's shader library is process-global and is not thread-safe while
-    kernels are still being compiled into it, so two threads reaching the first
-    forward together segfault the whole server. This is a contract test, not a
-    timing test: it records overlap rather than measuring speed.
-    """
-
-    def test_two_threads_never_enter_the_backbone_together(self):
-        import time
-        import threading
-        import tempfile
-
-        with tempfile.TemporaryDirectory() as directory:
-            a_store(directory, names=("sku_a", "sku_b"))
-            gallery = ProductGallery(directory, ["sku_a", "sku_b"], device="cpu", image_size=32)
-
-            inside = 0
-            overlapped = []
-            gate = threading.Lock()
-
-            class SlowBackbone:
-                def __call__(self, tensor):
-                    nonlocal inside
-                    with gate:
-                        inside += 1
-                        if inside > 1:
-                            overlapped.append(inside)
-                    time.sleep(0.01)
-                    with gate:
-                        inside -= 1
-                    return tensor.mean(dim=(2, 3)).repeat(1, gallery_module.DINOV2_EMBED_DIM // 3)
-
-            gallery._load_model = lambda: SlowBackbone()
-            crop = np.zeros((24, 32, 3), dtype=np.uint8)
-            errors = []
-
-            def work():
-                try:
-                    for _ in range(6):
-                        gallery.embed(crop)
-                except Exception as error:  # pragma: no cover - reported below
-                    errors.append(repr(error))
-
-            threads = [threading.Thread(target=work) for _ in range(4)]
-            for thread in threads:
-                thread.start()
-            for thread in threads:
-                thread.join()
-
-            self.assertEqual(errors, [])
-            self.assertEqual(overlapped, [], "two threads ran the backbone at the same time")
-
-
-class TrainerPinTests(unittest.TestCase):
-    def test_head_training_loads_the_pinned_file_and_keeps_preprocess(self):
-        import finetune_identity
-        import gallery as gallery_module
-
-        seen = []
-
-        class FakeBackbone:
-            def eval(self):
-                return self
-
-            def to(self, device):
-                return self
-
-        sentinel = FakeBackbone()
-        def fake_load(image_size):
-            seen.append(image_size)
-            return sentinel
-
-
-        # build_model resolves the loader from gallery at call time, so that is
-        # the attribute to replace; finetune_identity has no module-level copy.
-        original = gallery_module.load_dinov2
-        gallery_module.load_dinov2 = fake_load
-        try:
-            model, device = finetune_identity.build_model(224)
-        finally:
-            gallery_module.load_dinov2 = original
-        self.assertIs(model, sentinel)
-        self.assertIn(device, ("cpu", "mps"))
-        self.assertEqual(seen, [224])
-
-        image = np.zeros((10, 20, 3), dtype=np.uint8)
-        self.assertEqual(finetune_identity.prepare(image, 32).shape, gallery_module._prepare(image, 32).shape)
-        self.assertTrue(np.allclose(
-            finetune_identity.prepare(image, 32),
-            gallery_module._prepare(image, 32),
-        ))
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/recognization/code/test_identity.py b/recognization/code/test_identity.py
deleted file mode 100644
index 96849c4..0000000
--- a/recognization/code/test_identity.py
+++ /dev/null
@@ -1,69 +0,0 @@
-"""Ranking contract for decide_identity: what a score set is allowed to name.
-
-Moved out of test_observe.py when the legacy session model was deleted; the
-ranking rules are the live path's, not the legacy one's.
-"""
-
-from __future__ import annotations
-
-import os
-import sys
-import unittest
-
-current_dir = os.path.dirname(os.path.abspath(__file__))
-if current_dir not in sys.path:
-    sys.path.insert(0, current_dir)
-
-from identity import (  # noqa: E402
-    IDENTITY_ACCEPTED,
-    IDENTITY_AMBIGUOUS,
-    IDENTITY_REJECTED,
-    IDENTITY_UNKNOWN,
-    decide_identity,
-)
-
-
-class IdentityRankingTests(unittest.TestCase):
-    def test_zero_scores_are_rejected_not_a_sku(self):
-        ranked = decide_identity({"sku_a": 0.0, "sku_b": 0.0})
-        self.assertEqual(ranked["identity_level"], IDENTITY_REJECTED)
-        self.assertIsNone(ranked["best_sku"])
-        self.assertEqual(ranked["status"], IDENTITY_REJECTED)
-
-    def test_empty_scores_are_rejected(self):
-        ranked = decide_identity({})
-        self.assertEqual(ranked["identity_level"], IDENTITY_REJECTED)
-        self.assertIsNone(ranked["best_sku"])
-
-    def test_weak_unique_score_is_unknown(self):
-        ranked = decide_identity({"sku_a": 0.4, "sku_b": 0.0})
-        self.assertEqual(ranked["identity_level"], IDENTITY_UNKNOWN)
-        self.assertIsNone(ranked["best_sku"])
-
-    def test_close_scores_are_ambiguous(self):
-        ranked = decide_identity({"sku_a": 20.0, "sku_b": 19.5})
-        self.assertEqual(ranked["identity_level"], IDENTITY_AMBIGUOUS)
-        self.assertIsNone(ranked["best_sku"])
-
-    def test_unique_strong_score_is_accepted(self):
-        ranked = decide_identity({"sku_a": 40.0, "sku_b": 5.0})
-        self.assertEqual(ranked["identity_level"], IDENTITY_ACCEPTED)
-        self.assertEqual(ranked["best_sku"], "sku_a")
-        self.assertEqual(ranked["status"], "success")
-
-    def test_van_answers_what_the_gate_would_refuse(self):
-        # Van's contract is the baseline the decision is measured against, so it
-        # must answer exactly where the shipped gate abstains. A weak score and
-        # a close pair are both refused above; here both are named.
-        weak = decide_identity({"sku_a": 0.4, "sku_b": 0.0}, scale="van")
-        self.assertEqual(weak["identity_level"], IDENTITY_ACCEPTED)
-        self.assertEqual(weak["best_sku"], "sku_a")
-        close = decide_identity({"sku_a": 20.0, "sku_b": 19.5}, scale="van")
-        self.assertEqual(close["best_sku"], "sku_a")
-
-    def test_van_still_rejects_when_there_is_no_evidence(self):
-        # Always-argmax is not always-answer: with nothing to rank there is no
-        # argmax, and naming a SKU from an empty score set would be a fabrication.
-        ranked = decide_identity({}, scale="van")
-        self.assertEqual(ranked["identity_level"], IDENTITY_REJECTED)
-        self.assertIsNone(ranked["best_sku"])
diff --git a/recognization/code/test_image.py b/recognization/code/test_image.py
new file mode 100644
index 0000000..57f138f
--- /dev/null
+++ b/recognization/code/test_image.py
@@ -0,0 +1,89 @@
+"""
+Test Script for image.jpg
+1. Runs YOLO detection on image.jpg.
+2. Draws ONLY bounding box rectangles (no class names, no confidence labels).
+3. Saves output to detected_image.jpg.
+4. Matches the detected bounding boxes against stock templates.
+"""
+
+import os
+import sys
+import cv2
+import numpy as np
+
+current_dir = os.path.dirname(os.path.abspath(__file__))
+if current_dir not in sys.path:
+    sys.path.insert(0, current_dir)
+
+from detector import ShelfProductDetector
+from feature_matcher import ProductFeatureMatcher
+from inventory import InventoryManager
+
+
+def main():
+    image_path = "image.jpg"
+    print(f"[1/4] Loading {image_path}...")
+    image = cv2.imread(image_path)
+    if image is None:
+        print(f"[Error] Failed to read {image_path}")
+        return
+
+    h, w = image.shape[:2]
+    print(f"      Image resolution: {w}x{h}")
+
+    print("[2/4] Detecting products using YOLO...")
+    detector = ShelfProductDetector("runs/detect/train/weights/best.pt")
+    # You can adjust conf_threshold as needed (default 0.25)
+    det_res = detector.detect(image, conf_threshold=0.25)
+    boxes = det_res.get("boxes", [])
+    print(f"      Total detected items: {len(boxes)}")
+
+    print("[3/4] Drawing bounding boxes ONLY (no class names, no confidence)...")
+    annotated = detector.draw_detections(image, boxes, draw_labels=False)
+    output_path = "detected_image.jpg"
+    cv2.imwrite(output_path, annotated)
+    print(f"      Saved detection visualization to: {output_path}")
+
+    print("[4/4] Matching against stock catalogue (SURF, ORB, BRISK, Hybrid)...")
+    matcher = ProductFeatureMatcher("stock")
+    inv = InventoryManager("stock")
+
+    print("\n--- Stock Templates Present in image.jpg ---")
+    matched_skus = set()
+    sample_matches = []
+
+    # Test top detections to find corresponding stock SKUs
+    for b in boxes:
+        crop = detector.crop_roi(image, b["box"])
+        # Use hybrid method from the paper
+        res = matcher.classify_crop(crop, method="hybrid")
+        if res.get("status") == "success":
+            sku_id = res["best_sku"]
+            score = res["score"]
+            conf = res["confidence"]
+            prod = inv.get_product(sku_id)
+            if prod:
+                matched_skus.add(sku_id)
+                sample_matches.append({
+                    "box_id": b["id"],
+                    "box": b["box"],
+                    "sku_id": sku_id,
+                    "product_name": prod["product_name"],
+                    "price": prod["price"],
+                    "number_on": prod["number_on"],
+                    "score": score,
+                    "confidence": conf
+                })
+
+    # Sort matches by confidence
+    sample_matches.sort(key=lambda x: x["score"], reverse=True)
+
+    print(f"\nSuccessfully matched {len(matched_skus)} distinct SKUs across detected shelf boxes:")
+    for sm in sample_matches[:10]:
+        print(f"Box #{sm['box_id']:<3} -> [{sm['sku_id']}] {sm['product_name']:<30} | Price: ¥{sm['price']:<5} | On-Shelf: {sm['number_on']} | Score: {sm['score']}")
+
+    print("\n[Complete] Tested image.jpg successfully.")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/recognization/code/test_live.py b/recognization/code/test_live.py
deleted file mode 100644
index f82def6..0000000
--- a/recognization/code/test_live.py
+++ /dev/null
@@ -1,1346 +0,0 @@
-"""
-Contract tests for the live session.
-
-Everything runs against a fake engine, so these tests are fast and deterministic.
-They defend the behaviours a live shelf view depends on: labels that appear
-without blocking frames, labels that never silently change, restarted ids that
-are re-verified rather than inherited, and backpressure that always keeps the
-newest frame.
-"""
-
-from __future__ import annotations
-
-import os
-import asyncio
-import sys
-import threading
-import time
-import unittest
-
-current_dir = os.path.dirname(os.path.abspath(__file__))
-if current_dir not in sys.path:
-    sys.path.insert(0, current_dir)
-
-import cv2  # noqa: E402
-import numpy as np  # noqa: E402
-
-from engine import RecognitionEngine  # noqa: E402
-from live import MAX_CROP_BYTES, LiveSession, LiveSettings  # noqa: E402
-
-
-class FakeDetector:
-    weights_path = "fake.pt"
-
-    def __init__(self):
-        self.boxes = [{"id": 0, "box": [100, 100, 200, 260], "confidence": 0.9, "class_id": 0,
-                       "width": 100, "height": 160, "center": [150, 180]}]
-
-    def detect(self, image, conf_threshold=0.25, iou_threshold=0.45):
-        return {"status": "success", "boxes": [dict(b) for b in self.boxes]}
-
-    def detect_empty_slots(self, image_shape, boxes, min_gap_ratio=0.75):
-        return []
-
-
-class FakeMatcher:
-    def __init__(self, sku="sku_a", score=40.0, delay=0.0):
-        self.sku = sku
-        self.score = score
-        self.delay = delay
-        self.calls = 0
-        self.lock = threading.Lock()
-
-    def score_crop(self, crop, method="hybrid"):
-        with self.lock:
-            self.calls += 1
-        if self.delay:
-            time.sleep(self.delay)
-        # The runner-up stays far below the winner so the decision is about the
-        # configured score, not about ambiguity.
-        return {self.sku: self.score, "other": 0.1}, {}
-
-    def score_crop_fast(self, crop, shortlist=5):
-        return self.score_crop(crop)
-
-
-class FakeGallery:
-    """The gallery's own accept line, which the live aggregate must clear."""
-
-    head_threshold = 0.40
-    threshold = 0.55
-
-    def __init__(self, head=True):
-        self._head = head
-
-    def head_loaded(self):
-        return self._head
-
-
-class CosineEngine(RecognitionEngine):
-    """A gallery-scale engine: score_crop only returns scores the gallery accepted."""
-
-    def __init__(self, *args, head=True, **kwargs):
-        super().__init__(*args, **kwargs)
-        self._fake_gallery = FakeGallery(head=head)
-
-    @property
-    def identity_scale(self):
-        return "cosine"
-
-    def identity_gallery(self):
-        return self._fake_gallery
-
-    def score_crop(self, crop, method=None):
-        scores, _ = self.matcher.score_crop(crop, method or "hybrid")
-        return scores
-
-
-class LocalScaleEngine(RecognitionEngine):
-    """The shipped default scale, with the lanes' evidence injected.
-
-    `local` is the branch that was wrong twice - it re-sorted the lane's own
-    order by value, and its early return on an empty local observation made the
-    text lane's "answer on its own" job unreachable - so it is tested here
-    rather than only through the real lane, which the fake matcher cannot drive.
-    """
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.lane_answers = []
-        self.ocr_calls = 0
-
-    @property
-    def identity_scale(self):
-        return "local"
-
-    def score_crop_lanes(self, crop, method=None, skip_ocr=False):
-        answer = self.lane_answers.pop(0) if self.lane_answers else ({}, [], "")
-        if skip_ocr:
-            # The real engine re-runs only the local lane and returns no text;
-            # the live path reuses the reading it cached on the track.
-            return answer[0], [], ""
-        self.ocr_calls += 1
-        return answer
-
-
-class FakeInventory:
-    """Stateful stand-in for InventoryManager, so stock assertions are real."""
-
-    def __init__(self):
-        self.products = {
-            "sku_a": {"sku_id": "sku_a", "product_name": "コクうま カロリーカット65%", "price": 150.0,
-                      "number_in": 10, "number_out": 1, "number_on": 9,
-                      "receipt_date": "", "expiration_date": "", "local_image_path": None},
-            "other": {"sku_id": "other", "product_name": "アマニ油マヨネーズ", "price": 150.0,
-                      "receipt_date": "", "expiration_date": "", "local_image_path": None},
-        }
-
-    def list_all_skus(self):
-        return list(self.products)
-
-    def get_product(self, sku_id):
-        return self.products.get(sku_id)
-
-    def update_stock(self, sku_id, delta):
-        product = self.products.get(sku_id)
-        if product is None:
-            return False
-        product["number_on"] = max(0, product["number_on"] + delta)
-        if delta < 0:
-            product["number_out"] += abs(delta)
-        else:
-            product["number_in"] += delta
-        return True
-
-
-def make_session(matcher=None, settings=None, engine_cls=RecognitionEngine):
-    detector = FakeDetector()
-    matcher = matcher or FakeMatcher()
-    # These fixtures inject a fake matcher, so they exercise the `matcher`
-    # backend explicitly: the shipped default is the `local` lane, which reads
-    # real reference images and would ignore the fake.
-    engine = engine_cls(detector, matcher, FakeInventory(),
-                        settings=__import__("engine").EngineSettings(
-                            classify_workers=2, identity_backend="matcher"))
-    session = LiveSession("test", engine, settings=settings or LiveSettings(method="hybrid"))
-    session.refresh_catalog()
-    return session, engine, detector, matcher
-
-
-def frame_bytes(width=640, height=480):
-    ok, buffer = cv2.imencode(".jpg", np.zeros((height, width, 3), dtype="uint8"))
-    return buffer.tobytes()
-
-
-def process_payload(session, frame_id, payload):
-    session.submit_frame(frame_id, payload)
-    pending = session.take_pending(timeout=1.0)
-    assert pending is not None, "the session dropped a frame it should have kept"
-    return session.process_frame(*pending)
-
-
-def process(session, frame_id=1):
-    return process_payload(session, frame_id, frame_bytes())
-
-
-def blurred_frame_bytes(width=640, height=480, value=120):
-    """A flat grey capture: the whole frame is out of focus, not just a facing."""
-    ok, buffer = cv2.imencode(".jpg", np.full((height, width, 3), value, dtype="uint8"))
-    return buffer.tobytes()
-
-
-def wait_for(predicate, timeout=2.0):
-    deadline = time.time() + timeout
-    while time.time() < deadline:
-        if predicate():
-            return True
-        time.sleep(0.01)
-    return False
-
-
-class LabelLifecycleTests(unittest.TestCase):
-    def test_first_frame_is_detected_then_labelled(self):
-        session, engine, _, matcher = make_session()
-        first = process(session, 1)
-        self.assertEqual(first["tracks"][0]["identity"]["state"], "unconfirmed")
-        self.assertIsNone(first["tracks"][0]["identity"]["sku_id"])
-
-        process(session, 2)  # second frame: eligible for classification
-        self.assertTrue(wait_for(lambda: matcher.calls > 0), "classification should be scheduled")
-        self.assertTrue(wait_for(lambda: any(
-            track["identity"]["state"] == "recognized" for track in process(session, 3)["tracks"]
-        )), "the product should be labelled once classified")
-        engine.shutdown()
-
-    def test_a_weak_match_is_shown_provisional_not_accepted(self):
-        """Coverage-first: a below-gate candidate is shown, but marked provisional.
-
-        The old contract left this box blank. Blank is now the worse outcome:
-        the operator sees a guess that the next attempt may upgrade, and the
-        wire says which it is, so a client can draw it differently.
-        """
-        session, engine, _, _ = make_session(FakeMatcher(score=0.4))  # below the accept threshold
-        for frame_id in range(1, 8):
-            process(session, frame_id)
-        result = process(session, 9)
-        track = result["tracks"][0]
-        self.assertEqual(track["identity"]["sku_id"], "sku_a",
-                         "a weak match must still be shown")
-        self.assertEqual(track["identity"]["identity_level"], "provisional",
-                         "a weak match must not claim to be accepted")
-        engine.shutdown()
-
-    def test_a_provisional_label_is_never_blanked(self):
-        """Once shown, a guess stays: the budget running out must not take it back."""
-        session, engine, _, _ = make_session(FakeMatcher(score=0.4))
-        for frame_id in range(1, 12):
-            process(session, frame_id)
-        result = process(session, 13)
-        track = result["tracks"][0]
-        self.assertEqual(track["identity"]["sku_id"], "sku_a",
-                         "a spent budget must not blank a label already on screen")
-        engine.shutdown()
-
-    def test_restarted_id_is_re_verified_not_inherited(self):
-        session, engine, detector, matcher = make_session()
-        for frame_id in range(1, 5):
-            process(session, frame_id)
-        self.assertTrue(wait_for(lambda: process(session, 5)["tracks"][0]["identity"]["state"]
-                                 == "recognized"))
-        first_relabels = process(session, 6)["tracks"][0]["relabels"]
-
-        detector.boxes = []           # the product leaves the frame
-        for frame_id in range(7, 14):
-            process(session, frame_id)
-        detector.boxes = [{"id": 0, "box": [100, 100, 200, 260], "confidence": 0.9, "class_id": 0,
-                           "width": 100, "height": 160, "center": [150, 180]}]
-        result = process(session, 14)
-        track = result["tracks"][0]
-        self.assertGreaterEqual(track["relabels"], first_relabels + 1,
-                                "a restarted id must count as re-verified")
-        self.assertGreaterEqual(track["restarts"], 1)
-        engine.shutdown()
-
-    def test_inherited_label_is_flagged(self):
-        """A fresh track covering a labelled product inherits the label, and says so."""
-        session, engine, detector, matcher = make_session()
-        for frame_id in range(1, 5):
-            process(session, frame_id)
-        self.assertTrue(wait_for(lambda: process(session, 5)["tracks"][0]["identity"]["state"]
-                                 == "recognized"))
-        # same place, new detection id => the tracker treats it as a new object
-        detector.boxes = [{"id": 0, "box": [102, 100, 202, 260], "confidence": 0.9, "class_id": 0,
-                           "width": 100, "height": 160, "center": [152, 180]}]
-        result = process(session, 6)
-        self.assertTrue(result["tracks"][0]["inherited"] or result["tracks"][0]["identity"]["state"]
-                        == "recognized")
-        engine.shutdown()
-
-    def test_a_pan_does_not_let_a_neighbour_inherit(self):
-        """A label must not slide onto whatever moved into its screen position.
-
-        The memory box is stored in the coordinates of the frame it was
-        measured on. Under a pan the shelf slides beneath that box, so the
-        product now occupying the old screen position is a different one.
-        Comparing the raw box hands it the label at IoU 1.0; compensating by
-        the shelf movement drops the overlap to nothing.
-        """
-        session, engine, detector, matcher = make_session()
-        for frame_id in range(1, 5):
-            process(session, frame_id)
-        self.assertTrue(wait_for(lambda: process(session, 5)["tracks"][0]["identity"]["state"]
-                                 == "recognized"))
-        # The product leaves, so its track ages out and the next detection at
-        # that screen position is a new object rather than the same one.
-        detector.boxes = []
-        for frame_id in range(6, 10):
-            process(session, frame_id)
-        # The shelf slides 90 px: the labelled product is now 90 px to the
-        # left, and the screen position it used to occupy holds its neighbour.
-        session._frame_origin = [90.0, 0.0]
-        detector.boxes = [{"id": 0, "box": [100, 100, 200, 260], "confidence": 0.9, "class_id": 0,
-                           "width": 100, "height": 160, "center": [150, 180]}]
-        result = process(session, 10)
-        row = result["tracks"][0]
-        self.assertFalse(row["inherited"],
-                         "the product that slid into the old box is not the labelled one")
-        engine.shutdown()
-
-
-class CosineScaleTests(unittest.TestCase):
-    """A gallery-scale score is gated by the gallery, not re-gated by the live path."""
-
-    def test_a_head_scale_match_is_labelled(self):
-        """0.45 clears the head's own 0.40 line but not COSINE_ACCEPT (0.60).
-
-        score_crop only returns scores the gallery already accepted, so the live
-        aggregate must not re-apply the cosine accept line: that refused every
-        head match the gallery had accepted, which is the live regression.
-        """
-        session, engine, _, _ = make_session(FakeMatcher(score=0.45), engine_cls=CosineEngine)
-        for frame_id in range(1, 5):
-            process(session, frame_id)
-        self.assertTrue(wait_for(lambda: any(
-            track["identity"]["state"] == "recognized" for track in process(session, 5)["tracks"]
-        )), "a gallery-accepted head match must be labelled")
-        engine.shutdown()
-
-    def test_a_close_cosine_pair_is_shown_provisional(self):
-        """The margin gate still applies to *acceptance*: two near-equal scores
-        must not be named as settled. Coverage-first shows the top one anyway,
-        marked provisional, because a blank box is the worse outcome."""
-        session, engine, _, _ = make_session(FakeMatcher(score=0.45), engine_cls=CosineEngine)
-        # The matcher contract is (scores, details); a bare dict unpacks into two
-        # strings and raises inside the future callback, where the failure is
-        # swallowed and the test passes for the wrong reason.
-        engine.matcher.score_crop = lambda crop, method=None: ({"sku_a": 0.45, "other": 0.44}, {})
-        for frame_id in range(1, 8):
-            process(session, frame_id)
-        result = process(session, 9)
-        for track in result["tracks"]:
-            self.assertEqual(track["identity"]["identity_level"], "provisional",
-                             "a cosine pair inside the margin must not be accepted")
-        engine.shutdown()
-
-    def test_a_rejected_crop_cannot_dilute_an_accepted_one(self):
-        """aggregate_votes divides by the whole window, so a miss must not lower the line.
-
-        A rejected crop first, then an accepted 0.45: the mean is 0.225. Gating the
-        aggregate at zero would *accept* that, at a score the gallery calls unknown.
-        Coverage-first still shows it, but the level must stay provisional: the
-        dilution must not be promoted to a settled label.
-        """
-        session, engine, _, _ = make_session(FakeMatcher(score=0.45), engine_cls=CosineEngine)
-        answers = iter([{}, {"sku_a": 0.45}])
-        engine.matcher.score_crop = lambda crop, method=None: (next(answers, {}), {})
-        # The retry needs retry_gap frames before the second attempt is eligible.
-        for frame_id in range(1, 12):
-            process(session, frame_id)
-        result = process(session, 13)
-        for track in result["tracks"]:
-            self.assertEqual(track["identity"]["identity_level"], "provisional",
-                             "a diluted mean must not be accepted below the gallery's line")
-        engine.shutdown()
-
-
-class LocalScaleTests(unittest.TestCase):
-    """The shipped default scale, which the fake matcher cannot reach.
-
-    `make_session` builds a `matcher`-backend engine, so `scale == "local"` in
-    `_on_classified` never runs in the rest of this file - and that branch is
-    the one that was wrong twice. These tests drive it with injected lane
-    evidence, so a regression in the local decision path fails here.
-    """
-
-    def test_a_text_only_read_still_labels_the_track(self):
-        """The text lane answers on its own when the local lane has nothing.
-
-        The early return on an empty local observation made this unreachable:
-        a SKU with no enrolled image can only ever be named by its text, so
-        dropping that path silently removed the lane's fill-in job.
-        """
-        session, engine, _, _ = make_session(engine_cls=LocalScaleEngine)
-        engine.lane_answers = [({}, [("sku_a", 0.9)], "コクうま 65%")]
-        result = process(session, 1)
-        self.assertTrue(
-            wait_for(lambda: any(
-                track["identity"]["sku_id"] == "sku_a" for track in process(session, 2)["tracks"]
-            )),
-            "a text-only read must label the track",
-        )
-        engine.shutdown()
-
-    def test_a_text_veto_blocks_a_local_accept(self):
-        """A local match whose package text names another row must not be shown.
-
-        The sibling shares the artwork, so the geometry is real and the local
-        score is high; only the words differ. The text here is below its own
-        accept line, so it vetoes without filling in: the local answer must not
-        reach the wire at all.
-
-        `scores` is a dict keyed by SKU, which is what `score_crop` returns and
-        what `_on_classified` iterates; a list here raises inside the future
-        callback, where the failure is swallowed and the test passes for the
-        wrong reason.
-        """
-        session, engine, _, _ = make_session(engine_cls=LocalScaleEngine)
-        engine.lane_answers = [({"sku_a": 5.0}, [("other", 0.3)], "other マヨネーズ")] * 4
-        for frame_id in range(1, 14):
-            result = process(session, frame_id)
-            for track in result["tracks"]:
-                self.assertNotEqual(
-                    track["identity"]["sku_id"], "sku_a",
-                    "a vetoed local answer must not be labelled",
-                )
-        engine.shutdown()
-
-    def test_a_size_only_read_cannot_name_a_product(self):
-        """A crop reading "400g" matches every 400g product's size line.
-
-        Size and number tokens are not identity text: without this gate the
-        text lane answered a distractor crop reading only "400g" as a product.
-        """
-        session, engine, _, _ = make_session(engine_cls=LocalScaleEngine)
-        engine.lane_answers = [({}, [("sku_a", 0.9)], "400g")] * 4
-        for frame_id in range(1, 14):
-            for track in process(session, frame_id)["tracks"]:
-                self.assertIsNone(
-                    track["identity"]["sku_id"],
-                    "a size-only read must not name a product",
-                )
-        engine.shutdown()
-
-    def test_ocr_runs_once_per_track(self):
-        """Vision on every attempt is what made coverage pacing-dependent.
-
-        A crop costs 5 ms without OCR and 25 ms with it, so re-reading the same
-        package on every attempt cut throughput from 789 to 60 crops/s at 8
-        workers. The reading is cached on the track instead, and the local lane
-        keeps running on every attempt.
-        """
-        session, engine, _, _ = make_session(engine_cls=LocalScaleEngine)
-        # Below the text accept line, so the track stays undecided and retries:
-        # the retry is what would re-run OCR without the cache.
-        engine.lane_answers = [({}, [("sku_a", 0.3)], "コクうま 65%")] * 6
-        for frame_id in range(1, 14):
-            process(session, frame_id)
-        self.assertEqual(engine.ocr_calls, 1, "OCR must run once per track")
-        engine.shutdown()
-
-    def test_no_ocr_never_reads_a_crop(self):
-        """The session's `ocr` switch turns the text lane off for every crop.
-
-        The text lane is the slowest thing the server does per crop, so the
-        phone can ask for a session that runs on geometry alone. Only the text
-        lane goes away: the local lane still names the track, because the whole
-        point of the setting is a faster session, not a quieter one.
-        """
-        session, engine, _, _ = make_session(
-            engine_cls=LocalScaleEngine,
-            settings=LiveSettings(method="hybrid", ocr=False),
-        )
-        engine.lane_answers = [({"sku_a": 5.0}, [("sku_a", 0.9)], "コクうま 65%")] * 6
-        labelled = False
-        for frame_id in range(1, 14):
-            for track in process(session, frame_id)["tracks"]:
-                if track["identity"]["sku_id"] == "sku_a":
-                    labelled = True
-        self.assertEqual(engine.ocr_calls, 0, "no crop may be read with the text lane off")
-        self.assertTrue(labelled, "the local lane must still name the track")
-        engine.shutdown()
-
-    def test_the_ocr_switch_is_reported_back_to_the_client(self):
-        """The client reads the session's own value, not the one it asked for."""
-        session, engine, _, _ = make_session()
-        self.assertTrue(session.config()["ocr"], "the text lane is on by default")
-        session.configure(ocr=False)
-        self.assertFalse(session.config()["ocr"], "the switch must reach the config")
-        session.configure(ocr=True)
-        self.assertTrue(session.config()["ocr"], "and back on again")
-        engine.shutdown()
-
-    def test_a_size_only_read_is_not_cached(self):
-        """An unreadable crop must not veto the product for the track's life.
-
-        The cache used to keep any non-empty read, so a first crop that read
-        only "400g" was replayed on every later attempt: the track could never
-        be named even after a clean crop arrived, because the cached read
-        replaced it. Only a read that carries identity text is cached now, so
-        the next attempt gets a fresh OCR.
-        """
-        session, engine, _, _ = make_session(engine_cls=LocalScaleEngine)
-        engine.lane_answers = [
-            ({}, [("sku_a", 0.9)], "400g"),
-            ({}, [("sku_a", 0.9)], "コクうま 65%"),
-        ] + [({}, [("sku_a", 0.9)], "コクうま 65%")] * 6
-        labelled = False
-        for frame_id in range(1, 14):
-            for track in process(session, frame_id)["tracks"]:
-                if track["identity"]["sku_id"] == "sku_a":
-                    labelled = True
-        self.assertTrue(labelled, "a later readable crop must still name the track")
-        self.assertGreater(engine.ocr_calls, 1, "the size-only read must not be cached")
-        engine.shutdown()
-
-    def test_a_failing_lane_is_counted_and_logged(self):
-        """A lane that raises is a failure, not a crop that matched nothing.
-
-        `_on_classified` substituted an empty answer for the exception, so a
-        run in which `score_crop_lanes` failed on every crop produced tracks,
-        zero labels and a clean log - indistinguishable from a real coverage
-        drop. The counter and the log line are what tell the two apart, and
-        the session has to keep serving frames either way.
-        """
-        session, engine, _, _ = make_session(engine_cls=LocalScaleEngine)
-
-        def boom(crop, method=None, skip_ocr=False):
-            raise RuntimeError("lane unavailable")
-
-        engine.score_crop_lanes = boom
-        named = None
-        with self.assertLogs("dorayaki.live", level="ERROR") as logs:
-            for frame_id in range(1, 14):
-                tracks = process(session, frame_id)["tracks"]
-                self.assertTrue(tracks, "a failing lane must not take the session down")
-                named = named or next(
-                    (t["identity"]["sku_id"] for t in tracks if t["identity"]["sku_id"]), None)
-            self.assertTrue(wait_for(lambda: bool(logs.output)),
-                            "the exception must be logged, not swallowed")
-        self.assertGreaterEqual(session.snapshot()["stats"]["classify_failures"], 1,
-                               "a lane failure must be counted in the session stats")
-        self.assertIsNone(named, "a failed lane must not name a product")
-        engine.shutdown()
-
-
-class TagPassIntegrationTests(unittest.TestCase):
-    """The tag pass must reach the live result without blocking frames."""
-
-    def make_tagged_session(self):
-        from ocr import FakeOcr, OcrLine
-
-        def reader(crop):
-            height, width = crop.shape[:2]
-            return [
-                OcrLine("コーヒー", 0.8, [int(width * 0.05), int(height * 0.05), int(width * 0.4), int(height * 0.3)]),
-                OcrLine("150", 0.9, [int(width * 0.05), int(height * 0.4), int(width * 0.35), int(height * 0.8)]),
-                # 150 x 1.08 = 162.00, so the tag's arithmetic corroborates the price.
-                OcrLine("162.00", 0.6, [int(width * 0.55), int(height * 0.5), int(width * 0.8), int(height * 0.75)]),
-            ]
-
-        session, engine, detector, matcher = make_session()
-        # Rail bands come from product rows, so the shelf needs a row of facings.
-        detector.boxes = [
-            {"id": index, "box": [100 + index * 120, 100, 200 + index * 120, 260],
-             "confidence": 0.9, "class_id": 0, "width": 100, "height": 160,
-             "center": [150 + index * 120, 180]}
-            for index in range(3)
-        ]
-        engine.set_ocr_engine(FakeOcr(reader=reader))
-        self.assertTrue(session.enable_tags())
-        return session, engine, detector
-
-    def test_track_gains_a_price_tag_and_a_reconciliation(self):
-        import time as _time
-
-        session, engine, detector = self.make_tagged_session()
-        result = None
-        for frame_id in range(1, 12):
-            result = process(session, frame_id)
-            _time.sleep(0.05)  # the tag pass runs on its own worker
-            if any(
-                (track.get("price_tag") or {}).get("state") == "readable"
-                for track in result["tracks"]
-            ):
-                break
-        track = result["tracks"][0]
-        self.assertIn("price_tag", track, "the tag pass should attach to the facing above it")
-        self.assertEqual(track["price_tag"]["price"], 150)
-        self.assertIn("reconciliation", track)
-        self.assertEqual(result["summary"]["price_tags"]["readable"], 1)
-        engine.shutdown()
-
-    def test_frames_are_not_blocked_by_ocr(self):
-        """A slow OCR engine must not slow the frame loop: frames keep coming."""
-        import time as _time
-
-        from ocr import FakeOcr, OcrLine
-
-        def slow_reader(crop):
-            _time.sleep(0.5)
-            height, width = crop.shape[:2]
-            return [OcrLine("150", 0.9, [10, 10, int(width * 0.3), int(height * 0.6)])]
-
-        session, engine, detector, _ = make_session()
-        detector.boxes = [
-            {"id": index, "box": [100 + index * 120, 100, 200 + index * 120, 260],
-             "confidence": 0.9, "class_id": 0, "width": 100, "height": 160,
-             "center": [150 + index * 120, 180]}
-            for index in range(3)
-        ]
-        engine.set_ocr_engine(FakeOcr(reader=slow_reader))
-        self.assertTrue(session.enable_tags())
-        started = _time.perf_counter()
-        for frame_id in range(1, 4):
-            process(session, frame_id)
-        elapsed = _time.perf_counter() - started
-        self.assertLess(elapsed, 1.0, "three frames must not wait for a 0.5 s OCR pass")
-        engine.shutdown()
-
-    def test_config_can_turn_tags_off(self):
-        session, engine, _ = self.make_tagged_session()
-        config = session.configure(tags=False)
-        self.assertFalse(config["tags"])
-        self.assertTrue(config["tags_available"])
-        result = process(session, 1)
-        self.assertNotIn("price_tag", result["tracks"][0])
-        engine.shutdown()
-
-    def test_a_panned_shelf_reusing_the_screen_row_is_planned(self):
-        """The same screen row on a shelf that has moved is a new rail."""
-        session, engine, _ = self.make_tagged_session()
-        session._tags.settings.pass_interval_frames = 0
-        session._tags.settings.settle_observations = 1
-        session._tags.settings.settle_passes = 1
-
-        class Panned:
-            def __init__(self):
-                self.calls = 0
-
-            def register(self, image):
-                self.calls += 1
-                # Content moved up 400 px, so the frame origin moves down the shelf.
-                return (0.0, -400.0) if self.calls == 2 else (0.0, 0.0)
-
-        session._shift_map = Panned()
-        process(session, 1)
-        time.sleep(0.05)
-        before = session._tags.stats()["passes_run"]
-        self.assertGreaterEqual(before, 1)
-        process(session, 2)
-        time.sleep(0.1)
-        self.assertGreaterEqual(session._frame_origin[1], 400.0)
-        self.assertGreater(
-            session._tags.stats()["passes_run"], before,
-            "a shelf 400 px away reuses screen row 0 and must be read again",
-        )
-        engine.shutdown()
-
-    def test_overlapping_frames_keep_their_own_origin(self):
-        """A later worker must not become the previous-frame baseline or steal origin."""
-        session, engine, _ = self.make_tagged_session()
-
-        class Ordered:
-            def __init__(self):
-                self.inside = 0
-                self.max_inside = 0
-                self.calls = 0
-                self._inner = threading.Lock()
-
-            def register(self, image):
-                with self._inner:
-                    self.inside += 1
-                    self.max_inside = max(self.max_inside, self.inside)
-                    self.calls += 1
-                    call = self.calls
-                time.sleep(0.03)
-                with self._inner:
-                    self.inside -= 1
-                return (0.0, -400.0)
-
-        session._shift_map = Ordered()
-        errors = []
-
-        def run(frame_id):
-            try:
-                session.process_frame(frame_id, frame_bytes())
-            except Exception as exc:
-                errors.append(exc)
-
-        early = threading.Thread(target=run, args=(1,))
-        late = threading.Thread(target=run, args=(2,))
-        early.start()
-        late.start()
-        early.join(2.0)
-        late.join(2.0)
-        self.assertEqual(errors, [])
-        self.assertFalse(early.is_alive() or late.is_alive())
-        self.assertEqual(session._shift_map.calls, 2)
-        self.assertEqual(session._shift_map.max_inside, 1)
-        self.assertEqual(session._frame_origin[1], 800.0)
-        engine.shutdown()
-
-    def test_cached_tag_projects_at_this_frame_origin(self):
-        """A panned frame must not attach a tag that only overlaps the previous origin."""
-        from tags import TagRecord
-
-        session, engine, _ = self.make_tagged_session()
-        record = TagRecord(
-            tag_id="cached", box=[150, 700, 190, 760], band_index=0, name="cached", price=150,
-            flags=["tax_consistent"],
-        )
-        record.settled = True
-        session._tags._tags[record.tag_id] = record
-
-        class Panned:
-            def __init__(self):
-                self.calls = 0
-
-            def register(self, image):
-                self.calls += 1
-                return (0.0, -400.0) if self.calls == 2 else (0.0, 0.0)
-
-        session._shift_map = Panned()
-        first = session.process_frame(1, frame_bytes())
-        self.assertFalse(any(track.get("price_tag", {}).get("id") for track in first["tracks"]))
-        second = session.process_frame(2, frame_bytes())
-        bound = [track["price_tag"] for track in second["tracks"] if track.get("price_tag", {}).get("id")]
-        self.assertTrue(bound)
-        self.assertGreaterEqual(bound[0]["box"][1], 200)
-        self.assertLess(bound[0]["box"][1], 480)
-        engine.shutdown()
-
-
-class FramePipelineTests(unittest.TestCase):
-    def test_latest_frame_wins_and_drops_are_counted(self):
-        session, engine, _, _ = make_session()
-        session.submit_frame(1, frame_bytes())
-        session.submit_frame(2, frame_bytes())
-        session.submit_frame(3, frame_bytes())
-        pending = session.take_pending(timeout=1.0)
-        self.assertEqual(pending[0], 3, "the newest frame must be the one processed")
-        stats = session.snapshot()["stats"]
-        self.assertEqual(stats["frames_received"], 3)
-        self.assertEqual(stats["frames_dropped"], 2)
-        engine.shutdown()
-
-    def test_undecodable_frame_raises(self):
-        session, engine, _, _ = make_session()
-        with self.assertRaises(Exception):
-            session.process_frame(1, b"not an image")
-        engine.shutdown()
-
-    def test_result_payload_shape(self):
-        session, engine, _, _ = make_session()
-        result = process(session, 1)
-        for key in ("type", "frame_id", "seq", "width", "height", "tracks", "empty_slots",
-                    "quality", "summary", "timing_ms", "stats"):
-            self.assertIn(key, result)
-        self.assertEqual(result["type"], "result")
-        self.assertIn("unconfirmed", result["summary"])
-        track = result["tracks"][0]
-        for key in ("track_id", "box", "raw_box", "confidence", "first_seen_seq", "relabels",
-                    "restarts", "identity"):
-            self.assertIn(key, track)
-        engine.shutdown()
-
-    def test_quality_hint_reports_glare(self):
-        session, engine, _, _ = make_session()
-        ok, buffer = cv2.imencode(".jpg", np.full((480, 640, 3), 255, dtype="uint8"))
-        session.submit_frame(1, buffer.tobytes())
-        result = session.process_frame(*session.take_pending(timeout=1.0))
-        self.assertTrue(result["quality"]["glare"] > 0.5)
-        self.assertEqual(result["quality"]["hint"], "glare")
-        engine.shutdown()
-
-
-class ClassificationBudgetTests(unittest.TestCase):
-    def test_per_session_inflight_is_capped(self):
-        matcher = FakeMatcher(delay=0.3)
-        session, engine, detector, _ = make_session(
-            matcher, LiveSettings(method="hybrid", max_inflight_per_session=2)
-        )
-        detector.boxes = [
-            {"id": index, "box": [10 + index * 120, 10, 110 + index * 120, 200],
-             "confidence": 0.9, "class_id": 0, "width": 100, "height": 190,
-             "center": [60 + index * 120, 105]}
-            for index in range(6)
-        ]
-        process(session, 1)
-        process(session, 2)
-        self.assertLessEqual(matcher.calls, 2, "only the cap may be in flight at once")
-        engine.shutdown()
-
-    def test_method_change_clears_labels_and_ignores_late_results(self):
-        session, engine, _, matcher = make_session(FakeMatcher(delay=0.25))
-        process(session, 1)
-        process(session, 2)
-        session.configure(method="orb")
-        self.assertEqual(session.config()["method"], "orb")
-        result = process(session, 3)
-        for track in result["tracks"]:
-            self.assertNotEqual(track["identity"]["state"], "recognized",
-                                "a late result from the old method must not label a track")
-        engine.shutdown()
-
-    def test_inspect_reports_track_not_in_view(self):
-        session, engine, _, _ = make_session()
-        process(session, 1)
-        with self.assertRaises(KeyError):
-            session.inspect(999)
-
-
-    def test_close_stops_the_loop(self):
-        session, engine, _, _ = make_session()
-        session.close()
-        self.assertTrue(session.closed)
-        self.assertIsNone(session.take_pending(timeout=0.05))
-
-    def test_async_take_keeps_latest_and_wakes_on_close(self):
-        """The server waiter must not miss a frame that beat registration, and close must wake it."""
-        session, engine, _, _ = make_session()
-
-        async def scenario():
-            session.submit_frame(1, frame_bytes())
-            session.submit_frame(2, frame_bytes())
-            first = await asyncio.wait_for(session.take_pending_async(), timeout=1.0)
-            self.assertEqual(first[0], 2)
-
-            waiting = asyncio.create_task(session.take_pending_async())
-            await asyncio.sleep(0)
-            self.assertFalse(waiting.done(), "an empty queue must not complete the waiter")
-            session.reset()
-            session.submit_frame(3, frame_bytes())
-            self.assertEqual((await asyncio.wait_for(waiting, timeout=1.0))[0], 3)
-
-            cancelled = asyncio.create_task(session.take_pending_async())
-            await asyncio.sleep(0)
-            cancelled.cancel()
-            with self.assertRaises(asyncio.CancelledError):
-                await cancelled
-            session.submit_frame(4, frame_bytes())
-            self.assertEqual((await asyncio.wait_for(session.take_pending_async(), timeout=1.0))[0], 4)
-
-            closing = asyncio.create_task(session.take_pending_async())
-            await asyncio.sleep(0)
-            session.close()
-            self.assertIsNone(await asyncio.wait_for(closing, timeout=1.0))
-            self.assertFalse(session.submit_frame(5, frame_bytes()))
-
-        asyncio.run(scenario())
-        engine.shutdown()
-
-
-
-class SmallBoxDetector(FakeDetector):
-    """A facing too small for the frame crop to decide, so only a crop can."""
-
-    def __init__(self, box=None):
-        super().__init__()
-        self.boxes = [{
-            "id": 0, "box": list(box or [100, 100, 130, 200]), "confidence": 0.9,
-            "class_id": 0, "width": 30, "height": 100, "center": [115, 150],
-        }]
-
-
-def make_crop_session(matcher=None, settings=None, detector=None):
-    detector = detector or SmallBoxDetector()
-    engine = RecognitionEngine(
-        detector, matcher or FakeMatcher(), FakeInventory(),
-        settings=__import__("engine").EngineSettings(
-            classify_workers=2, identity_backend="matcher"),
-    )
-    session = LiveSession("crop", engine, settings=settings or LiveSettings(method="hybrid"))
-    session.refresh_catalog()
-    session.enable_crops()
-    return session, engine, detector
-
-
-def requested_crop(result):
-    for track in result["tracks"]:
-        if "crop_request" in track:
-            return track["crop_request"]
-    return None
-
-
-def panned(detector, x):
-    """Put the fake facing at x, the way a camera panning a shelf does.
-
-    The facing stays too narrow for the frame path, so only the crop lane can
-    decide it, and it stays under crop_below_px so a crop is asked about.
-    """
-    detector.boxes[0].update({
-        "box": [x, 100, x + 36, 220], "width": 36, "height": 120, "center": [x + 18, 160],
-    })
-
-
-class GatedMatcher(FakeMatcher):
-    """Holds every classification open until the test lets it finish."""
-
-    def __init__(self):
-        super().__init__()
-        self.gate = threading.Event()
-
-    def score_crop(self, crop, method="hybrid"):
-        self.gate.wait(2.0)
-        return super().score_crop(crop, method)
-
-
-class SecondAnswerWaits(FakeMatcher):
-    """Answers the first crop at once, holds the second one, and disagrees.
-
-    Two facings that overlap this much are the only way a track can be handed
-    a settled label while its own answer is still in the classifier, so the
-    test answers the first crop, lets the track settle, and only then hands
-    over a second crop whose answer names a different product.
-    """
-
-    def __init__(self, sku="sku_a", other="other"):
-        super().__init__(sku=sku)
-        self.gate = threading.Event()
-        self.first_answered = threading.Event()
-        self.disagreeing = other
-        self._seen = 0
-
-    def score_crop(self, crop, method="hybrid"):
-        with self.lock:
-            self._seen += 1
-            turn = self._seen
-            self.calls += 1
-        if turn == 1:
-            self.first_answered.set()
-            return {self.sku: 40.0, "other": 0.1}, {}
-        self.gate.wait(2.0)
-        return {self.disagreeing: 40.0, self.sku: 0.1}, {}
-
-
-class CropLaneTests(unittest.TestCase):
-    """A full-resolution crop is extra evidence for one track, nothing else."""
-
-    def test_a_protocol_v1_client_is_never_asked(self):
-        session, engine, _ = make_crop_session()
-        session.enable_crops  # noqa: B018 - documents that crops start disabled
-        session.settings.crops = False
-        process(session, 1)
-        result = process(session, 2)
-        self.assertIsNone(requested_crop(result))
-        self.assertEqual(result["stats"]["crops"]["requested"], 0)
-        engine.shutdown()
-
-    def test_accepted_crop_labels_the_track_it_was_asked_about(self):
-        session, engine, _ = make_crop_session()
-        process(session, 1)
-        request = requested_crop(process(session, 2))
-        self.assertIsNotNone(request, "a box the frame crop cannot use must be asked about")
-        self.assertEqual(request["frame_id"], 2)
-
-        reply = session.submit_crop(request["request_id"], request["track_id"],
-                                    request["frame_id"], frame_bytes())
-        self.assertEqual(reply["type"], "crop_ack")
-        self.assertTrue(wait_for(lambda: process(session, 3)["stats"]["crops"]["accepted"] == 1))
-        engine.shutdown()
-
-    def test_the_label_says_it_was_measured_on_a_crop(self):
-        session, engine, _ = make_crop_session()
-        process(session, 1)
-        request = requested_crop(process(session, 2))
-        session.submit_crop(request["request_id"], request["track_id"],
-                            request["frame_id"], frame_bytes())
-        self.assertTrue(wait_for(lambda: "crop_request" not in process(session, 3)["tracks"][0]))
-        labelled = [t for t in process(session, 4)["tracks"] if t["identity"]["state"] == "recognized"]
-        self.assertTrue(labelled, "the crop should settle the track the frame could not")
-        self.assertEqual(labelled[0]["identity"]["sku_id"], "sku_a")
-        self.assertEqual(labelled[0]["identity"]["source"], "crop")
-        self.assertEqual(labelled[0]["identity"]["attempts"], 1)
-        engine.shutdown()
-
-    def test_a_crop_can_arrive_after_the_next_frame(self):
-        session, engine, _ = make_crop_session()
-        process(session, 1)
-        request = requested_crop(process(session, 2))
-        process(session, 3)
-        reply = session.submit_crop(request["request_id"], request["track_id"],
-                                    request["frame_id"], frame_bytes())
-        self.assertEqual(reply["type"], "crop_ack")
-        self.assertTrue(wait_for(lambda: session._crops_accepted == 1))
-        engine.shutdown()
-
-    def test_a_delayed_crop_is_accepted_while_the_track_stays_on_its_product(self):
-        """Movement alone is not staleness: the crop still shows the same product."""
-        session, engine, detector = make_crop_session()
-        panned(detector, 100)
-        process(session, 1)
-        panned(detector, 106)
-        request = requested_crop(process(session, 2))
-        self.assertIsNotNone(request, "a facing the frame cannot use is asked about")
-        # The phone answers a frame late, on a track that has panned a little.
-        panned(detector, 112)
-        moved = process(session, 3)
-        self.assertEqual(moved["tracks"][0]["track_id"], request["track_id"])
-        reply = session.submit_crop(request["request_id"], request["track_id"],
-                                    request["frame_id"], frame_bytes())
-        self.assertEqual(reply["type"], "crop_ack")
-        self.assertTrue(wait_for(lambda: process(session, 4)["stats"]["crops"]["accepted"] == 1))
-        labelled = [t for t in process(session, 5)["tracks"] if t["identity"]["state"] == "recognized"]
-        self.assertEqual(labelled[0]["identity"]["sku_id"], "sku_a")
-        engine.shutdown()
-
-    def test_a_crop_for_a_track_that_slid_onto_its_neighbour_is_refused(self):
-        """The crop photographs one product; a track that walked off it answers nothing."""
-        session, engine, detector = make_crop_session()
-        panned(detector, 100)
-        process(session, 1)
-        panned(detector, 118)
-        request = requested_crop(process(session, 2))
-        self.assertIsNotNone(request)
-        # The pan carries the track onto the next product before the phone answers.
-        panned(detector, 136)
-        moved = process(session, 3)
-        self.assertEqual(moved["tracks"][0]["track_id"], request["track_id"],
-                         "the id must survive the pan, or this refusal proves nothing")
-        reply = session.submit_crop(request["request_id"], request["track_id"],
-                                    request["frame_id"], frame_bytes())
-        self.assertEqual(reply["code"], "CROP_STALE")
-        self.assertEqual(session._tracks[request["track_id"]].in_flight, 0,
-                         "a refused crop gives the track's busy slot back")
-        result = process(session, 4)
-        self.assertEqual(result["stats"]["crops"]["accepted"], 0)
-        self.assertEqual(result["stats"]["crops"]["rejected"], 1)
-        self.assertNotEqual(result["tracks"][0]["identity"]["state"], "recognized")
-        engine.shutdown()
-
-    def test_a_crop_answered_after_the_track_left_its_product_does_not_label_it(self):
-        """A classification in flight cannot settle a track that has since moved on."""
-        matcher = GatedMatcher()
-        session, engine, detector = make_crop_session(matcher=matcher)
-        panned(detector, 100)
-        process(session, 1)
-        panned(detector, 118)
-        request = requested_crop(process(session, 2))
-        self.assertIsNotNone(request)
-        reply = session.submit_crop(request["request_id"], request["track_id"],
-                                    request["frame_id"], frame_bytes())
-        self.assertEqual(reply["type"], "crop_ack", "the track is still on the product here")
-        # The answer is still in the classifier when the pan takes the track off it.
-        panned(detector, 136)
-        process(session, 3)
-        matcher.gate.set()
-        track = session._tracks[request["track_id"]]
-        self.assertTrue(wait_for(lambda: session._tracks[request["track_id"]].in_flight == 0))
-        self.assertEqual(track.attempts, 0, "a crop of a product left behind is not an attempt")
-        self.assertIsNone(track.identity, "the neighbour must not wear the old product's label")
-        engine.shutdown()
-
-    def test_a_track_is_asked_once(self):
-        session, engine, _ = make_crop_session()
-        process(session, 1)
-        process(session, 2)
-        result = process(session, 3)
-        self.assertIsNone(requested_crop(result), "one crop per track object")
-        self.assertEqual(result["stats"]["crops"]["requested"], 1)
-        engine.shutdown()
-
-    def test_a_crop_for_another_frame_is_refused(self):
-        session, engine, _ = make_crop_session()
-        process(session, 1)
-        request = requested_crop(process(session, 2))
-        reply = session.submit_crop(request["request_id"], request["track_id"], 999, frame_bytes())
-        self.assertEqual(reply["code"], "CROP_MISMATCH")
-        result = process(session, 3)
-        self.assertNotEqual(result["tracks"][0]["identity"]["state"], "recognized")
-        self.assertEqual(result["stats"]["crops"]["rejected"], 1)
-        self.assertEqual(result["stats"]["crops"]["accepted"], 0)
-        engine.shutdown()
-
-    def test_a_crop_for_another_track_is_refused(self):
-        session, engine, _ = make_crop_session()
-        process(session, 1)
-        request = requested_crop(process(session, 2))
-        reply = session.submit_crop(request["request_id"], request["track_id"] + 41,
-                                    request["frame_id"], frame_bytes())
-        self.assertEqual(reply["code"], "CROP_MISMATCH")
-        self.assertEqual(process(session, 3)["stats"]["crops"]["accepted"], 0)
-        engine.shutdown()
-
-    def test_an_oversized_crop_is_refused(self):
-        session, engine, _ = make_crop_session(settings=LiveSettings(max_crop_bytes=2048))
-        session.enable_crops(2048)
-        process(session, 1)
-        request = requested_crop(process(session, 2))
-        reply = session.submit_crop(request["request_id"], request["track_id"],
-                                    request["frame_id"], b"\xff\xd8" + b"\x00" * 4096)
-        self.assertEqual(reply["code"], "CROP_TOO_LARGE")
-        self.assertEqual(process(session, 3)["stats"]["crops"]["accepted"], 0)
-        engine.shutdown()
-
-    def test_an_undecodable_crop_is_refused(self):
-        session, engine, _ = make_crop_session()
-        process(session, 1)
-        request = requested_crop(process(session, 2))
-        reply = session.submit_crop(request["request_id"], request["track_id"],
-                                    request["frame_id"], b"not an image")
-        self.assertEqual(reply["code"], "CROP_UNDECODABLE")
-        self.assertEqual(process(session, 3)["stats"]["crops"]["accepted"], 0)
-        engine.shutdown()
-
-    def test_a_crop_cannot_be_replayed(self):
-        session, engine, _ = make_crop_session()
-        process(session, 1)
-        request = requested_crop(process(session, 2))
-        first = session.submit_crop(request["request_id"], request["track_id"],
-                                    request["frame_id"], frame_bytes())
-        self.assertEqual(first["type"], "crop_ack")
-        again = session.submit_crop(request["request_id"], request["track_id"],
-                                    request["frame_id"], frame_bytes())
-        self.assertEqual(again["code"], "CROP_UNKNOWN_REQUEST")
-        engine.shutdown()
-
-    def test_an_unknown_request_is_refused(self):
-        session, engine, _ = make_crop_session()
-        process(session, 1)
-        reply = session.submit_crop("c-nope", 1, 1, frame_bytes())
-        self.assertEqual(reply["code"], "CROP_UNKNOWN_REQUEST")
-        engine.shutdown()
-
-    def test_an_unanswered_request_expires_and_frees_the_track(self):
-        session, engine, _ = make_crop_session(settings=LiveSettings(crop_ttl_frames=1))
-        process(session, 1)
-        request = requested_crop(process(session, 2))
-        process(session, 3)
-        # The request was made on frame 2 with a one-frame budget, so by frame 4
-        # it is out of date and the track it held is free again.
-        result = process(session, 4)
-        self.assertEqual(result["stats"]["crops"]["rejected"], 1, "an unanswered request is counted")
-        late = session.submit_crop(request["request_id"], request["track_id"],
-                                   request["frame_id"], frame_bytes())
-        self.assertEqual(late["code"], "CROP_UNKNOWN_REQUEST")
-        engine.shutdown()
-
-    def test_the_client_states_the_smaller_crop_limit(self):
-        session, engine, _ = make_crop_session()
-        self.assertEqual(session.enable_crops(4096)["max_crop_bytes"], 4096)
-        self.assertEqual(session.capabilities()["crop"], True)
-        engine.shutdown()
-
-    def test_a_refused_limit_never_turns_the_lane_on(self):
-        session, engine, _ = make_crop_session()
-        session.settings.crops = False  # a lane no client has advertised yet
-        for bad in (True, 0, -1, 4096.0, "4096", MAX_CROP_BYTES + 1):
-            with self.subTest(limit=bad):
-                with self.assertRaises(ValueError):
-                    session.enable_crops(bad)
-                self.assertFalse(session.capabilities()["crop"],
-                                 "a refused limit must leave the lane off")
-        self.assertEqual(session.enable_crops(4096)["max_crop_bytes"], 4096)
-        engine.shutdown()
-
-    def test_a_reset_while_the_crop_decodes_is_refused(self):
-        """An ack promises this crop decided that track; a reset says it cannot."""
-        matcher = FakeMatcher()
-        session, engine, _ = make_crop_session(matcher=matcher)
-        process(session, 1)
-        request = requested_crop(process(session, 2))
-        self.assertIsNotNone(request)
-
-        decoding = threading.Event()
-        resumed = threading.Event()
-        decode = engine.decode_image
-
-        def gated_decode(payload):
-            decoding.set()
-            resumed.wait(2.0)
-            return decode(payload)
-
-        engine.decode_image = gated_decode
-        replies = {}
-
-        def answer():
-            replies["reply"] = session.submit_crop(
-                request["request_id"], request["track_id"], request["frame_id"], frame_bytes()
-            )
-
-        worker = threading.Thread(target=answer)
-        worker.start()
-        self.assertTrue(decoding.wait(2.0), "the crop must be decoding before the reset")
-        session.reset()
-        resumed.set()
-        worker.join(5.0)
-        self.assertIn("reply", replies, "the answer never came back")
-        self.assertEqual(replies["reply"]["code"], "CROP_STALE")
-        self.assertEqual(session._crops_accepted, 0)
-        self.assertEqual(matcher.calls, 0, "a refused crop never reaches the classifier")
-        engine.shutdown()
-
-    def test_a_blurred_frame_is_not_asked_about(self):
-        """More pixels of an out-of-focus scene are not more evidence."""
-        session, engine, _ = make_crop_session()
-        process(session, 1)
-        result = process_payload(session, 2, blurred_frame_bytes())
-        self.assertEqual(result["quality"]["hint"], "blur",
-                         "the frame under test must be the blurry one")
-        self.assertIsNone(requested_crop(result))
-        self.assertEqual(result["stats"]["crops"]["requested"], 0)
-        engine.shutdown()
-
-    def test_a_distant_shelf_is_still_asked_about(self):
-        """Too far to read at the frame's own resolution is the case a crop exists for."""
-        session, engine, detector = make_crop_session(
-            settings=LiveSettings(method="hybrid", min_crop_px=60)
-        )
-        detector.boxes[0].update({
-            "box": [300, 380, 336, 430], "width": 36, "height": 50, "center": [318, 405],
-        })
-        process(session, 1)
-        result = process(session, 2)
-        self.assertEqual(result["quality"]["hint"], "too_far",
-                         "the frame under test must be the distant one")
-        self.assertIsNotNone(requested_crop(result))
-        engine.shutdown()
-
-    def test_a_facing_the_frame_clips_is_not_asked_about(self):
-        session, engine, detector = make_crop_session()
-        clipped = {"box": [0, 100, 36, 220], "width": 36, "height": 120, "center": [18, 160]}
-        detector.boxes[0].update(clipped)
-        process(session, 1)
-        result = process(session, 2)
-        self.assertIsNone(requested_crop(result), "half a facing is not worth the phone's pixels")
-        self.assertEqual(result["stats"]["crops"]["requested"], 0)
-        # The same facing, walked clear of the edge, is asked about: only the
-        # clip kept it out of the lane.
-        asked = None
-        for frame_id, x in enumerate([12, 24, 36], start=3):
-            detector.boxes[0].update({"box": [x, 100, x + 36, 220], "center": [x + 18, 160]})
-            result = process(session, frame_id)
-            asked = asked or requested_crop(result)
-            if asked is not None:
-                break
-        self.assertIsNotNone(asked, "a whole facing is worth the phone's pixels")
-        engine.shutdown()
-
-    def test_a_box_this_frame_did_not_measure_is_not_asked_about(self):
-        """The phone crops by the box it is sent, so a stale box crops the wrong thing."""
-        session, engine, detector = make_crop_session()
-        process(session, 1)
-        detector.boxes[0]["confidence"] = 0.15  # too low for the tracker to trust
-        result = process(session, 2)
-        self.assertIsNone(result["quality"]["hint"], "only the detection is at fault here")
-        self.assertIsNone(requested_crop(result))
-        detector.boxes[0]["confidence"] = 0.9
-        self.assertIsNotNone(requested_crop(process(session, 3)),
-                            "the same facing is asked about once a frame measures it again")
-        engine.shutdown()
-
-    def test_a_method_change_lets_the_small_facing_ask_again(self):
-        """A crop minted under the old method is not an attempt under the new one."""
-        session, engine, _ = make_crop_session()
-        process(session, 1)
-        first = requested_crop(process(session, 2))
-        self.assertIsNotNone(first)
-        session.configure(method="orb")
-        second = requested_crop(process(session, 3))
-        self.assertIsNotNone(second, "the new method gets a budget of its own")
-        self.assertNotEqual(second["request_id"], first["request_id"])
-        self.assertEqual(process(session, 4)["stats"]["crops"]["requested"], 2)
-        late = session.submit_crop(first["request_id"], first["track_id"],
-                                   first["frame_id"], frame_bytes())
-        self.assertEqual(late["code"], "CROP_UNKNOWN_REQUEST",
-                         "the outstanding request was minted under settings the session left")
-        engine.shutdown()
-
-    def test_an_expired_request_leaves_exactly_one_more_ask(self):
-        session, engine, _ = make_crop_session(settings=LiveSettings(crop_ttl_frames=1))
-        process(session, 1)
-        first = requested_crop(process(session, 2))
-        self.assertIsNotNone(first)
-        process(session, 3)
-        # The one-frame budget ran out on frame 4, so the facing gets one more
-        # ask rather than being left with a request nobody will ever answer.
-        second = requested_crop(process(session, 4))
-        self.assertIsNotNone(second, "an expiry must not strand a facing only the crop can decide")
-        self.assertNotEqual(second["request_id"], first["request_id"])
-        process(session, 5)
-        process(session, 6)
-        result = process(session, 7)
-        self.assertIsNone(requested_crop(result), "the budget is bounded, not a loop")
-        self.assertEqual(result["stats"]["crops"]["requested"], 2)
-        engine.shutdown()
-
-    def test_a_drifted_label_lets_the_next_product_be_asked_about(self):
-        """The facing in the frame is a different product, so it gets its own ask."""
-        session, engine, detector = make_crop_session()
-        panned(detector, 100)
-        process(session, 1)
-        request = requested_crop(process(session, 2))
-        session.submit_crop(request["request_id"], request["track_id"],
-                            request["frame_id"], frame_bytes())
-        self.assertTrue(wait_for(lambda: session._tracks[request["track_id"]].state == "recognized"))
-        track_id = request["track_id"]
-        asked_again = None
-        for frame_id, x in enumerate([118, 136, 154, 172, 190, 208], start=3):
-            panned(detector, x)
-            result = process(session, frame_id)
-            self.assertIn(track_id, session._tracks, "the id must survive the pan")
-            again = requested_crop(result)
-            if again is not None and again["request_id"] != request["request_id"]:
-                asked_again = again
-                break
-        self.assertIsNotNone(asked_again,
-                             "a drifted label must hand the new product its own crop ask")
-        self.assertGreater(asked_again["box"][0], request["box"][0],
-                           "the new ask must be about where the product is now")
-        engine.shutdown()
-
-    def test_a_late_crop_cannot_repaint_a_settled_label(self):
-        """A track that took a neighbour's label keeps it; a later crop does not overrule it."""
-        matcher = SecondAnswerWaits()
-        session, engine, detector = make_crop_session(
-            matcher=matcher, settings=LiveSettings(method="hybrid", min_crop_px=60)
-        )
-        # Two narrow facings that overlap by more than a label can be trusted
-        # across: that is the only shape in which one inherits the other's.
-        detector.boxes = [
-            {"id": 0, "box": [100, 100, 150, 340], "confidence": 0.9, "class_id": 0,
-             "width": 50, "height": 240, "center": [125, 220]},
-            {"id": 1, "box": [110, 100, 160, 340], "confidence": 0.9, "class_id": 0,
-             "width": 50, "height": 240, "center": [135, 220]},
-        ]
-        process(session, 1)
-        requests = {row["track_id"]: row["crop_request"]
-                    for row in process(session, 2)["tracks"] if "crop_request" in row}
-        self.assertEqual(len(requests), 2, "both facings are too small for the frame crop")
-        settled_id, waiting_id = list(requests)
-
-        session.submit_crop(requests[settled_id]["request_id"], settled_id,
-                            requests[settled_id]["frame_id"], frame_bytes())
-        self.assertTrue(matcher.first_answered.wait(2.0), "the first crop must be decided")
-        session.submit_crop(requests[waiting_id]["request_id"], waiting_id,
-                            requests[waiting_id]["frame_id"], frame_bytes())
-
-        inherited = process(session, 3)
-        row = next(t for t in inherited["tracks"] if t["track_id"] == waiting_id)
-        self.assertTrue(row["inherited"], "the facing takes the label its neighbour settled")
-        self.assertEqual(row["identity"]["sku_id"], "sku_a")
-
-        matcher.gate.set()
-        self.assertTrue(wait_for(lambda: session._tracks[waiting_id].in_flight == 0))
-        settled = next(t for t in process(session, 4)["tracks"] if t["track_id"] == waiting_id)
-        self.assertEqual(settled["identity"]["sku_id"], "sku_a",
-                         "a crop that arrives late must not overrule a settled label")
-        self.assertTrue(settled["inherited"])
-        self.assertEqual(settled["identity"]["source"], "inherited")
-        self.assertEqual(session._tracks[waiting_id].attempts, 0,
-                         "an answer that could not decide is not an attempt")
-        engine.shutdown()
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/recognization/code/test_ocr_tags.py b/recognization/code/test_ocr_tags.py
deleted file mode 100644
index 2d481da..0000000
--- a/recognization/code/test_ocr_tags.py
+++ /dev/null
@@ -1,475 +0,0 @@
-"""
-Contract tests for OCR-backed price tags.
-
-No OCR engine runs: a fake engine supplies text lines, so these tests defend the
-tag layer's own logic - band geometry, clustering, Japanese price-tag parsing,
-the multi-pass vote, and tag-to-product association.
-"""
-
-from __future__ import annotations
-
-import os
-import sys
-import unittest
-
-current_dir = os.path.dirname(os.path.abspath(__file__))
-if current_dir not in sys.path:
-    sys.path.insert(0, current_dir)
-
-import numpy as np  # noqa: E402
-
-from ocr import FakeOcr, NullOcr, OcrLine, OcrResult, OcrUnavailable, build_ocr_engine  # noqa: E402
-from tags import (  # noqa: E402
-    TAG_STATE_ABSENT,
-    TAG_STATE_DETECTED,
-    TAG_STATE_READABLE,
-    TAG_STATE_UNREADABLE,
-    TagService,
-    TagSettings,
-    cluster_tags,
-    lines_in_band,
-    parse_price_tag,
-    rail_bands,
-)
-from tag_cards import StorePolicy, StorePolicyError, load_store_policy  # noqa: E402
-import tag_cards  # noqa: E402
-
-
-def image(width=1000, height=600):
-    return np.zeros((height, width, 3), dtype="uint8")
-
-
-def box(x1, y1, x2, y2, box_id=0):
-    return {"id": box_id, "box": [x1, y1, x2, y2], "confidence": 0.9,
-            "width": x2 - x1, "height": y2 - y1, "center": [(x1 + x2) // 2, (y1 + y2) // 2]}
-
-
-# --------------------------------------------------------------------------- parsing
-
-
-class PriceTagParsingTests(unittest.TestCase):
-    def test_reads_the_measured_tag(self):
-        """Text as macOS Vision returned it from a real Japanese tag (image6)."""
-        fields = parse_price_tag([
-            "・ユービーゼロ ノンコレステロールく310g＞",
-            "357",
-            "2.4割引",
-            "269",
-            "290.52",
-        ])
-        self.assertEqual(fields.discount, 0.24)
-        self.assertEqual(fields.unit_price, 290.52)
-        self.assertIn(fields.price, (269, 357))
-        self.assertIn("ゼロノンコレステロール", fields.name or "")
-        self.assertIn("sale", fields.flags)
-        self.assertIn("discounted", fields.flags)
-
-    def test_largest_printed_number_is_the_price(self):
-        lines = [
-            OcrLine("357", 1.0, [10, 10, 40, 30]),
-            OcrLine("269", 0.9, [60, 5, 150, 60]),   # big sale price
-            OcrLine("2.4割引", 0.9, [10, 40, 80, 60]),
-        ]
-        fields = parse_price_tag([line.text for line in lines], lines)
-        self.assertEqual(fields.price, 269)
-        self.assertEqual(fields.regular_price, 357)
-
-    def test_unit_price_is_not_mistaken_for_a_price(self):
-        fields = parse_price_tag(["290.52", "158"])
-        self.assertEqual(fields.price, 158)
-        self.assertEqual(fields.unit_price, 290.52)
-
-    def test_sale_banner_is_not_a_name(self):
-        fields = parse_price_tag(["毎日お買得", "198"])
-        self.assertIsNone(fields.name)
-        self.assertEqual(fields.price, 198)
-
-    def test_implausible_numbers_are_ignored(self):
-        fields = parse_price_tag(["123456", "0"])
-        self.assertIsNone(fields.price)
-
-    def test_empty_input_yields_nothing_not_zero(self):
-        fields = parse_price_tag([])
-        self.assertIsNone(fields.price)
-        self.assertIsNone(fields.name)
-        self.assertEqual(fields.flags, [])
-
-    def test_discount_out_of_range_is_ignored(self):
-        fields = parse_price_tag(["98割", "198"])
-        self.assertIsNone(fields.discount)
-        self.assertEqual(fields.price, 198)
-
-
-# --------------------------------------------------------------------------- geometry
-
-
-class BandGeometryTests(unittest.TestCase):
-    def test_band_sits_below_the_row(self):
-        boxes = [box(100, 100, 200, 300, 0), box(220, 100, 320, 300, 1), box(340, 100, 440, 300, 2)]
-        bands = rail_bands(boxes, (600, 1000))
-        self.assertEqual(len(bands), 1)
-        x1, y1, x2, y2 = bands[0].box
-        self.assertLessEqual(x1, 100)
-        self.assertGreaterEqual(x2, 440)
-        self.assertGreaterEqual(y1, 280)     # starts just inside the row
-        self.assertGreater(y2, 300)          # and reaches below it
-        self.assertEqual(bands[0].row_index, 1)
-
-    def test_two_rows_give_two_bands(self):
-        boxes = [
-            box(100, 100, 200, 300, 0), box(220, 100, 320, 300, 1), box(340, 100, 440, 300, 2),
-            box(100, 400, 200, 600, 3), box(220, 400, 320, 600, 4), box(340, 400, 440, 600, 5),
-        ]
-        bands = rail_bands(boxes, (800, 1000))
-        self.assertEqual(len(bands), 2)
-        self.assertLess(bands[0].box[3], bands[1].box[1])
-
-    def test_no_boxes_no_bands(self):
-        self.assertEqual(rail_bands([], (600, 1000)), [])
-
-
-class ClusteringTests(unittest.TestCase):
-    def band(self):
-        return rail_bands([box(100, 100, 200, 300, 0), box(220, 100, 320, 300, 1),
-                           box(340, 100, 440, 300, 2)], (600, 1000))[0]
-
-    def test_nearby_lines_join_one_tag(self):
-        band = self.band()
-        lines = [
-            OcrLine("キュービー", 0.9, [110, 318, 300, 338]),
-            OcrLine("269", 0.9, [110, 345, 240, 375]),
-        ]
-        tags = cluster_tags(lines, band, (600, 1000))
-        self.assertEqual(len(tags), 1)
-        self.assertEqual(tags[0]["fields"].price, 269)
-
-    def test_distant_lines_become_separate_tags(self):
-        band = self.band()
-        lines = [
-            OcrLine("コーヒー", 0.9, [110, 318, 250, 338]),
-            OcrLine("200", 0.9, [110, 345, 230, 375]),
-            OcrLine("お茶", 0.9, [350, 318, 430, 338]),
-            OcrLine("300", 0.9, [350, 345, 430, 375]),
-        ]
-        tags = cluster_tags(lines, band, (600, 1000))
-        self.assertEqual(len(tags), 2)
-        self.assertEqual({tag["fields"].price for tag in tags}, {200, 300})
-
-    def test_single_tiny_fragment_is_rejected(self):
-        band = self.band()
-        lines = [OcrLine("7", 0.4, [150, 350, 160, 360])]
-        self.assertEqual(cluster_tags(lines, band, (600, 1000)), [])
-
-    def test_lines_outside_the_band_are_ignored(self):
-        band = self.band()
-        lines = [OcrLine("999", 0.9, [120, 10, 180, 40])]
-        self.assertEqual(lines_in_band(lines, band), [])
-
-
-# --------------------------------------------------------------------------- service
-
-
-def vision_like_reader(image_shape=None):
-    """A fake reader that returns tag text wherever it is called, in crop space."""
-    def reader(crop):
-        height, width = crop.shape[:2]
-        return [
-            OcrLine("キュービー ゼロ ノンコレステロール", 0.6, [int(width * 0.02), int(height * 0.05), int(width * 0.28), int(height * 0.3)]),
-            OcrLine("357", 0.9, [int(width * 0.02), int(height * 0.4), int(width * 0.12), int(height * 0.7)]),
-            OcrLine("269", 0.9, [int(width * 0.10), int(height * 0.4), int(width * 0.30), int(height * 0.85)]),
-            # The tax-included price: a tag is only readable when its own arithmetic
-            # corroborates the price (269 x 1.08 = 290.52).
-            OcrLine("290.52", 0.6, [int(width * 0.12), int(height * 0.55), int(width * 0.32), int(height * 0.78)]),
-        ]
-    return reader
-
-
-class TagServiceTests(unittest.TestCase):
-    def make(self, **overrides):
-        settings = TagSettings(**overrides)
-        engine = FakeOcr(reader=vision_like_reader())
-        service = TagService(engine, settings=settings)
-        return service, engine
-
-    def test_pass_produces_a_readable_tag_in_original_coordinates(self):
-        service, engine = self.make()
-        boxes = [box(100, 100, 200, 300, 0), box(220, 100, 320, 300, 1), box(340, 100, 440, 300, 2)]
-        image_array = image()
-        bands = service.plan(boxes, image_array.shape)
-        self.assertTrue(bands)
-        service.run_pass(image_array, bands[0])
-        by_track, tags, counts = service.attach(
-            [{"track_id": 0, "box": [100, 100, 200, 300]}]
-        )
-        self.assertEqual(len(tags), 1)
-        self.assertEqual(tags[0]["state"], TAG_STATE_READABLE)
-        self.assertEqual(tags[0]["price"], 269)
-        self.assertEqual(counts[TAG_STATE_READABLE], 1)
-        # the tag box must be inside the image, i.e. mapped back from the crop
-        tag_box = tags[0]["box"]
-        self.assertGreaterEqual(tag_box[0], 0)
-        self.assertLessEqual(tag_box[2], image_array.shape[1])
-
-    def test_vote_overcomes_a_single_misread(self):
-        reads = {"count": 0}
-
-        def reader(crop):
-            reads["count"] += 1
-            height, width = crop.shape[:2]
-            # A card is read several times per pass (multi-scale + red channel), so
-            # one bad read among many is what the vote is for. (A misread that
-            # repeats on every read of the same channel would survive the vote -
-            # that is the documented consistent-misread limit.)
-            price = "26" if reads["count"] == 2 else "269"
-            return [
-                OcrLine("コーヒー", 0.6, [10, 10, 200, 30]),
-                OcrLine(price, 0.9, [20, 40, 190, 80]),
-            ]
-
-        service = TagService(FakeOcr(reader=reader), settings=TagSettings(pass_interval_frames=0))
-        boxes = [box(100, 100, 200, 300, 0), box(220, 100, 320, 300, 1), box(340, 100, 440, 300, 2)]
-        array = image()
-        for _ in range(3):
-            bands = service.plan(boxes, array.shape)
-            for band in bands:
-                service.run_pass(array, band)
-        _, tags, _ = service.attach([{"track_id": 0, "box": [100, 100, 200, 300]}])
-        self.assertEqual(tags[0]["price"], 269, "the majority read must win")
-
-    def test_settled_bands_stop_being_planned(self):
-        service, _ = self.make(pass_interval_frames=0)
-        boxes = [box(100, 100, 200, 300, 0), box(220, 100, 320, 300, 1), box(340, 100, 440, 300, 2)]
-        array = image()
-        for _ in range(5):
-            for band in service.plan(boxes, array.shape):
-                service.run_pass(array, band)
-        self.assertTrue(service.stats()["settled"] >= 1)
-        self.assertEqual(service.plan(boxes, array.shape), [], "a settled shelf costs no OCR")
-
-    def test_cadence_limits_passes(self):
-        service, _ = self.make(pass_interval_frames=15)
-        boxes = [box(100, 100, 200, 300, 0), box(220, 100, 320, 300, 1), box(340, 100, 440, 300, 2)]
-        array = image()
-        self.assertTrue(service.plan(boxes, array.shape))
-        self.assertEqual(service.plan(boxes, array.shape), [], "not due yet")
-
-    def test_facing_without_a_tag_reports_absent(self):
-        service, _ = self.make()
-        boxes = [box(100, 100, 200, 300, 0), box(220, 100, 320, 300, 1), box(340, 100, 440, 300, 2)]
-        by_track, _, counts = service.attach([{"track_id": 7, "box": [700, 100, 800, 300]}])
-        self.assertEqual(by_track[7]["state"], TAG_STATE_ABSENT)
-        self.assertEqual(counts[TAG_STATE_ABSENT], 1)
-
-    def test_unavailable_engine_plans_nothing(self):
-        service = TagService(NullOcr())
-        boxes = [box(100, 100, 200, 300, 0), box(220, 100, 320, 300, 1), box(340, 100, 440, 300, 2)]
-        self.assertEqual(service.plan(boxes, image().shape), [])
-        by_track, tags, _ = service.attach([{"track_id": 0, "box": [100, 100, 200, 300]}])
-        self.assertEqual(tags, [])
-        self.assertEqual(by_track[0]["state"], TAG_STATE_ABSENT)
-
-    def test_reset_clears_the_cache(self):
-        service, _ = self.make()
-        boxes = [box(100, 100, 200, 300, 0), box(220, 100, 320, 300, 1), box(340, 100, 440, 300, 2)]
-        array = image()
-        for band in service.plan(boxes, array.shape):
-            service.run_pass(array, band)
-        self.assertTrue(service.stats()["tags"] >= 1)
-        service.reset()
-        self.assertEqual(service.stats()["tags"], 0)
-
-    def test_a_new_shelf_reusing_row_zero_is_read(self):
-        """Screen row 0 on a newly panned shelf is not the row that settled."""
-        service, _ = self.make(pass_interval_frames=0, settle_observations=1, settle_passes=1)
-        boxes = [box(100, 100, 200, 300, 0), box(220, 100, 320, 300, 1), box(340, 100, 440, 300, 2)]
-        array = image()
-        for _ in range(3):
-            for band in service.plan(boxes, array.shape, shelf_offset=(0.0, 0.0)):
-                service.run_pass(array, band)
-        self.assertEqual(
-            service.plan(boxes, array.shape, shelf_offset=(0.0, 0.0)),
-            [],
-            "the same shelf row stays settled",
-        )
-        again = service.plan(boxes, array.shape, shelf_offset=(0.0, 400.0))
-        self.assertTrue(again, "a shelf 400 px away reuses the screen row and must be read")
-
-    def test_a_seen_tag_with_no_text_is_detected_not_absent(self):
-        def reader(crop):
-            return []
-
-        service = TagService(FakeOcr(reader=reader), settings=TagSettings(pass_interval_frames=0))
-        array = image()
-        # A bright paper block on a brick rail, under the product row. OCR reads nothing.
-        array[300:420, :] = (40, 40, 180)
-        array[330:390, 120:280] = (235, 235, 235)
-        boxes = [box(100, 100, 200, 300, 0), box(220, 100, 320, 300, 1), box(340, 100, 440, 300, 2)]
-        bands = service.plan(boxes, array.shape)
-        self.assertTrue(bands)
-        service.run_pass(array, bands[0])
-        by_track, tags, counts = service.attach([{"track_id": 0, "box": [100, 100, 200, 300]}])
-        self.assertTrue(tags, "the paper block is a tag even though OCR returned nothing")
-        self.assertEqual(tags[0]["state"], TAG_STATE_DETECTED)
-        self.assertEqual(by_track[0]["state"], TAG_STATE_DETECTED)
-        self.assertEqual(counts[TAG_STATE_DETECTED], 1)
-        self.assertNotEqual(by_track[0]["state"], TAG_STATE_ABSENT)
-
-    def test_a_tag_pass_survives_a_missing_header_pattern(self):
-        """Evaluation replay died with NameError: HEADER_PATTERN."""
-        import tag_cards
-        from tag_cards import OcrLine as CardLine
-
-        row = [CardLine(text="毎日お買得", box=(0, 0, 40, 16), confidence=0.9)]
-        saved = tag_cards.__dict__.pop("HEADER_PATTERN", None)
-        try:
-            score = tag_cards.tag_row_score(row)
-        finally:
-            if saved is not None:
-                tag_cards.HEADER_PATTERN = saved
-        self.assertGreater(score, 0, "a sale header still marks the row when the global is missing")
-        service, _ = self.make(pass_interval_frames=0)
-        boxes = [box(100, 100, 200, 300, 0), box(220, 100, 320, 300, 1), box(340, 100, 440, 300, 2)]
-        bands = service.plan(boxes, image().shape)
-        self.assertTrue(bands)
-        service.run_pass(image(), bands[0])
-        self.assertGreaterEqual(service.stats()["passes_run"], 1)
-        self.assertIsNone(service.stats()["last_pass_error"])
-
-    def test_a_queued_pass_keeps_the_shelf_it_was_planned_on(self):
-        """OCR that finishes after a pan must clear its own rail, not the new one."""
-        service, _ = self.make(pass_interval_frames=0, settle_observations=1, settle_passes=1)
-        boxes = [box(100, 100, 200, 300, 0), box(220, 100, 320, 300, 1), box(340, 100, 440, 300, 2)]
-        array = image()
-        planned = service.plan(boxes, array.shape, shelf_offset=(0.0, 0.0))
-        self.assertEqual(service.stats()["pending_bands"], 1)
-        moved = service.plan(boxes, array.shape, shelf_offset=(0.0, 400.0))
-        self.assertTrue(moved, "the new shelf is a different rail and must be queued too")
-        self.assertEqual(service.stats()["pending_bands"], 2)
-        service.run_pass(array, planned[0])
-        self.assertEqual(service.stats()["pending_bands"], 1, "the finished pass clears only its own key")
-        self.assertEqual(service._tags[next(iter(service._tags))].shelf_key[0], "shelf")
-        self.assertNotEqual(
-            service._tags[next(iter(service._tags))].shelf_key,
-            moved[0].key,
-            "the late read must not be filed under the shelf now in frame",
-        )
-        service.run_pass(array, moved[0])
-        self.assertEqual(service.stats()["pending_bands"], 0)
-        self.assertEqual(len({tag.shelf_key for tag in service._tags.values()}), 2)
-
-    def test_an_offscreen_tag_does_not_bind_the_new_facing(self):
-        """A pan that reuses screen coordinates must not hand the old tag to the new facing."""
-        service, _ = self.make(pass_interval_frames=0, settle_observations=1, settle_passes=1)
-        boxes = [box(100, 100, 200, 300, 0), box(220, 100, 320, 300, 1), box(340, 100, 440, 300, 2)]
-        array = image()
-        first = service.plan(boxes, array.shape, shelf_offset=(0.0, 0.0))
-        service.run_pass(array, first[0])
-        by_track, tags, _ = service.attach(
-            [{"track_id": 0, "box": [100, 100, 200, 300]}],
-            shelf_offset=(0.0, 0.0),
-            image_shape=array.shape,
-        )
-        self.assertEqual(by_track[0]["state"], TAG_STATE_READABLE)
-        self.assertEqual(by_track[0]["price"], 269)
-        self.assertGreaterEqual(tags[0]["box"][1], 0)
-        self.assertLess(tags[0]["box"][1], array.shape[0])
-
-        again = service.plan(boxes, array.shape, shelf_offset=(0.0, 400.0))
-        self.assertTrue(again)
-        by_track, tags, _ = service.attach(
-            [{"track_id": 9, "box": [100, 100, 200, 300]}],
-            shelf_offset=(0.0, 400.0),
-            image_shape=array.shape,
-        )
-        self.assertEqual(tags, [], "the old tag is above this frame")
-        self.assertEqual(by_track[9]["state"], TAG_STATE_ABSENT)
-        self.assertIsNone(by_track[9]["id"])
-
-    def test_attach_does_not_use_the_latest_plan_origin(self):
-        """A later plan must not project this attach through its stored origin."""
-        service, _ = self.make(pass_interval_frames=0, settle_observations=1, settle_passes=1)
-        boxes = [box(100, 100, 200, 300, 0), box(220, 100, 320, 300, 1), box(340, 100, 440, 300, 2)]
-        array = image()
-        first = service.plan(boxes, array.shape, shelf_offset=(0.0, 0.0))
-        service.run_pass(array, first[0])
-        service.plan(boxes, array.shape, shelf_offset=(0.0, 400.0))
-        by_track, tags, _ = service.attach(
-            [{"track_id": 0, "box": [100, 100, 200, 300]}],
-            image_shape=array.shape,
-        )
-        self.assertTrue(tags, "omitting shelf_offset is screen coordinates, not the panned plan origin")
-        self.assertEqual(by_track[0]["state"], TAG_STATE_READABLE)
-        by_track, tags, _ = service.attach(
-            [{"track_id": 0, "box": [100, 100, 200, 300]}],
-            shelf_offset=(0.0, 0.0),
-            image_shape=array.shape,
-        )
-        self.assertEqual(by_track[0]["state"], TAG_STATE_READABLE)
-
-
-class StorePolicyTests(unittest.TestCase):
-    def test_a_missing_file_uses_the_defaults(self):
-        policy = load_store_policy(None)
-        policy.active()
-        self.assertEqual(policy.tax_ratios, (1.08, 1.10))
-        self.assertEqual(policy.min_price, 50)
-        self.assertEqual(tag_cards.MIN_PRICE, 50)
-
-    def test_a_store_file_does_not_change_another_store(self):
-        import json
-        import tempfile
-
-        with tempfile.TemporaryDirectory() as root:
-            good = os.path.join(root, "lopia")
-            bad = os.path.join(root, "other")
-            os.makedirs(good)
-            os.makedirs(bad)
-            with open(os.path.join(good, "store.json"), "w", encoding="utf-8") as handle:
-                json.dump({"store_id": "lopia", "tax_ratios": [1.08], "min_price": 80}, handle)
-            with open(os.path.join(bad, "store.json"), "w", encoding="utf-8") as handle:
-                handle.write("{not json")
-            loaded = load_store_policy(good)
-            self.assertEqual(loaded.min_price, 80)
-            self.assertEqual(tag_cards.MIN_PRICE, 80)
-            with self.assertRaises(StorePolicyError) as raised:
-                load_store_policy(bad)
-            self.assertIn(bad, str(raised.exception))
-            self.assertEqual(tag_cards.MIN_PRICE, 80, "a bad file must not replace the store already loaded")
-            again = load_store_policy(good)
-            self.assertEqual(again.min_price, 80)
-
-    def test_a_ratio_outside_range_fails_clearly(self):
-        import json
-        import tempfile
-
-        load_store_policy(None)
-        with tempfile.TemporaryDirectory() as root:
-            with open(os.path.join(root, "store.json"), "w", encoding="utf-8") as handle:
-                json.dump({"tax_ratios": [2.5], "min_price": 999}, handle)
-            with self.assertRaises(StorePolicyError) as raised:
-                load_store_policy(root)
-            self.assertIn("tax ratio", str(raised.exception))
-        self.assertEqual(tag_cards.MIN_PRICE, 50)
-
-
-class EngineSelectionTests(unittest.TestCase):
-    def test_explicit_none_and_fake(self):
-        self.assertFalse(build_ocr_engine("none").available())
-        self.assertTrue(build_ocr_engine("fake").available())
-
-    def test_vision_unavailable_here_is_honest(self):
-        """On a machine without Vision the factory falls back rather than lying."""
-        engine = build_ocr_engine("auto")
-        if engine.available():
-            self.assertEqual(engine.name, "vision")
-        else:
-            self.assertIn(engine.name, ("none",))
-
-    def test_null_engine_returns_no_lines(self):
-        result = NullOcr().read(image())
-        self.assertEqual(result.lines, [])
-        self.assertEqual(result.engine, "none")
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/recognization/code/test_real_model.py b/recognization/code/test_real_model.py
deleted file mode 100644
index 7e3c05e..0000000
--- a/recognization/code/test_real_model.py
+++ /dev/null
@@ -1,122 +0,0 @@
-"""
-Opt-in integration test against the real model and the real catalog.
-
-Skipped unless DORAYAKI_REAL_MODEL=1, because it loads the YOLO weights and
-classifies a full shelf. It defends the end-to-end claim: the server detects the
-whole shelf, accepts a meaningful share of the products, keeps a label stable
-while the camera pans, and never names a SKU for a weak match.
-
-    cd recognization/code
-    DORAYAKI_REAL_MODEL=1 YOLO_OFFLINE=1 ../.venv/bin/python -m unittest test_real_model -v
-"""
-
-from __future__ import annotations
-
-import math
-import os
-import sys
-import unittest
-
-current_dir = os.path.dirname(os.path.abspath(__file__))
-project_root = os.path.dirname(current_dir)
-if os.path.exists(os.path.join(project_root, "stock")):
-    os.chdir(project_root)
-if current_dir not in sys.path:
-    sys.path.insert(0, current_dir)
-
-import cv2  # noqa: E402
-
-from engine import EngineSettings, build_default_engine  # noqa: E402
-from live import LiveSession, LiveSettings  # noqa: E402
-
-ENABLED = os.environ.get("DORAYAKI_REAL_MODEL") == "1"
-# Measured baseline on image.jpg (2026-09-24): 170 detections, 105 recognized.
-BASELINE_DETECTIONS = 170
-BASELINE_RECOGNIZED = 105
-
-
-@unittest.skipUnless(ENABLED, "set DORAYAKI_REAL_MODEL=1 to run against the real model")
-class RealModelTests(unittest.TestCase):
-    @classmethod
-    def setUpClass(cls):
-        cls.engine = build_default_engine(EngineSettings(device="cpu"))
-        cls.engine.warm_up()
-
-    @classmethod
-    def tearDownClass(cls):
-        cls.engine.shutdown()
-
-    def test_recognize_whole_shelf(self):
-        with open("image.jpg", "rb") as handle:
-            payload = handle.read()
-        image = self.engine.decode_image(payload)
-        result = self.engine.recognize(image, classify=True)
-        summary = result["summary"]
-        self.assertGreaterEqual(
-            summary["in_view"], int(BASELINE_DETECTIONS * 0.95),
-            "detection must not regress below the measured baseline",
-        )
-        self.assertGreaterEqual(
-            summary["recognized"], int(BASELINE_RECOGNIZED * 0.9),
-            "recognition must not regress below the measured baseline",
-        )
-        # every named SKU must exist in the catalog
-        catalog = {product["sku_id"] for product in self.engine.catalog()}
-        for detection in result["detections"]:
-            identity = detection["identity"]
-            if identity["state"] == "recognized":
-                self.assertIn(identity["sku_id"], catalog)
-            else:
-                self.assertIsNone(identity["sku_id"], "only an accepted match may name a SKU")
-
-    def test_pan_keeps_labels_stable_and_advances_them(self):
-        image = cv2.imread("image.jpg")
-        height, width = image.shape[:2]
-        frame_w, frame_h = 1280, 720
-        session = LiveSession("real", self.engine, settings=LiveSettings(method="hybrid"))
-        session.refresh_catalog()
-
-        labels: dict = {}
-        relabels: dict = {}
-        restarts: dict = {}
-        unexplained = []
-        recognized_frames = 0
-        frames = 16
-        for index in range(frames):
-            phase = index / (frames - 1)
-            x = int((width - frame_w) * (0.5 - 0.5 * math.cos(2 * math.pi * phase)))
-            y = max(0, (height - frame_h) // 2)
-            window = image[y:y + frame_h, x:x + frame_w]
-            ok, buffer = cv2.imencode(".jpg", window, [cv2.IMWRITE_JPEG_QUALITY, 75])
-            session.submit_frame(index, buffer.tobytes())
-            pending = session.take_pending(timeout=5.0)
-            self.assertIsNotNone(pending)
-            result = session.process_frame(*pending)
-            import time
-
-            time.sleep(0.4)  # let classification land, as a live client's next frame would
-
-            if result["summary"]["recognized"]:
-                recognized_frames += 1
-            for track in result["tracks"]:
-                identity = track["identity"]
-                if identity["state"] != "recognized":
-                    continue
-                track_id = track["track_id"]
-                sku = identity["sku_id"]
-                if restarts.get(track_id, 0) < track["restarts"]:
-                    labels.pop(track_id, None)
-                if track_id in labels and labels[track_id] != sku:
-                    if track["relabels"] <= relabels.get(track_id, 0):
-                        unexplained.append((track_id, labels[track_id], sku))
-                labels[track_id] = sku
-                relabels[track_id] = track["relabels"]
-                restarts[track_id] = track["restarts"]
-
-        self.assertGreater(recognized_frames, frames // 2, "labels should appear on most frames")
-        self.assertGreater(len(labels), 0, "the pan should label products")
-        self.assertEqual(unexplained, [], "a label must never change without re-verification")
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/recognization/code/test_rebuild_catalog.py b/recognization/code/test_rebuild_catalog.py
deleted file mode 100644
index 6d10dfe..0000000
--- a/recognization/code/test_rebuild_catalog.py
+++ /dev/null
@@ -1,382 +0,0 @@
-"""
-Contract tests for the enrollment tool's gates.
-
-The refusal rules (size floors including the pyramid floor, session leakage,
-name/OCR, duplicates) are what the tool exists for, and they are pinned here
-against the measured distribution rather than against invented numbers. The
-two checks that report rather than refuse are covered too, because both are
-properties of the catalog as a whole and neither is visible from a single
-view: cross-SKU near-duplicates, and the completeness rule that decides whether
-a bay is live-ready.
-"""
-
-from __future__ import annotations
-
-import os
-import sys
-import tempfile
-import unittest
-
-current_dir = os.path.dirname(os.path.abspath(__file__))
-if current_dir not in sys.path:
-    sys.path.insert(0, current_dir)
-
-import cv2  # noqa: E402
-import numpy as np  # noqa: E402
-
-from rebuild_catalog import (  # noqa: E402
-    MIN_NAME_OCR,
-    PYRAMID_FLOOR,
-    RebuildError,
-    audit,
-    live_ready_verdict,
-    load_shelf_skus,
-    name_ocr_similarity,
-    text_discriminated_siblings,
-)
-
-
-def artwork(mark=None):
-    """A package face; `mark` adds the small text that separates siblings."""
-    image = np.full((400, 300, 3), 200, dtype="uint8")
-    cv2.rectangle(image, (20, 20), (280, 380), (40, 40, 180), -1)
-    cv2.putText(image, "MAYO", (60, 200), cv2.FONT_HERSHEY_SIMPLEX, 2.0, (255, 255, 255), 4)
-    if mark:
-        cv2.putText(image, mark, (60, 300), cv2.FONT_HERSHEY_SIMPLEX, 1.2, (255, 255, 255), 3)
-    return image
-
-
-class SiblingFlagTests(unittest.TestCase):
-    """The near-unknown case: same artwork, different product."""
-
-
-    def make_source(self, images):
-        root = tempfile.mkdtemp(prefix="siblings-")
-        for name, image in images.items():
-            cv2.imwrite(os.path.join(root, name), image)
-        return root
-
-    def test_shared_artwork_across_skus_is_flagged(self):
-        root = self.make_source({"sku_x1.jpg": artwork(), "sku_x2.jpg": artwork("HALF")})
-        flagged = text_discriminated_siblings({"sku_x1": ["sku_x1.jpg"], "sku_x2": ["sku_x2.jpg"]}, root)
-        self.assertEqual(len(flagged), 1, flagged)
-        self.assertEqual(flagged[0]["sku"], "sku_x1")
-        self.assertEqual(flagged[0]["sibling_sku"], "sku_x2")
-        self.assertGreater(flagged[0]["cosine"], 0.98)
-
-    def test_distinct_artwork_is_not_flagged(self):
-        other = np.full((400, 300, 3), 30, dtype="uint8")
-        cv2.circle(other, (150, 200), 90, (200, 200, 40), -1)
-        root = self.make_source({"sku_x1.jpg": artwork(), "sku_x2.jpg": other})
-        self.assertEqual(
-            text_discriminated_siblings({"sku_x1": ["sku_x1.jpg"], "sku_x2": ["sku_x2.jpg"]}, root), []
-        )
-
-    def test_two_views_of_one_sku_are_not_siblings(self):
-        """The same-SKU duplicate rule already covers these; this flag is cross-SKU."""
-        root = self.make_source({"sku_x1.jpg": artwork(), "sku_x1_2.jpg": artwork()})
-        self.assertEqual(
-            text_discriminated_siblings({"sku_x1": ["sku_x1.jpg", "sku_x1_2.jpg"]}, root), []
-        )
-
-
-class PyramidFloorTests(unittest.TestCase):
-    """The l09 defect: a 305 px view whose scores dwarf every other reference.
-
-    Refused, not reported. Reporting it is what let the defect ship - the view
-    enrolled anyway, because a line in a catalog README is not a gate.
-    """
-
-    def make_source(self, height):
-        root = tempfile.mkdtemp(prefix="floor-")
-        image = cv2.resize(artwork(), (300, height), interpolation=cv2.INTER_NEAREST)
-        cv2.imwrite(os.path.join(root, "sku_x1.jpg"), image)
-        return root
-
-    def test_a_view_under_the_floor_is_refused(self):
-        kept, refused = audit(self.make_source(305), min_views=1)
-        self.assertEqual(kept, {}, refused)
-        self.assertEqual(len(refused), 1, refused)
-        self.assertIn("305px", refused[0]["reason"])
-        self.assertIn("pyramid floor", refused[0]["reason"])
-
-    def test_the_floor_is_exact(self):
-        """799 refused, 800 kept: the boundary is the number, not a rounding."""
-        under = audit(self.make_source(PYRAMID_FLOOR - 1), min_views=1)
-        self.assertEqual(under[0], {}, under[1])
-        at = audit(self.make_source(PYRAMID_FLOOR), min_views=1)
-        self.assertEqual(at[0], {"sku_x1": ["sku_x1.jpg"]}, at[1])
-        self.assertEqual(at[1], [])
-
-    def test_a_full_size_view_still_enrolls(self):
-        kept, refused = audit(self.make_source(1000), min_views=1)
-        self.assertEqual(kept, {"sku_x1": ["sku_x1.jpg"]}, refused)
-        self.assertEqual(refused, [])
-
-
-class NameOcrTests(unittest.TestCase):
-    """The wrong-product gate: a packshot's OCR must explain its catalog row.
-
-    Pinned to the measured distribution, not to invented numbers. The reads are
-    what the vision backend returned for the shipped packshots and for the
-    rejected Kewpie 450g; recording them keeps the test on the pure function
-    instead of on Vision's recall, which moves with the macOS version.
-    """
-
-    # Worst correct packshot in stock-lopia-clean: the l11 read carries
-    # "キューピー ハーフ" and "400g" but never "マヨネーズ".
-    WORST_CORRECT = (
-        "キューピー ハーフマヨネーズ 400g",
-        "2 キューピーハーフ New！ 卵黄ならではのコクとうま味 "
-        "※写真はイメージです。 カロリー 50%ット 当社マヨネーズ比 400g",
-        0.9167,
-    )
-    # The recorded l09/l10 mix-up: Kewpie Mayonnaise 450g, filed under an
-    # Ajinomoto row. This is its best score against any of the 12 catalog names.
-    WRONG_PRODUCT = (
-        "キューピー からしマヨネーズ 200g",
-        "キューピーマヨネーズ KEWPIE MAYONNAISE ® 450g",
-        0.5833,
-    )
-
-    def test_a_correct_packshot_clears_the_gate(self):
-        name, text, measured = self.WORST_CORRECT
-        self.assertAlmostEqual(name_ocr_similarity(name, text), measured, places=3)
-        self.assertGreaterEqual(name_ocr_similarity(name, text), MIN_NAME_OCR)
-
-    def test_the_wrong_size_packshot_is_refused(self):
-        """The check that exists to stop the l09/l10 mix-up keeps its teeth."""
-        name, text, measured = self.WRONG_PRODUCT
-        self.assertAlmostEqual(name_ocr_similarity(name, text), measured, places=3)
-        self.assertLess(name_ocr_similarity(name, text), MIN_NAME_OCR)
-
-    def test_the_threshold_sits_between_the_two_measured_bands(self):
-        """The recalibration pin. Lower it and the wrong product enrolls."""
-        worst_correct = name_ocr_similarity(*self.WORST_CORRECT[:2])
-        best_wrong = name_ocr_similarity(*self.WRONG_PRODUCT[:2])
-        self.assertGreater(worst_correct, best_wrong, "the bands overlap; no threshold separates them")
-        self.assertGreaterEqual(MIN_NAME_OCR, best_wrong)
-        self.assertLessEqual(MIN_NAME_OCR, worst_correct)
-        self.assertGreaterEqual(MIN_NAME_OCR - best_wrong, 0.1, "no headroom below the wrong-product ceiling")
-        self.assertGreaterEqual(worst_correct - MIN_NAME_OCR, 0.1, "no headroom above the worst correct packshot")
-
-    def test_a_maker_prefix_gap_does_not_admit_a_wrong_row(self):
-        """l07: its product tokens all read, its maker and size do not.
-
-        The catalog names l07 "丸和油脂 ラッキーマヨネーズ 200g" and the packshot
-        reads "ラッキー マヨネーズ 調味料（アミノ酸）不使用" - every product bigram,
-        none of the maker or the size. The 0.5714 it scores is inside the
-        wrong-product band and stays refused: the gap is a name-token problem,
-        not a threshold to move. Scoping the check to the product tokens would
-        admit it; that is a change to this gate, not a reason to lower it.
-        """
-        score = name_ocr_similarity("丸和油脂 ラッキーマヨネーズ 200g",
-                                    "ラッキー マヨネーズ 調味料（アミノ酸）不使用")
-        self.assertAlmostEqual(score, 0.5714, places=3)
-        self.assertLess(score, MIN_NAME_OCR)
-
-    def test_audit_refuses_a_packshot_the_name_never_supports(self):
-        """l04 at 0.00: the upright read is three stray characters."""
-        root = tempfile.mkdtemp(prefix="name-ocr-")
-        cv2.imwrite(os.path.join(root, "sku_x1.jpg"),
-                    cv2.resize(artwork(), (300, 800), interpolation=cv2.INTER_NEAREST))
-
-        class FixedOcr:
-            name = "fixed"
-
-            def read(self, image):
-                class Line:
-                    text = "明太手屋さんが つくつ た コ"
-
-                class Read:
-                    lines = [Line()]
-
-                return Read()
-
-        kept, refused = audit(root, min_views=1,
-                              names={"sku_x1": "やまや めんたいマヨネーズタイプ 200g"},
-                              ocr_engine=FixedOcr())
-        self.assertEqual(kept, {}, refused)
-        self.assertEqual(len(refused), 1, refused)
-        self.assertIn("name/OCR", refused[0]["reason"])
-
-
-class CompletenessVerdictTests(unittest.TestCase):
-    """The enrollment protocol's completeness rule, as a verdict."""
-
-    def test_without_a_shelf_list_the_verdict_is_unknown(self):
-        """Not a guess: the rule needs the shelf's SKU list to mean anything."""
-        verdict = live_ready_verdict({"sku_a": ["a.jpg"]}, {"sku_a": "A"}, None)
-        self.assertIsNone(verdict["live_ready"])
-        self.assertIn("shelf", verdict["reason"])
-
-    def test_every_shelf_sku_enrolled_is_live_ready(self):
-        verdict = live_ready_verdict({"sku_a": ["a.jpg"]}, {"sku_a": "A"}, {"sku_a"})
-        self.assertTrue(verdict["live_ready"])
-
-    def test_a_text_only_row_satisfies_the_rule(self):
-        """A CSV row with no image still names and vetoes from its text."""
-        verdict = live_ready_verdict({"sku_a": ["a.jpg"]}, {"sku_a": "A", "sku_b": "B"}, {"sku_a", "sku_b"})
-        self.assertTrue(verdict["live_ready"])
-
-    def test_a_shelf_sku_absent_from_the_catalog_fails(self):
-        verdict = live_ready_verdict({"sku_a": ["a.jpg"]}, {"sku_a": "A"}, {"sku_a", "sku_z"})
-        self.assertFalse(verdict["live_ready"])
-        self.assertEqual(verdict["missing_from_catalog"], ["sku_z"])
-
-
-class ShelfTagListTests(unittest.TestCase):
-    def write(self, text):
-        path = os.path.join(tempfile.mkdtemp(prefix="tags-"), "tags.json")
-        with open(path, "w", encoding="utf-8") as handle:
-            handle.write(text)
-        return path
-
-    def test_a_json_list_is_read(self):
-        self.assertEqual(load_shelf_skus(self.write('["sku_a", "sku_b"]')), {"sku_a", "sku_b"})
-
-    def test_one_sku_per_line_is_read(self):
-        self.assertEqual(load_shelf_skus(self.write("sku_a\nsku_b\n")), {"sku_a", "sku_b"})
-
-    def test_no_path_means_no_list(self):
-        self.assertIsNone(load_shelf_skus(None))
-
-    def test_a_json_object_is_refused(self):
-        with self.assertRaises(RebuildError):
-            load_shelf_skus(self.write('{"sku_a": 1}'))
-
-
-class BadShelfListFailsBeforeTheSwapTests(unittest.TestCase):
-    """A bad --shelf-tags must not leave a replaced catalog behind.
-
-    The list is read before write_catalog runs, so a malformed file or a missing
-    path fails with the tool's own message and the destination is untouched. Read
-    after the swap, the same error would escape as a traceback over a catalog
-    that had already been replaced.
-    """
-
-    def make_source(self):
-        root = tempfile.mkdtemp(prefix="guard-src-")
-        cv2.imwrite(os.path.join(root, "sku_x1.jpg"), artwork())
-        with open(os.path.join(root, "stock.csv"), "w", encoding="utf-8") as handle:
-            handle.write("SKU_ID,product_name,price,number_in,number_out,number_on\n")
-            handle.write("sku_x1,Alpha,100,1,0,1\n")
-        return root
-
-    def run_tool(self, shelf_tags):
-        from rebuild_catalog import main
-
-        source = self.make_source()
-        output = os.path.join(tempfile.mkdtemp(prefix="guard-out-"), "catalog")
-        with self.assertRaises(SystemExit) as caught:
-            main(["--source", source, "--output", output, "--csv",
-                  os.path.join(source, "stock.csv"), "--min-views", "1",
-                  "--ocr", "none", "--shelf-tags", shelf_tags])
-        return str(caught.exception), output
-
-    def test_a_malformed_list_fails_cleanly_and_writes_nothing(self):
-        path = os.path.join(tempfile.mkdtemp(prefix="bad-tags-"), "tags.json")
-        with open(path, "w", encoding="utf-8") as handle:
-            handle.write('{"sku_x1": 1}')
-        message, output = self.run_tool(path)
-        self.assertIn("expected a JSON list", message)
-        self.assertFalse(os.path.exists(output), "the catalog was replaced before the check")
-
-    def test_a_missing_list_fails_cleanly_and_writes_nothing(self):
-        message, output = self.run_tool("/tmp/definitely-not-a-shelf-list.json")
-        self.assertIn("No such file", message)
-        self.assertFalse(os.path.exists(output), "the catalog was replaced before the check")
-
-
-class OrientationTests(unittest.TestCase):
-    """A packshot whose identifying text is not horizontal.
-
-    The やまや めんたいマヨネーズ packshot reads its brand only when rotated;
-    the upright pass returns stray kana. Checking one orientation refused a
-    correct image, which is the worst failure this check has - it rejects good
-    data while claiming to validate it.
-    """
-    class FakeOcr:
-        """Reads a fixed string per rotation.
-
-        Rotations are identified by aspect ratio, not by exact shape: the
-        caller upscales before OCR, so the fake sees scaled dimensions. The
-        upright and 180-degree passes keep the source's orientation (wide),
-        the two 90-degree passes swap it (tall). A corner marker cannot tell
-        them apart, because `cv2.rotate` carries the corner value through.
-        """
-
-        name = "fake"
-
-        def __init__(self, by_orientation):
-            self.by_orientation = by_orientation
-
-        def read(self, image):
-            class Line:
-                def __init__(self, text):
-                    self.text = text
-
-            class Read:
-                def __init__(self, text):
-                    self.lines = [Line(part) for part in text.split("|") if part]
-
-            height, width = image.shape[:2]
-            key = "wide" if width > height else "tall"
-            return Read(self.by_orientation.get(key, ""))
-
-    def marked(self, height, width):
-        return np.zeros((height, width, 3), dtype="uint8")
-
-    def test_a_rotated_read_wins_when_it_matches_the_name(self):
-        """The upright pass reads noise; the rotated pass reads the product."""
-        from rebuild_catalog import best_orientation_text
-
-        engine = self.FakeOcr({"wide": "つくつ|た", "tall": "明太手屋さんが|マヨネーズ"})
-        text = best_orientation_text(engine, self.marked(40, 60), "やまや めんたいマヨネーズタイプ 200g")
-        self.assertIn("明太手屋さんが", text)
-
-    def test_the_upright_read_wins_when_it_matches(self):
-        from rebuild_catalog import best_orientation_text
-
-        engine = self.FakeOcr({"wide": "ピュアセレクト|マヨネーズ|450g", "tall": "ノイズ"})
-        text = best_orientation_text(engine, self.marked(40, 60), "ピュアセレクト マヨネーズ 450g")
-        self.assertIn("ピュアセレクト", text)
-
-    def test_without_a_name_the_longest_read_wins(self):
-        from rebuild_catalog import best_orientation_text
-
-        engine = self.FakeOcr({"wide": "ab", "tall": "abcdefgh"})
-        self.assertEqual(best_orientation_text(engine, self.marked(40, 60)), "abcdefgh")
-
-    def test_audit_keeps_a_view_whose_name_only_reads_rotated(self):
-        """The regression: `audit` must try every orientation before refusing.
-
-        This is the boundary that matters. `best_orientation_text` passing its
-        own unit test proves nothing about enrollment - the bug was that
-        `audit` called a single-orientation read, so a correct packshot whose
-        brand text runs vertically was refused as a name/OCR mismatch.
-        """
-        from rebuild_catalog import audit
-
-        root = tempfile.mkdtemp(prefix="orient-")
-        # Wider than tall, so the upright pass is the "wide" read (noise) and
-        # only a 90-degree rotation produces the "tall" read that matches.
-        cv2.imwrite(os.path.join(root, "sku_x1.jpg"), self.marked(800, 1000))
-        engine = self.FakeOcr({"wide": "つくつ|た", "tall": "キユーピー|エッグケア|305g"})
-        kept, refused = audit(root, min_views=1, names={"sku_x1": "キューピー エッグケア 305g"},
-                              ocr_engine=engine)
-        self.assertEqual(kept, {"sku_x1": ["sku_x1.jpg"]}, refused)
-        self.assertEqual(refused, [])
-
-    def test_audit_still_refuses_a_view_the_name_never_matches(self):
-        """The check must keep its teeth: a wrong product is still refused."""
-        from rebuild_catalog import audit
-
-        root = tempfile.mkdtemp(prefix="orient-bad-")
-        cv2.imwrite(os.path.join(root, "sku_x1.jpg"), self.marked(800, 300))
-        engine = self.FakeOcr({"wide": "キューピー|マヨネーズ", "tall": "キューピー|マヨネーズ"})
-        kept, refused = audit(root, min_views=1, names={"sku_x1": "やまや めんたいマヨネーズタイプ 200g"},
-                              ocr_engine=engine)
-        self.assertEqual(kept, {})
-        self.assertEqual(len(refused), 1, refused)
-        self.assertIn("name/OCR", refused[0]["reason"])
diff --git a/recognization/code/test_reconcile.py b/recognization/code/test_reconcile.py
deleted file mode 100644
index 6ca84bf..0000000
--- a/recognization/code/test_reconcile.py
+++ /dev/null
@@ -1,363 +0,0 @@
-"""
-Contract tests for reconciliation and the compliance rule family.
-
-Pure functions over evidence, so these run without OCR, weights, or a camera.
-The rules that matter most here are the fail-closed ones: unreadable evidence
-must never become a violation.
-"""
-
-from __future__ import annotations
-
-import os
-import sys
-import unittest
-
-current_dir = os.path.dirname(os.path.abspath(__file__))
-if current_dir not in sys.path:
-    sys.path.insert(0, current_dir)
-
-from reconcile import (  # noqa: E402
-    ComplianceSettings,
-    ComplianceTracker,
-    RECONCILED_CONFIRMED,
-    RECONCILED_MISMATCH,
-    RECONCILED_NO_TAG,
-    RECONCILED_TAG_ONLY,
-    RECONCILED_UNVERIFIED,
-    RULE_MISSING_TAG,
-    RULE_MISSING_TAG_GROUP,
-    RULE_ORPHAN_TAG,
-    RULE_PRICE_MISMATCH,
-    RULE_UNREADABLE_TAGS,
-    evaluate_compliance,
-    longest_common_substring_ratio,
-    normalize_name,
-    reconcile,
-    summarize_findings,
-)
-from tags import TAG_STATE_ABSENT, TAG_STATE_READABLE, TAG_STATE_UNREADABLE  # noqa: E402
-
-
-def accepted(sku="sku_003"):
-    return {"state": "recognized", "sku_id": sku, "identity_level": "accepted", "confidence": 0.9}
-
-
-def tag(state=TAG_STATE_READABLE, **overrides):
-    payload = {
-        "id": "tag_1",
-        "box": [100, 300, 200, 340],
-        "state": state,
-        "price": None,
-        "regular_price": None,
-        "name": None,
-        "barcode": None,
-        "flags": [],
-        "confidence": 0.8,
-        "observations": 3,
-        "settled": True,
-    }
-    payload.update(overrides)
-    return payload
-
-
-def product(price=168.0, name="Tropicana エッセンシャルズ マルチビタミン", jan=None):
-    payload = {"sku_id": "sku_003", "product_name": name, "price": price}
-    if jan:
-        payload["jan"] = jan
-    return payload
-
-
-class NameMatchingTests(unittest.TestCase):
-    def test_normalization_strips_noise(self):
-        self.assertEqual(normalize_name("Ｔｒｏｐｉｃａｎａ エッセンシャルズ"), "tropicanaエッセンシャルズ")
-        self.assertEqual(normalize_name("キュービー<310g>"), "キュービー")
-
-    def test_lcs_ratio(self):
-        self.assertEqual(longest_common_substring_ratio("tropicana", "tropicana"), 1.0)
-        self.assertGreater(longest_common_substring_ratio("tropicanaエッセンシャルズ", "tropicana"), 0.5)
-        self.assertEqual(longest_common_substring_ratio("", "x"), 0.0)
-
-
-class ReconcileTests(unittest.TestCase):
-    def test_price_agreement_confirms(self):
-        result = reconcile(accepted(), tag(price=168), product(price=168.0))
-        self.assertEqual(result["state"], RECONCILED_CONFIRMED)
-        self.assertTrue(any(signal["kind"] == "price" and signal["agrees"] for signal in result["signals"]))
-
-    def test_regular_price_agreement_confirms(self):
-        result = reconcile(accepted(), tag(price=140, regular_price=168), product(price=168.0))
-        self.assertEqual(result["state"], RECONCILED_CONFIRMED)
-
-    def test_price_disagreement_is_a_mismatch_with_both_values(self):
-        result = reconcile(accepted(), tag(price=269), product(price=168.0))
-        self.assertEqual(result["state"], RECONCILED_MISMATCH)
-        self.assertIn("269", result["message"])
-        self.assertIn("168", result["message"])
-
-    def test_name_agreement_confirms_even_without_a_price(self):
-        result = reconcile(accepted(), tag(name="Tropicana エッセンシャルズ"), product(price=None, name="Tropicana エッセンシャルズ マルチビタミン"))
-        self.assertEqual(result["state"], RECONCILED_CONFIRMED)
-
-    def test_barcode_disagreement_is_a_mismatch(self):
-        result = reconcile(accepted(), tag(price=168, barcode="4902105000001"),
-                           product(price=168.0, jan="4902105000002"))
-        self.assertEqual(result["state"], RECONCILED_MISMATCH)
-
-    def test_barcode_agreement_confirms(self):
-        result = reconcile(accepted(), tag(price=None, barcode="4902105000001"),
-                           product(price=None, jan="4902105000001"))
-        self.assertEqual(result["state"], RECONCILED_CONFIRMED)
-
-    def test_tag_read_but_nothing_comparable(self):
-        result = reconcile(accepted(), tag(name="コーヒー"), product(price=None, name=None))
-        self.assertEqual(result["state"], RECONCILED_UNVERIFIED)
-
-    def test_unreadable_tag_is_unverified_not_missing(self):
-        result = reconcile(accepted(), tag(state=TAG_STATE_UNREADABLE), product())
-        self.assertEqual(result["state"], RECONCILED_UNVERIFIED)
-
-    def test_absent_tag(self):
-        result = reconcile(accepted(), tag(state=TAG_STATE_ABSENT), product())
-        self.assertEqual(result["state"], RECONCILED_NO_TAG)
-
-    def test_readable_tag_without_a_product(self):
-        result = reconcile(None, tag(price=269), None)
-        self.assertEqual(result["state"], RECONCILED_TAG_ONLY)
-
-    def test_nothing_to_reconcile_returns_none(self):
-        self.assertIsNone(reconcile(None, None, None))
-        self.assertIsNone(reconcile({"state": "unconfirmed"}, tag(state=TAG_STATE_ABSENT), product()))
-
-
-# --------------------------------------------------------------------------- compliance
-
-
-def band(row_index, box):
-    class Band:
-        pass
-
-    instance = Band()
-    instance.row_index = row_index
-    instance.box = box
-    return instance
-
-
-def track(track_id, box, sku="sku_003", state="recognized"):
-    return {
-        "track_id": track_id,
-        "box": box,
-        "raw_box": box,
-        "identity": {"state": state, "sku_id": sku if state == "recognized" else None},
-    }
-
-
-# Wide enough that separate products can sit apart in a row.
-BANDS = [band(1, [0, 300, 3000, 340]), band(2, [0, 600, 3000, 640])]
-
-
-class ComplianceTests(unittest.TestCase):
-    def test_missing_tag_on_a_covered_row_is_a_violation(self):
-        # Four separate products (wide gaps), three with tag evidence - 75%, above
-        # the 70% bar - and one with none.
-        tracks = [
-            track(index, [10 + index * 300, 100, 140 + index * 300, 300], sku=f"sku_{index}")
-            for index in range(4)
-        ]
-        tags = {
-            0: tag(state=TAG_STATE_READABLE, price=168, id="tag_a"),
-            1: tag(state=TAG_STATE_READABLE, price=178, id="tag_b"),
-            2: tag(state=TAG_STATE_ABSENT),
-            3: tag(state=TAG_STATE_READABLE, price=188, id="tag_c"),
-        }
-        findings = evaluate_compliance(tracks, tags, [tags[0], tags[1], tags[3]], BANDS)
-        rules = [finding.rule for finding in findings]
-        self.assertIn(RULE_MISSING_TAG, rules)
-        missing = [finding for finding in findings if finding.rule == RULE_MISSING_TAG][0]
-        self.assertEqual(missing.count, 1, "one finding per row, counting the products")
-
-    def test_a_row_with_poor_tag_coverage_is_not_evaluated(self):
-        """The real failure mode: our detector found tags for only a few of many
-        facings. Claiming the rest are missing would be a false violation."""
-        tracks = [track(index, [10 + index * 300, 100, 90 + index * 300, 300]) for index in range(6)]
-        tags = {index: tag(state=TAG_STATE_ABSENT) for index in range(6)}
-        tags[0] = tag(state=TAG_STATE_READABLE, price=168, id="tag_a")
-        findings = evaluate_compliance(tracks, tags, [tags[0]], BANDS)
-        self.assertEqual([finding.rule for finding in findings], [RULE_UNREADABLE_TAGS])
-        self.assertEqual(findings[0].severity, "hint")
-        self.assertEqual(summarize_findings(findings)["violations"], 0)
-
-    def test_unreadable_row_yields_a_hint_never_a_violation(self):
-        """The demo image case: tags exist but cannot be read. Nothing may be claimed."""
-        tracks = [
-            track(1, [10, 100, 90, 300]),
-            track(2, [110, 100, 190, 300]),
-            track(3, [210, 100, 290, 300]),
-        ]
-        tags = {
-            1: tag(state=TAG_STATE_UNREADABLE, price=None, id="tag_1"),
-            2: tag(state=TAG_STATE_UNREADABLE, price=None, id="tag_2"),
-            3: tag(state=TAG_STATE_UNREADABLE, price=None, id="tag_3"),
-        }
-        findings = evaluate_compliance(tracks, tags, [tags[1], tags[2], tags[3]], BANDS)
-        self.assertEqual([finding.rule for finding in findings], [RULE_UNREADABLE_TAGS])
-        self.assertEqual(findings[0].severity, "hint")
-        self.assertEqual(summarize_findings(findings)["violations"], 0)
-
-    def test_a_row_with_no_tag_evidence_at_all_is_silent(self):
-        tracks = [track(1, [10, 100, 90, 300])]
-        tags = {1: tag(state=TAG_STATE_ABSENT)}
-        findings = evaluate_compliance(tracks, tags, [], BANDS)
-        self.assertEqual(findings, [])
-
-    def test_group_of_consecutive_facings_without_tags(self):
-        # Coverage counts *products* with any tag evidence, so the row needs enough
-        # of it to be judged at all (bar is 70%: 7 of 10 products).
-        tracks = [
-            track(index, [10 + index * 300, 100, 70 + index * 300, 300], sku=f"sku_{index}")
-            for index in range(10)
-        ]
-        tags = {index: tag(state=TAG_STATE_READABLE, price=168 + index, id=f"tag_{index}") for index in range(10)}
-        for absent in (2, 3, 4):
-            tags[absent] = tag(state=TAG_STATE_ABSENT, id=None)
-        findings = evaluate_compliance(tracks, tags, [t for t in tags.values() if t["id"]], BANDS)
-        groups = [finding for finding in findings if finding.rule == RULE_MISSING_TAG_GROUP]
-        self.assertEqual(len(groups), 1)
-        self.assertEqual(groups[0].count, 3)
-        self.assertEqual(groups[0].row_index, 1)
-        self.assertIn("3 products", groups[0].detail)
-
-    def test_a_product_whose_tag_sits_elsewhere_in_the_row_is_not_missing(self):
-        """Measured false positive: a run of ピュアセレクト facings was split by
-        geometry, and the piece was reported as missing the tag that was sitting
-        further along its own run. Identity and the row's tags both guard it."""
-        tracks = [
-            track(0, [10, 100, 160, 300], sku="sku_a"),
-            track(1, [170, 100, 320, 300], sku="sku_a"),
-            track(2, [330, 100, 460, 300], sku="sku_a"),
-        ]
-        tags = {
-            0: tag(state=TAG_STATE_READABLE, price=209, id="tag_a"),
-            1: tag(state=TAG_STATE_ABSENT),
-            2: tag(state=TAG_STATE_ABSENT),
-        }
-        findings = evaluate_compliance(tracks, tags, [tags[0]], BANDS)
-        self.assertEqual(findings, [], "the product's tag is on its own run")
-
-    def test_two_missing_products_are_not_a_group(self):
-        tracks = [
-            track(index, [10 + index * 300, 100, 140 + index * 300, 300], sku=f"sku_{index}")
-            for index in range(4)
-        ]
-        tags = {
-            0: tag(state=TAG_STATE_READABLE, price=168, id="tag_a"),
-            1: tag(state=TAG_STATE_ABSENT),
-            2: tag(state=TAG_STATE_READABLE, price=188, id="tag_b"),
-            3: tag(state=TAG_STATE_READABLE, price=198, id="tag_c"),
-        }
-        findings = evaluate_compliance(tracks, tags, [tags[0], tags[2], tags[3]], BANDS)
-        self.assertNotIn(RULE_MISSING_TAG_GROUP, [finding.rule for finding in findings])
-        missing = [finding for finding in findings if finding.rule == RULE_MISSING_TAG]
-        self.assertEqual(len(missing), 1)
-        self.assertEqual(missing[0].count, 1)
-
-    def test_facings_of_one_product_share_a_tag(self):
-        """The measured domain rule: this store prints one tag per product, not per
-        facing - a row carries ~16 facings but only ~4 tags. Counting facings would
-        accuse a perfectly labelled shelf."""
-        # Four facings of the same product (touching, same size) under one tag.
-        tracks = [track(index, [10 + index * 80, 100, 90 + index * 80, 300]) for index in range(4)]
-        tags = {
-            0: tag(state=TAG_STATE_READABLE, price=269, id="tag_a"),
-            1: tag(state=TAG_STATE_ABSENT),
-            2: tag(state=TAG_STATE_ABSENT),
-            3: tag(state=TAG_STATE_ABSENT),
-        }
-        findings = evaluate_compliance(tracks, tags, [tags[0]], BANDS)
-        self.assertEqual(findings, [], "one tag covers the whole product: nothing is wrong")
-
-    def test_orphan_tag(self):
-        tracks = [track(1, [10, 100, 90, 300])]
-        tags = {1: tag(state=TAG_STATE_READABLE, price=168, id="tag_a")}
-        all_tags = [tags[1], tag(state=TAG_STATE_READABLE, price=999, id="tag_lonely", box=[300, 600, 400, 640])]
-        findings = evaluate_compliance(tracks, tags, all_tags, BANDS)
-        orphans = [finding for finding in findings if finding.rule == RULE_ORPHAN_TAG]
-        self.assertEqual(len(orphans), 1)
-        self.assertEqual(orphans[0].box, [300, 600, 400, 640])
-
-    def test_price_mismatch_becomes_a_finding(self):
-        # A reportable mismatch carries an anchor: the tag's JAN was read, so we
-        # know which tag we are comparing.
-        tracks = [track(1, [10, 100, 90, 300])]
-        # The anchor is the tag's own arithmetic agreeing, not a price field.
-        tags = {1: tag(state=TAG_STATE_READABLE, price=269, flags=["tax_consistent"])}
-        reconciliations = {1: reconcile(accepted(), tags[1], product(price=168.0))}
-        findings = evaluate_compliance(tracks, tags, [tags[1]], BANDS, reconciliations)
-        mismatches = [finding for finding in findings if finding.rule == RULE_PRICE_MISMATCH]
-        self.assertEqual(len(mismatches), 1)
-        self.assertIn("269", mismatches[0].detail)
-
-    def test_a_mismatch_without_an_anchor_is_only_a_hint(self):
-        """Measured noise: a wrong identity plus a garbled tag read (name match
-        0.11, no JAN) produced a confident "tag says 90, catalog says 188" that was
-        really two bad reads disagreeing. Without an anchor it is a hint."""
-        tracks = [track(1, [10, 100, 90, 300])]
-        tags = {1: tag(state=TAG_STATE_READABLE, price=90, name="ラスお要をおいしくだべおまにすくg)")}
-        reconciliations = {1: reconcile(accepted(), tags[1], product(price=188.0))}
-        findings = evaluate_compliance(tracks, tags, [tags[1]], BANDS, reconciliations)
-        self.assertNotIn(RULE_PRICE_MISMATCH, [finding.rule for finding in findings])
-        hints = [finding for finding in findings if finding.severity == "hint"]
-        self.assertTrue(hints)
-        self.assertEqual(summarize_findings(findings)["violations"], 0)
-
-    def test_summary_counts_only_violations(self):
-        findings = [
-            evaluate_compliance(
-                [track(1, [10, 100, 90, 300])],
-                {1: tag(state=TAG_STATE_READABLE, price=269, flags=["tax_consistent"])},
-                [tag(state=TAG_STATE_READABLE, price=269, flags=["tax_consistent"])],
-                BANDS,
-                {1: reconcile(accepted(), tag(price=269), product(price=168.0))},
-            )
-        ][0]
-        summary = summarize_findings(findings)
-        self.assertGreaterEqual(summary["violations"], 1)
-        self.assertIn(RULE_PRICE_MISMATCH, summary["by_rule"])
-
-
-class StabilityTests(unittest.TestCase):
-    def test_findings_need_to_persist(self):
-        tracker = ComplianceTracker(ComplianceSettings(stability_frames=3, stability_window=5))
-        from reconcile import Finding
-
-        finding = Finding(rule=RULE_MISSING_TAG, severity="violation", row_index=1, box=[0, 0, 10, 10])
-        first = tracker.update([finding])
-        self.assertEqual(len(first), 0, "a single frame must not publish a violation")
-        tracker.update([finding])
-        stable = tracker.update([finding])
-        self.assertEqual(len(stable), 1)
-
-    def test_a_transient_finding_never_publishes(self):
-        """The rule is windowed: a finding must appear in 3 of the last 5 frames.
-        Two sightings spread over six frames is a flicker, not a violation."""
-        tracker = ComplianceTracker(ComplianceSettings(stability_frames=3, stability_window=5))
-        from reconcile import Finding
-
-        finding = Finding(rule=RULE_MISSING_TAG, severity="violation", row_index=1, box=[0, 0, 10, 10])
-        published = 0
-        for index in range(6):
-            published += len(tracker.update([finding] if index in (0, 2) else []))
-        self.assertEqual(published, 0)
-
-    def test_a_finding_present_in_three_of_five_frames_publishes(self):
-        tracker = ComplianceTracker(ComplianceSettings(stability_frames=3, stability_window=5))
-        from reconcile import Finding
-
-        finding = Finding(rule=RULE_MISSING_TAG, severity="violation", row_index=1, box=[0, 0, 10, 10])
-        published = 0
-        for index in range(5):
-            published += len(tracker.update([finding] if index in (0, 2, 4) else []))
-        self.assertGreater(published, 0)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/recognization/code/test_record.py b/recognization/code/test_record.py
deleted file mode 100644
index ed4d6df..0000000
--- a/recognization/code/test_record.py
+++ /dev/null
@@ -1,96 +0,0 @@
-"""Tests for recording a live session as data to label."""
-
-from __future__ import annotations
-
-import json
-import os
-import shutil
-import sys
-import tempfile
-import unittest
-
-current_dir = os.path.dirname(os.path.abspath(__file__))
-if current_dir not in sys.path:
-    sys.path.insert(0, current_dir)
-
-from record import FrameRecorder, labelling_candidates  # noqa: E402
-
-
-def a_result(sku=None, flags=None):
-    return {
-        "summary": {"in_view": 1, "recognized": 1 if sku else 0},
-        "tracks": [
-            {
-                "box": [10, 100, 100, 300],
-                "identity": {"state": "recognized", "sku_id": sku} if sku else {"state": "unconfirmed"},
-                "tag_identity": {"sku_id": sku, "score": 0.7} if sku else {},
-                "price_tag": {"state": "readable", "price": 269, "flags": flags or []},
-            }
-        ],
-    }
-
-
-class RecorderTests(unittest.TestCase):
-    def setUp(self):
-        self.root = tempfile.mkdtemp()
-        self.addCleanup(shutil.rmtree, self.root, ignore_errors=True)
-
-    def test_frames_and_an_index_are_written(self):
-        recorder = FrameRecorder(self.root, every=1)
-        for frame_id in range(3):
-            recorder.write("session-a", frame_id, b"\xff\xd8jpeg", a_result("sku_l05"))
-        directory = os.path.join(self.root, "session-a")
-        self.assertEqual(sorted(os.listdir(directory)), ["000000.jpg", "000001.jpg", "000002.jpg", "index.jsonl"])
-        lines = [json.loads(line) for line in open(os.path.join(directory, "index.jsonl"), encoding="utf-8")]
-        self.assertEqual([line["frame"] for line in lines], [0, 1, 2])
-        self.assertEqual(lines[0]["tracks"][0]["tag_identity"]["sku_id"], "sku_l05")
-
-    def test_the_active_backend_is_written_on_each_line(self):
-        recorder = FrameRecorder(self.root, every=1, identity_backend="gallery")
-        recorder.write("session-d", 0, b"x", a_result("sku_l05"))
-        recorder.write("session-d", 1, b"y", a_result())
-        with open(os.path.join(self.root, "session-d", "index.jsonl"), encoding="utf-8") as handle:
-            lines = [json.loads(line) for line in handle]
-        self.assertEqual([line["identity_backend"] for line in lines], ["gallery", "gallery"])
-
-    def test_an_unspecified_backend_is_omitted(self):
-        recorder = FrameRecorder(self.root, every=1)
-        recorder.write("session-e", 0, b"x", a_result())
-        with open(os.path.join(self.root, "session-e", "index.jsonl"), encoding="utf-8") as handle:
-            line = json.loads(handle.readline())
-        self.assertNotIn("identity_backend", line)
-
-    def test_sampling_keeps_the_frame_numbering_visible(self):
-        recorder = FrameRecorder(self.root, every=3)
-        for frame_id in range(9):
-            recorder.write("session-b", frame_id, b"x", a_result())
-        lines = [json.loads(line) for line in open(os.path.join(self.root, "session-b", "index.jsonl"), encoding="utf-8")]
-        self.assertEqual([line["frame"] for line in lines], [2, 5, 8],
-                         "every third frame, recorded under its own number")
-
-    def test_sessions_are_separate(self):
-        recorder = FrameRecorder(self.root, every=1)
-        recorder.write("one", 0, b"a", a_result())
-        recorder.write("two", 0, b"b", a_result())
-        self.assertTrue(os.path.exists(os.path.join(self.root, "one", "000000.jpg")))
-        self.assertTrue(os.path.exists(os.path.join(self.root, "two", "000000.jpg")))
-
-    def test_a_session_is_capped(self):
-        recorder = FrameRecorder(self.root, every=1, max_frames_per_session=2)
-        for frame_id in range(5):
-            recorder.write("capped", frame_id, b"x", a_result())
-        self.assertEqual(recorder.stats()["frames_written"], 2)
-
-    def test_labelling_candidates_come_from_the_tag_channel(self):
-        recorder = FrameRecorder(self.root, every=1)
-        recorder.write("session-c", 0, b"x", a_result("sku_l05", ["tax_consistent"]))
-        recorder.write("session-c", 1, b"x", a_result())
-        seeds = labelling_candidates(os.path.join(self.root, "session-c", "index.jsonl"))
-        self.assertEqual(len(seeds), 1, "only the tag-named facing is a seed")
-        self.assertEqual(seeds[0]["sku_id"], "sku_l05")
-        self.assertTrue(seeds[0]["corroborated"])
-        self.assertEqual(seeds[0]["tag_price"], 269)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/recognization/code/test_score_stream.py b/recognization/code/test_score_stream.py
deleted file mode 100644
index 5294657..0000000
--- a/recognization/code/test_score_stream.py
+++ /dev/null
@@ -1,137 +0,0 @@
-"""
-Tests for the G1 scorer.
-
-The gate has two halves - the new lane covers at least as much as Van, and the
-lower bound of the paired-bootstrap difference clears -2 pp - and the second
-half is only computable if a scored row still says which facing it matched.
-These tests pin that pairing and the bound it produces.
-"""
-
-from __future__ import annotations
-
-import os
-import sys
-import tempfile
-import unittest
-
-current_dir = os.path.dirname(os.path.abspath(__file__))
-if current_dir not in sys.path:
-    sys.path.insert(0, current_dir)
-
-from score_stream import (  # noqa: E402
-    G1_BOUND_MIN,
-    correct_facings,
-    paired_bootstrap,
-    score,
-)
-
-
-def facing(index, box, label="sku_a"):
-    return {"index": index, "box": list(box), "label": label}
-
-
-def track(track_id, box, sku="sku_a"):
-    return {"track_id": track_id, "sku_id": sku, "label_box": list(box)}
-
-
-def report(rows, windows=None):
-    """A score report shaped the way `score` returns one, without the boxes."""
-    return {"rows": rows, "windows": windows}
-
-
-def correct(track_id, matched):
-    return {"track_id": track_id, "predicted": "sku_a", "facing": matched,
-            "truth": "sku_a", "iou": 1.0, "outcome": "correct"}
-
-
-def grid(count):
-    return [facing(i, [i * 20, 0, i * 20 + 10, 10]) for i in range(count)]
-
-
-class ScoreRowTests(unittest.TestCase):
-    def setUp(self):
-        self.stock = tempfile.TemporaryDirectory()
-        open(os.path.join(self.stock.name, "sku_a.jpg"), "wb").close()
-        self.addCleanup(self.stock.cleanup)
-
-    def test_rows_carry_the_matched_facing(self):
-        facings = [facing(7, [0, 0, 100, 100]), facing(8, [900, 0, 1000, 100])]
-        rows = score([track(1, [0, 0, 100, 100]), track(2, [400, 400, 500, 500])],
-                     facings, self.stock.name, 0.5)["rows"]
-        by_track = {row["track_id"]: row for row in rows}
-        self.assertEqual(by_track[1]["facing"], 7)
-        self.assertEqual(by_track[1]["outcome"], "correct")
-        # A track that hit nothing is unpaired, and says so instead of guessing.
-        self.assertIsNone(by_track[2]["facing"])
-        self.assertEqual(by_track[2]["outcome"], "unmatched")
-
-    def test_correct_facings_counts_a_facing_once(self):
-        rows = [correct(1, 7), correct(2, 7), correct(3, 9)]
-        self.assertEqual(correct_facings(rows), {7, 9})
-
-
-class PairedBootstrapTests(unittest.TestCase):
-    """27 facings is a small sample, so the pairing is what carries the gate."""
-
-    def test_identical_systems_sit_at_zero_everywhere(self):
-        rows = [correct(1, i) for i in range(10)]
-        result = paired_bootstrap(report(rows), report(rows), [])
-        self.assertEqual(result["difference"], 0.0)
-        self.assertEqual(result["lower_95"], 0.0)
-        self.assertEqual(result["upper_95"], 0.0)
-        self.assertEqual(result["van"], result["new"])
-
-    def test_a_superset_of_vans_hits_can_never_bound_below_zero(self):
-        # The image6 shape: the new lane gets everything Van got, plus a facing.
-        van = [correct(i, i) for i in range(17)]
-        new = van + [correct(99, 17)]
-        result = paired_bootstrap(report(van), report(new), grid(27))
-        self.assertEqual(result["facings"], 27)
-        self.assertEqual(result["van"], 17)
-        self.assertEqual(result["new"], 18)
-        self.assertAlmostEqual(result["difference"], 1 / 27, places=4)
-        # No resample of these 27 can put a facing in the new run that Van's run
-        # did not already have, so the 2.5th percentile is a hard zero. A bound
-        # printed as +0.0 pp is the floor of this pairing, not a margin.
-        self.assertEqual(result["lower_95"], 0.0)
-        self.assertGreaterEqual(result["lower_95"], G1_BOUND_MIN)
-
-    def test_a_swapped_facing_spreads_across_zero(self):
-        van = [correct(i, i) for i in range(17)]
-        new = van[:16] + [correct(99, 17)]
-        result = paired_bootstrap(report(van), report(new), grid(27))
-        self.assertEqual(result["difference"], 0.0)
-        self.assertLess(result["lower_95"], G1_BOUND_MIN)
-        self.assertGreater(result["upper_95"], 0.0)
-
-    def test_both_runs_are_graded_on_what_either_window_reached(self):
-        # Two facings at opposite edges; each run panned to one of them. Grading
-        # on the union keeps both on the same facings; the intersection would
-        # score each run on its own pan and the pairing would be empty.
-        facings = [facing(0, [0, 0, 100, 100]), facing(1, [900, 0, 1000, 100])]
-        left, right = [0, 0, 500, 1000], [500, 0, 1000, 1000]
-        result = paired_bootstrap(report([correct(1, 0)], [left]),
-                                  report([correct(2, 1)], [right]), facings)
-        self.assertEqual(result["facings"], 2)
-        self.assertEqual((result["van"], result["new"]), (1, 1))
-        self.assertEqual(result["difference"], 0.0)
-
-    def test_the_bound_repeats_for_the_same_seed(self):
-        van = [correct(i, i) for i in range(17)]
-        new = van[:5] + [correct(90 + i, 20 + i) for i in range(9)]
-        facings = grid(27)
-        first = paired_bootstrap(report(van), report(new), facings)
-        self.assertEqual(first, paired_bootstrap(report(van), report(new), facings))
-
-    def test_no_reachable_facing_scores_zero_rather_than_dividing_by_zero(self):
-        # Both runs panned to a corner the only facing is not in: the pairing
-        # set is empty, and a rate is not a number there.
-        elsewhere = [facing(0, [0, 0, 10, 10])]
-        corner = [[500, 500, 600, 600]]
-        result = paired_bootstrap(report([], corner), report([], corner), elsewhere)
-        self.assertEqual(result["facings"], 0)
-        self.assertEqual(result["lower_95"], 0.0)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/recognization/code/test_shelf_map.py b/recognization/code/test_shelf_map.py
deleted file mode 100644
index 664ecce..0000000
--- a/recognization/code/test_shelf_map.py
+++ /dev/null
@@ -1,125 +0,0 @@
-"""
-Tests for the shelf map: registration, merging, and evidence pooling.
-
-The sign convention is tested explicitly, because getting it backwards is silent:
-the map still produces boxes, they just land in the wrong place, and a shelf of
-~60 facings becomes 857. That happened during development.
-"""
-
-from __future__ import annotations
-
-import os
-import sys
-import unittest
-
-import numpy as np
-
-current_dir = os.path.dirname(os.path.abspath(__file__))
-project_root = os.path.dirname(current_dir)
-if os.path.exists(os.path.join(project_root, "stock")):
-    os.chdir(project_root)
-if current_dir not in sys.path:
-    sys.path.insert(0, current_dir)
-
-from shelf_map import Facing, ShelfMap, _iou  # noqa: E402
-
-
-def a_frame(shift_x=0, shift_y=0, size=(160, 240)):
-    """A textured frame, optionally a shifted copy of another."""
-    height, width = size
-    rng = np.random.RandomState(7)
-    base = rng.randint(0, 255, (height * 2, width * 2, 3), dtype=np.uint8)
-    return base[height - shift_y:height * 2 - shift_y, width - shift_x:width * 2 - shift_x]
-
-
-def a_track(track_id, box, sku=None, score=None, tag_sku=None):
-    track = {"track_id": track_id, "box": list(box)}
-    if sku:
-        track["identity"] = {"state": "recognized", "sku_id": sku, "score": score or 1.0}
-    else:
-        track["identity"] = {"state": "unconfirmed"}
-    if tag_sku:
-        track["tag_identity"] = {"sku_id": tag_sku, "score": 0.7}
-    return track
-
-
-class IouTests(unittest.TestCase):
-    def test_overlap_maths(self):
-        self.assertAlmostEqual(_iou([0, 0, 10, 10], [0, 0, 10, 10]), 1.0)
-        self.assertEqual(_iou([0, 0, 10, 10], [20, 20, 30, 30]), 0.0)
-
-
-class RegistrationTests(unittest.TestCase):
-    def test_the_frame_origin_moves_opposite_to_the_content(self):
-        """The sign that matters: content moving left means the camera moved right.
-
-        Getting this backwards walks the map the wrong way. It is silent - boxes
-        still appear - and it turned a shelf of ~60 facings into 857.
-        """
-        shelf = ShelfMap()
-        shelf.register(a_frame())
-        shift = shelf.register(a_frame(shift_x=10))          # content moved right 10
-        self.assertIsNotNone(shift)
-        self.assertAlmostEqual(shift[0], 10, delta=1.0)
-
-        # Content moving right means the camera moved LEFT, so the frame's origin
-        # sits 10 px further back along the shelf.
-        shelf.update(0, a_frame(), [a_track(1, [0, 0, 20, 20])])
-        offset_before = list(shelf._offset)
-        shelf.update(1, a_frame(shift_x=10), [a_track(2, [0, 0, 20, 20])])
-        self.assertLess(shelf._offset[0], offset_before[0] + 1,
-                        "the offset must follow the camera, not the content")
-
-    def test_a_first_frame_has_no_shift(self):
-        shelf = ShelfMap()
-        self.assertEqual(shelf.register(a_frame()), (0.0, 0.0))
-
-
-class MergingTests(unittest.TestCase):
-    def test_a_facing_seen_twice_is_one_facing(self):
-        shelf = ShelfMap()
-        shelf.update(0, a_frame(), [a_track(1, [10, 10, 110, 200])])
-        shelf.update(1, a_frame(), [a_track(2, [12, 11, 112, 201])])
-        self.assertEqual(len(shelf.facings()), 1)
-        self.assertEqual(shelf.stats()["merges"], 1)
-
-    def test_two_neighbouring_facings_stay_separate(self):
-        shelf = ShelfMap()
-        shelf.update(0, a_frame(), [a_track(1, [10, 10, 100, 200]), a_track(2, [110, 10, 200, 200])])
-        self.assertEqual(len(shelf.facings()), 2)
-
-    def test_identity_votes_pool_across_track_ids(self):
-        """The point of the map: one facing, many transient track ids, one answer."""
-        shelf = ShelfMap()
-        for index, (track_id, sku) in enumerate([(1, "sku_a"), (2, "sku_a"), (3, "sku_a"), (4, "sku_b")]):
-            shelf.update(index, a_frame(), [a_track(track_id, [10, 10, 110, 200], sku=sku)])
-        facing = shelf.facings()[0]
-        self.assertEqual(facing.observations, 4)
-        self.assertEqual(len(facing.seen_track_ids), 4, "four tracks, one facing")
-        sku_id, agreement = facing.best_identity()
-        self.assertEqual(sku_id, "sku_a")
-        self.assertAlmostEqual(agreement, 0.75)
-
-    def test_tag_evidence_pools_separately_from_packaging(self):
-        shelf = ShelfMap()
-        shelf.update(0, a_frame(), [a_track(1, [10, 10, 110, 200], sku="sku_a", tag_sku="sku_a")])
-        shelf.update(1, a_frame(), [a_track(2, [10, 10, 110, 200], tag_sku="sku_a")])
-        facing = shelf.facings()[0]
-        self.assertEqual(facing.best_identity()[0], "sku_a")
-        self.assertEqual(facing.best_tag_identity()[0], "sku_a")
-        self.assertEqual(len(facing.tag_readings), 0, "no readable tag payload was attached")
-
-
-class PayloadTests(unittest.TestCase):
-    def test_a_facing_reports_its_pooled_evidence(self):
-        facing = Facing(facing_id="face_1", shelf_box=[0, 0, 10, 10])
-        facing.vote_identity("sku_a", 2.0)
-        facing.vote_identity("sku_b", 1.0)
-        payload = facing.as_payload()
-        self.assertEqual(payload["sku_id"], "sku_a")
-        self.assertAlmostEqual(payload["identity_agreement"], 2 / 3, places=3)
-        self.assertIsNone(payload["tag_sku_id"])
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/recognization/code/test_suite.py b/recognization/code/test_suite.py
new file mode 100644
index 0000000..7a744a6
--- /dev/null
+++ b/recognization/code/test_suite.py
@@ -0,0 +1,91 @@
+"""
+Unit and Integration Test Suite
+Validates InventoryManager, ShelfProductDetector, ProductFeatureMatcher, and Pipeline.
+"""
+
+import os
+import sys
+import cv2
+
+current_dir = os.path.dirname(os.path.abspath(__file__))
+if current_dir not in sys.path:
+    sys.path.insert(0, current_dir)
+
+from inventory import InventoryManager
+from feature_matcher import ProductFeatureMatcher
+from detector import ShelfProductDetector
+from pipeline import ShelfRecognitionPipeline
+
+
+def test_inventory():
+    print("[TEST] Testing InventoryManager...")
+    inv = InventoryManager("stock")
+    assert len(inv.products) >= 10, f"Expected products >= 10 in stock.csv, found {len(inv.products)}"
+    p1 = inv.get_product("sku_001")
+    assert p1 is not None, "sku_001 not found"
+    assert p1["product_name"] == "明治 おいしい牛乳"
+    assert p1["price"] == 258.0
+    assert p1["number_on"] == 73
+    print("  -> InventoryManager test: PASSED")
+
+
+def test_feature_matcher():
+    print("[TEST] Testing ProductFeatureMatcher...")
+    matcher = ProductFeatureMatcher("stock")
+    assert len(matcher.db.templates) >= 7, f"Expected templates >= 7, got {len(matcher.db.templates)}"
+
+    for method in ["hybrid", "orb", "brisk"]:
+        tmpl_img = matcher.db.templates["sku_001"]["image"]
+        res = matcher.classify_crop(tmpl_img, method=method)
+        assert res["status"] == "success", f"Method {method} failed"
+        assert res["best_sku"] == "sku_001", f"Expected sku_001 with {method}, got {res['best_sku']}"
+        print(f"  -> {method.upper()} classification test: PASSED (Score: {res['score']}, Conf: {res['confidence']})")
+
+
+def test_detector():
+    print("[TEST] Testing ShelfProductDetector...")
+    detector = ShelfProductDetector("runs/detect/train/weights/best.pt")
+    sample_shelf = "runs/detect/train/train_batch0.jpg"
+    res = detector.detect(sample_shelf)
+    assert res["status"] == "success", "Detection failed"
+    assert res["total_detected"] > 0, "No boxes detected"
+    print(f"  -> Detector test: PASSED ({res['total_detected']} items localized)")
+
+    first_box = res["boxes"][0]
+    cx, cy = first_box["center"]
+    hit = detector.find_box_at_point(cx, cy, res["boxes"])
+    assert hit is not None, "Hit test failed on center coordinate"
+    assert hit["id"] == first_box["id"], "Hit test returned incorrect box"
+    print("  -> Spatial hit-test: PASSED")
+
+
+def test_pipeline():
+    print("[TEST] Testing End-to-End Pipeline...")
+    pipeline = ShelfRecognitionPipeline(
+        weights_path="runs/detect/train/weights/best.pt",
+        stock_dir="stock"
+    )
+    init_res = pipeline.set_active_shelf_image("runs/detect/train/train_batch0.jpg")
+    assert init_res["status"] == "success"
+    assert len(init_res["boxes"]) > 0
+
+    first_box = init_res["boxes"][0]
+    cx, cy = first_box["center"]
+    click_res = pipeline.process_click(cx, cy, method="hybrid")
+    assert click_res["status"] == "success", f"Click processing failed: {click_res.get('message')}"
+    assert "inventory" in click_res
+    assert click_res["inventory"] is not None
+    print(f"  -> End-to-End Pipeline test: PASSED (Matched: {click_res['classification']['matched_sku']} - {click_res['inventory']['product_name']})")
+
+
+if __name__ == "__main__":
+    print("=" * 60)
+    print("STARTING TEST SUITE")
+    print("=" * 60)
+    test_inventory()
+    test_feature_matcher()
+    test_detector()
+    test_pipeline()
+    print("=" * 60)
+    print("ALL TESTS PASSED SUCCESSFULLY!")
+    print("=" * 60)
diff --git a/recognization/code/test_tag_cards.py b/recognization/code/test_tag_cards.py
deleted file mode 100644
index 838e944..0000000
--- a/recognization/code/test_tag_cards.py
+++ /dev/null
@@ -1,239 +0,0 @@
-"""
-Tests for price tags as cards with fields.
-
-Two layers: the pure logic (grouping, layout priors, the tax relation) with
-synthetic lines, and one real-model test against a shelf photograph whose tags
-were read by eye, so the parser is measured against the printed truth.
-"""
-
-from __future__ import annotations
-
-import os
-import sys
-import unittest
-
-current_dir = os.path.dirname(os.path.abspath(__file__))
-project_root = os.path.dirname(current_dir)
-if os.path.exists(os.path.join(project_root, "stock")):
-    os.chdir(project_root)
-if current_dir not in sys.path:
-    sys.path.insert(0, current_dir)
-
-from tag_cards import (  # noqa: E402
-    OcrLine,
-    best_tag_rows,
-    card_from_lines,
-    cluster_rows,
-    group_lines_into_cards,
-    parse_tag_fields,
-)
-
-
-def line(text, x1, y1, x2, y2, confidence=0.9):
-    return OcrLine(text=text, box=(x1, y1, x2, y2), confidence=confidence)
-
-
-def a_tag(name="キューピー ゼロ ノンコレステロール＜310g＞", regular="357", price="269",
-          unit="290.52", discount="2.4割引", origin=(0, 0)):
-    """A tag's printed lines, laid out the way a real one is."""
-    ox, oy = origin
-    return [
-        line("毎日お買得", ox + 20, oy + 5, ox + 150, oy + 22, 0.5),
-        line(name, ox + 10, oy + 26, ox + 240, oy + 44, 0.3),
-        line(regular, ox + 12, oy + 48, ox + 46, oy + 62, 0.3),
-        line(discount, ox + 60, oy + 50, ox + 140, oy + 64, 0.3),
-        line(price, ox + 30, oy + 60, ox + 130, oy + 96, 0.6),     # the big print
-        line(unit, ox + 140, oy + 66, ox + 210, oy + 80, 0.5),
-    ]
-
-
-class GroupingTests(unittest.TestCase):
-    def test_a_row_of_tags_becomes_separate_cards(self):
-        lines = a_tag(origin=(0, 0)) + a_tag(origin=(300, 0), regular="400", price="379")
-        rows = best_tag_rows(lines)
-        self.assertEqual(len(rows), 1, "one rail line of tags")
-        cards = group_lines_into_cards(rows[0])
-        self.assertEqual(len(cards), 2, "the gap between tags separates them")
-
-    def test_packaging_text_above_the_rail_is_its_own_row(self):
-        # Product text sits above the rail; a tag row must be chosen on its own.
-        product = [line("キユーピー マヨネーズ 450g", 100, 0, 320, 18, 0.3)]
-        lines = product + a_tag(origin=(0, 60))
-        rows = cluster_rows(lines)
-        self.assertEqual(len(rows), 2)
-        chosen = best_tag_rows(lines, keep=1)
-        self.assertEqual(len(chosen), 1)
-        self.assertTrue(all(item.box[1] >= 60 for item in chosen[0]),
-                        "the tag row wins, not the packaging row")
-
-
-class ParsingTests(unittest.TestCase):
-    def test_the_tax_relation_recovers_a_price_the_big_digits_got_wrong(self):
-        # Measured failure: the stylised big digits read as 262/252/258 while the
-        # small clean number read 290.52. 290.52 / 1.08 = 269 exactly.
-        fields = parse_tag_fields(a_tag(price="262"))
-        self.assertEqual(fields.price, 269)
-        self.assertEqual(fields.price_source, "derived")
-        self.assertIn("tax_pair_consistent", fields.flags)
-        self.assertIn("digit_read_disagrees", fields.flags)
-        self.assertTrue(fields.corroborated())
-
-    def test_an_agreeing_pair_is_reported_as_measured(self):
-        fields = parse_tag_fields(a_tag(price="269"))
-        self.assertEqual(fields.price, 269)
-        self.assertEqual(fields.price_source, "measured")
-        self.assertIn("tax_consistent", fields.flags)
-
-    def test_the_small_number_is_the_regular_price_not_a_code(self):
-        """Corrected: the number beside the sale price is the pre-discount price.
-
-        Checked against the manufacturer, this product's JAN is 4901577092381, whose
-        last three digits are 381 - not the 357 the tag prints. The discount confirms
-        it: (357-269)/357 = 24.6 %, printed as 2.4割引.
-        """
-        fields = parse_tag_fields(a_tag(price="269", regular="357"))
-        self.assertEqual(fields.regular_price, 357)
-        self.assertEqual(fields.price, 269)
-        self.assertIn("discount_consistent", fields.flags)
-
-    def test_the_discount_check_corroborates_the_pair(self):
-        # The three tags on the demo shelf, with their printed discounts.
-        # Each fixture carries the tax-included price that belongs with it.
-        for regular, price, unit, discount in ((357, 269, "290.52", "2.4割引"),
-                                              (400, 379, "409.32", "0.5割引"),
-                                              (248, 199, "214.92", "1.9割引")):
-            fields = parse_tag_fields(a_tag(regular=str(regular), price=str(price), unit=unit, discount=discount))
-            self.assertIn("discount_consistent", fields.flags, f"{regular}/{price} {discount}")
-            self.assertTrue(fields.corroborated())
-
-    def test_a_discount_that_disagrees_is_flagged(self):
-        fields = parse_tag_fields(a_tag(regular="357", price="269", discount="5.0割引"))
-        self.assertIn("discount_mismatch", fields.flags)
-
-    def test_a_package_size_is_not_a_price(self):
-        fields = parse_tag_fields([
-            line("キューピー ゼロ ノンコレステロール", 10, 10, 240, 30, 0.3),
-            line("<310g>", 200, 12, 250, 30, 0.3),
-            line("357", 12, 48, 46, 62, 0.3),
-            line("269", 30, 60, 130, 96, 0.6),
-        ])
-        self.assertEqual(fields.price, 269)
-        self.assertNotEqual(fields.price, 310)
-
-    def test_an_implausible_printed_discount_is_replaced_by_the_derived_one(self):
-        self.assertEqual(parse_tag_fields(a_tag()).discount, 2.4)
-        # 12.4割引 is not a real rate. Rather than dropping the field, the value the
-        # regular price and sale price imply is used, and marked as derived.
-        fields = parse_tag_fields(a_tag(discount="12.4割引"))
-        self.assertEqual(fields.discount, 2.4)
-        self.assertIn("discount_derived", fields.flags)
-
-    def test_the_name_is_the_longest_text_line(self):
-        fields = parse_tag_fields(a_tag())
-        self.assertIn("ノンコレステロール", fields.name or "")
-
-    def test_an_uncorroborated_price_is_not_readable(self):
-        """Measured live: uncorroborated reads produced prices of 20, 23, 72, 2002.
-
-        Without the tax-included price or the discount to check it, a read of the
-        stylised digits is a candidate, not a readable tag.
-        """
-        fields = parse_tag_fields([
-            line("キューピー ゼロ ノンコレステロール", 10, 10, 240, 30, 0.3),
-            line("269", 30, 60, 130, 96, 0.6),
-        ])
-        self.assertEqual(fields.price, 269)
-        self.assertFalse(fields.corroborated())
-        self.assertFalse(fields.is_readable())
-
-    def test_a_implausibly_cheap_price_is_not_readable(self):
-        fields = parse_tag_fields(a_tag(regular="20", price="23", discount="0.1割引", unit="24.84"))
-        self.assertLess(fields.price, 50)
-        self.assertFalse(fields.is_readable())
-        self.assertIn("price_implausible", fields.flags)
-
-    def test_a_tag_without_a_price_is_not_readable(self):
-        fields = parse_tag_fields([line("毎日お買得", 0, 0, 100, 20, 0.5)])
-        self.assertFalse(fields.is_readable())
-        self.assertIn("no_price", fields.flags)
-
-    def test_the_card_box_is_the_union_of_its_lines(self):
-        # The box wraps the printed lines (with a little padding), so it lines up
-        # with the tag instead of with the rail band it was found in.
-        card = card_from_lines(a_tag(origin=(50, 100)))
-        self.assertLessEqual(card.box[0], 60)          # the leftmost line starts at ox+10
-        self.assertLessEqual(card.box[1], 105)
-        self.assertGreaterEqual(card.box[2], 260)
-        self.assertGreaterEqual(card.box[3], 190)
-
-
-class RealShelfTagTests(unittest.TestCase):
-    """One measured row of a real LOPIA shelf (add/image6.jpg), read by eye."""
-
-    # (x centre, jan, price, tax-included price, discount, name fragment)
-    # (x centre, regular price, sale price, tax-included price, discount, name)
-    EXPECTED = [
-        (175, 357, 269, 290.52, 2.4, "ノンコレステロール"),
-        (555, 400, 379, 409.32, 0.5, None),
-        (975, 248, 199, 214.92, 1.9, "ラッキーマヨネーズ"),
-    ]
-
-    def test_a_real_rail_reads_its_tags(self):
-        import cv2
-
-        from ocr import VisionOcr
-        from tag_cards import read_card_lines
-
-        image_path = "add/image6.jpg"
-        if not os.path.exists(image_path):
-            self.skipTest("demo image not present")
-        engine = VisionOcr()
-        if not engine.available():
-            self.skipTest("no OCR backend on this host")
-        image = cv2.imread(image_path)
-
-        band = image[630:800]
-        seed_lines = [
-            OcrLine(text=item.text, box=(item.box[0], item.box[1] + 630, item.box[2], item.box[3] + 630),
-                    confidence=item.confidence)
-            for item in engine.read(band).lines if item.text.strip()
-        ]
-        rows = best_tag_rows(seed_lines, keep=1)
-        self.assertTrue(rows, "the rail must yield a tag row")
-
-        cards = []
-        for group in group_lines_into_cards(rows[0]):
-            seed = card_from_lines(group)
-            pooled = read_card_lines(engine, image, seed.box)
-            card = card_from_lines(pooled) if pooled else seed
-            card.box = seed.box
-            cards.append(card)
-        cards = [card for card in cards if card.fields.price or card.fields.regular_price]
-        self.assertGreaterEqual(len(cards), 3, "the readable row carries at least three tags")
-
-        # The rule is that nothing WRONG is published, not that every field is
-        # filled: the big stylised digits misread on one tag, so its regular price
-        # is suppressed rather than guessed.
-        correct = 0
-        suppressed = 0
-        for centre_x, regular, price, unit, discount, name in self.EXPECTED:
-            card = min(cards, key=lambda c: abs(c.centre_x - centre_x))
-            fields = card.fields
-            if fields.regular_price is None:
-                suppressed += 1
-            elif fields.regular_price == regular:
-                correct += 1
-            if fields.price == price:
-                correct += 1
-            if fields.unit_price == unit:
-                correct += 1
-            if fields.discount == discount:
-                correct += 1
-            if name is None or (fields.name and name in fields.name):
-                correct += 1
-        self.assertGreaterEqual(correct, 14, f"fields correct: {correct} of 15, suppressed {suppressed}")
-        self.assertLessEqual(suppressed, 1, "at most one field may be withheld")
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/recognization/code/test_tag_identity.py b/recognization/code/test_tag_identity.py
deleted file mode 100644
index e98cfb1..0000000
--- a/recognization/code/test_tag_identity.py
+++ /dev/null
@@ -1,121 +0,0 @@
-"""
-Tests for identity from the shelf's own price tags.
-
-The measured case these encode: the store's printed label names the product in
-clean text, one tag per product, and on the demo shelf the packaging matcher
-agreed with it only 69 % of the time where both spoke - so the tag wins a
-disagreement, and the disagreement is recorded rather than hidden.
-"""
-
-from __future__ import annotations
-
-import os
-import sys
-import unittest
-
-current_dir = os.path.dirname(os.path.abspath(__file__))
-project_root = os.path.dirname(current_dir)
-if os.path.exists(os.path.join(project_root, "stock")):
-    os.chdir(project_root)
-if current_dir not in sys.path:
-    sys.path.insert(0, current_dir)
-
-from reconcile import (  # noqa: E402
-    TAG_STATE_ABSENT,
-    TAG_STATE_READABLE,
-    best_catalog_match,
-    cluster_into_rows,
-    identities_from_tags,
-    normalize_name,
-)
-
-CATALOG = {
-    "sku_l05": {"sku_id": "sku_l05", "product_name": "キューピー ゼロ ノンコレステロール 310g", "price": 269},
-    "sku_l07": {"sku_id": "sku_l07", "product_name": "丸和油脂 ラッキーマヨネーズ 200g", "price": 199},
-}
-
-
-def facing(track_id, box, sku=None):
-    identity = {"state": "recognized", "sku_id": sku} if sku else {"state": "unconfirmed"}
-    return {"track_id": track_id, "box": box, "identity": identity}
-
-
-def tag(box, name=None, state=TAG_STATE_READABLE):
-    return {"state": state, "box": box, "name": name, "regular_price": None, "price": None,
-            "barcode": None, "flags": []}
-
-
-class NameMatchTests(unittest.TestCase):
-    def test_an_ocr_name_still_matches_the_catalog(self):
-        # Measured OCR output against the catalog entry it should find.
-        sku, score = best_catalog_match(
-            "キュービーゼロ ノンコレステロールく310g＞",
-            {k: normalize_name(v["product_name"]) for k, v in CATALOG.items()},
-        )
-        self.assertEqual(sku, "sku_l05")
-        self.assertGreater(score, 0.5)
-
-    def test_unrelated_text_matches_nothing(self):
-        sku, _ = best_catalog_match(
-            "ラス BEをさいしく使べるまにてく。",
-            {k: normalize_name(v["product_name"]) for k, v in CATALOG.items()},
-        )
-        self.assertIsNone(sku, "a garbled read must not be forced onto a product")
-
-
-class AssignmentTests(unittest.TestCase):
-    def test_a_facing_is_named_by_the_tag_under_its_row(self):
-        # A row of four facings, one tag under it, one product.
-        tracks = [facing(index, [10 + index * 90, 100, 100 + index * 90, 300]) for index in range(4)]
-        tags = [tag([40, 320, 260, 380], name="キューピー ゼロ ノンコレステロール 310g")]
-        found = identities_from_tags(tracks, {}, CATALOG, all_tags=tags)
-        self.assertEqual(len(found), 4, "one tag covers the whole run of facings")
-        self.assertTrue(all(info["sku_id"] == "sku_l05" for info in found.values()))
-
-    def test_a_distant_facing_does_not_inherit_a_tag(self):
-        """A product whose own tag was not detected must not take its neighbour's.
-
-        Without a reach limit the nearest readable tag names it, which is how a
-        facing several columns away can acquire the wrong product.
-        """
-        tracks = [
-            facing(0, [10, 100, 100, 300]),
-            facing(1, [1600, 100, 1690, 300]),        # far along the same row
-        ]
-        tags = [tag([20, 320, 180, 380], name="キューピー ゼロ ノンコレステロール 310g")]
-        found = identities_from_tags(tracks, {}, CATALOG, all_tags=tags)
-        self.assertIn(0, found)
-        self.assertNotIn(1, found, "out of reach of that tag")
-
-    def test_the_nearest_tag_wins_along_a_row(self):
-        # Two products side by side, one tag each.
-        tracks = [
-            facing(0, [10, 100, 100, 300]),
-            facing(1, [110, 100, 200, 300]),
-            facing(2, [700, 100, 790, 300]),
-            facing(3, [800, 100, 890, 300]),
-        ]
-        tags = [
-            tag([20, 320, 180, 380], name="キューピー ゼロ ノンコレステロール 310g"),
-            tag([710, 320, 870, 380], name="丸和油脂 ラッキーマヨネーズ 200g"),
-        ]
-        found = identities_from_tags(tracks, {}, CATALOG, all_tags=tags)
-        self.assertEqual(found[0]["sku_id"], "sku_l05")
-        self.assertEqual(found[1]["sku_id"], "sku_l05")
-        self.assertEqual(found[2]["sku_id"], "sku_l07")
-        self.assertEqual(found[3]["sku_id"], "sku_l07")
-
-    def test_an_unreadable_tag_names_nothing(self):
-        tracks = [facing(0, [10, 100, 100, 300])]
-        tags = [tag([20, 320, 180, 380], name=None, state="unreadable")]
-        self.assertEqual(identities_from_tags(tracks, {}, CATALOG, all_tags=tags), {})
-
-    def test_rows_are_clustered_before_assignment(self):
-        # The same column on two shelves must not become one product.
-        tracks = [facing(0, [10, 100, 100, 300]), facing(1, [10, 600, 100, 800])]
-        rows = cluster_into_rows(tracks)
-        self.assertEqual(len(rows), 2)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/recognization/code/test_tracking.py b/recognization/code/test_tracking.py
deleted file mode 100644
index d94ea83..0000000
--- a/recognization/code/test_tracking.py
+++ /dev/null
@@ -1,110 +0,0 @@
-"""
-Contract tests for session tracking.
-
-The important one is ID isolation: ultralytics mints track ids from a
-process-global counter and resets it when a tracker is constructed, so two live
-clients would otherwise be able to hand each other's cached labels to the wrong
-product.
-"""
-
-from __future__ import annotations
-
-import os
-import sys
-import unittest
-
-current_dir = os.path.dirname(os.path.abspath(__file__))
-if current_dir not in sys.path:
-    sys.path.insert(0, current_dir)
-
-from tracking import SessionTracker  # noqa: E402
-
-
-def pan(tracker, start_x, frames=8, step=10, width=60, height=80, y=100):
-    ids = []
-    for index in range(frames):
-        x = start_x + index * step
-        tracks = tracker.update([[x, y, x + width, y + height]], [0.9])
-        ids.append(tracks[0].track_id if tracks else None)
-    return ids
-
-
-class IdIsolationTests(unittest.TestCase):
-    def test_sessions_do_not_share_ids(self):
-        first = SessionTracker()
-        second = SessionTracker()
-        first_ids = pan(first, 100)
-        second_ids = pan(second, 100)
-        self.assertEqual(len(set(first_ids)), 1, "a panning box keeps one id")
-        self.assertEqual(len(set(second_ids)), 1, "a panning box keeps one id")
-        self.assertEqual(first_ids[0], 1)
-        self.assertEqual(second_ids[0], 1, "each session starts its own numbering")
-
-    def test_constructing_a_tracker_does_not_reset_a_running_session(self):
-        """ultralytics resets the global id counter in BYTETracker.__init__; a
-        second client connecting mid-scan must not renumber the first client's
-        tracks (its cached labels are keyed by id)."""
-        first = SessionTracker()
-        ids = pan(first, 100, frames=6, step=5)
-        self.assertEqual(len(set(ids)), 1)
-        second = SessionTracker()  # a second client connects
-        continued = pan(first, 100 + 6 * 5, frames=6, step=5)
-        self.assertEqual(set(continued), {ids[0]}, "ids must keep counting, not restart")
-        self.assertEqual(pan(second, 400)[0], 1, "the new session still starts at 1")
-
-    def test_two_sessions_track_the_same_product_independently(self):
-        first = SessionTracker()
-        second = SessionTracker()
-        first_ids = pan(first, 100)
-        second_ids = pan(second, 100)
-        self.assertEqual(first_ids, second_ids, "same motion, same ids per session")
-        # ... but they are separate objects: dropping one does not touch the other
-        for _ in range(10):
-            first.update([], [])
-        self.assertEqual(len(set(pan(second, 100 + 8 * 10, frames=3, step=5))), 1)
-
-    def test_two_boxes_get_distinct_ids(self):
-        tracker = SessionTracker()
-        tracks = tracker.update([[10, 10, 70, 90], [200, 10, 260, 90]], [0.9, 0.8])
-        self.assertEqual(len(tracks), 2)
-        self.assertEqual(len({track.track_id for track in tracks}), 2)
-
-
-class TrackLifecycleTests(unittest.TestCase):
-    def test_pan_continuity(self):
-        tracker = SessionTracker()
-        ids = pan(tracker, 50, frames=10, step=12)
-        self.assertEqual(len(set(ids)), 1)
-
-    def test_absent_track_is_dropped_and_gets_a_new_id(self):
-        tracker = SessionTracker(buffer=5)
-        first_ids = pan(tracker, 50, frames=2)
-        for _ in range(8):
-            tracker.update([], [])
-        for _ in range(3):  # a re-detected object is unconfirmed for a frame or two
-            tracks = tracker.update([[50, 100, 110, 180]], [0.9])
-        self.assertTrue(tracks, "the object is tracked again")
-        self.assertNotEqual(tracks[0].track_id, first_ids[0], "a dropped track must not resume its old id")
-
-    def test_detection_index_maps_back_to_the_input_box(self):
-        tracker = SessionTracker()
-        tracks = tracker.update(
-            [[10, 10, 70, 90], [200, 10, 260, 90], [400, 10, 460, 90]], [0.9, 0.85, 0.95]
-        )
-        indices = sorted(track.det_index for track in tracks)
-        self.assertEqual(indices, [0, 1, 2])
-
-    def test_reset_starts_numbering_over_without_touching_others(self):
-        other = SessionTracker()
-        other_ids = pan(other, 100, frames=6, step=5)
-        tracker = SessionTracker()
-        pan(tracker, 100, frames=6, step=5)
-        tracker.reset()
-        after = pan(tracker, 300)
-        self.assertEqual(set(after), {1}, "reset restarts this session's numbering")
-        continued = pan(other, 100 + 6 * 5, frames=4, step=5)
-        self.assertEqual(set(continued), {other_ids[0]}, "the other session keeps counting")
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/recognization/code/tracking.py b/recognization/code/tracking.py
deleted file mode 100644
index 42b7b2a..0000000
--- a/recognization/code/tracking.py
+++ /dev/null
@@ -1,204 +0,0 @@
-"""
-Session-scoped multi-object tracking for live recognition.
-
-Wraps ultralytics' BYTETracker (Kalman prediction + two-stage association) with
-one critical fix: ultralytics mints track IDs from a PROCESS-GLOBAL counter
-(BaseTrack._count), and BYTETracker.__init__ resets that counter. With several
-live sessions that means one client's tracker creation re-issues IDs already in
-use by another client, and a cached label can land on the wrong product.
-
-SessionTracker gives every tracker instance its own counter and never touches
-the global one. The rest of the algorithm is used unmodified.
-
-Pinned to ultralytics 8.4.153 (see requirements.txt): this module touches
-tracker internals.
-"""
-
-from __future__ import annotations
-
-import itertools
-import os
-import sys
-import threading
-from typing import Any, List, Optional, Sequence
-
-import numpy as np
-
-current_dir = os.path.dirname(os.path.abspath(__file__))
-if current_dir not in sys.path:
-    sys.path.insert(0, current_dir)
-
-from ultralytics.trackers.byte_tracker import BYTETracker, STrack
-from ultralytics.utils import YAML, IterableSimpleNamespace
-
-_BYTETRACK_YAML = os.path.join(
-    os.path.dirname(os.path.abspath(__import__("ultralytics").__file__)),
-    "cfg", "trackers", "bytetrack.yaml",
-)
-
-DEFAULT_TRACKER_CONFIG = {
-    "tracker_type": "bytetrack",
-    "track_high_thresh": 0.25,
-    "track_low_thresh": 0.1,
-    "new_track_thresh": 0.25,
-    "track_buffer": 30,
-    "match_thresh": 0.8,
-    "fuse_score": True,
-}
-
-
-def _default_args() -> IterableSimpleNamespace:
-    try:
-        return IterableSimpleNamespace(**YAML.load(_BYTETRACK_YAML))
-    except Exception:
-        return IterableSimpleNamespace(**DEFAULT_TRACKER_CONFIG)
-
-
-class _Detections:
-    """Duck-typed stand-in for ultralytics Results: only what BYTETracker reads.
-
-    Must NOT expose `xywhr`; the tracker uses its presence to pick rotated-box
-    handling.
-    """
-
-    __slots__ = ("xywh", "conf", "cls")
-
-    def __init__(self, boxes_xyxy: np.ndarray, scores: np.ndarray):
-        boxes = np.asarray(boxes_xyxy, dtype=np.float32).reshape(-1, 4)
-        scores = np.asarray(scores, dtype=np.float32).reshape(-1)
-        widths = boxes[:, 2] - boxes[:, 0]
-        heights = boxes[:, 3] - boxes[:, 1]
-        self.xywh = np.stack(
-            [boxes[:, 0] + widths / 2.0, boxes[:, 1] + heights / 2.0, widths, heights], axis=1
-        ).astype(np.float32)
-        self.conf = scores
-        self.cls = np.zeros(len(scores), dtype=np.float32)
-
-    def __len__(self) -> int:
-        return len(self.conf)
-
-    def __getitem__(self, mask: Any) -> "_Detections":
-        mask = np.asarray(mask)
-        subset = _Detections.__new__(_Detections)
-        subset.xywh = self.xywh[mask]
-        subset.conf = self.conf[mask]
-        subset.cls = self.cls[mask]
-        return subset
-
-
-class _SessionByteTrack(BYTETracker):
-    """BYTETracker with a per-instance ID counter."""
-
-    def __init__(self, args: IterableSimpleNamespace):
-        self._id_counter = itertools.count(1)
-        self._id_lock = threading.Lock()
-        # super().__init__ calls self.reset_id(); the override below only touches
-        # this instance's counter, so the global one is never disturbed.
-        super().__init__(args)
-        self.track_class = self._make_track_class()
-
-    def _next_id(self) -> int:
-        with self._id_lock:
-            return next(self._id_counter)
-
-    def _make_track_class(self):
-        tracker = self
-
-        class SessionSTrack(STrack):
-            @staticmethod
-            def next_id() -> int:
-                return tracker._next_id()
-
-        SessionSTrack.__name__ = "SessionSTrack"
-        return SessionSTrack
-
-    def reset_id(self) -> None:  # type: ignore[override]
-        """Reset only this tracker's counter; never BaseTrack._count."""
-        with self._id_lock:
-            self._id_counter = itertools.count(1)
-        self.track_class = self._make_track_class()
-
-
-class TrackOut:
-    """One tracked object for a frame."""
-
-    __slots__ = ("track_id", "box", "score", "det_index", "low_score")
-
-    def __init__(
-        self,
-        track_id: int,
-        box: List[int],
-        score: float,
-        det_index: Optional[int],
-        *,
-        low_score: bool = False,
-    ):
-        self.track_id = int(track_id)
-        self.box = [int(v) for v in box]
-        self.score = float(score)
-        self.det_index = None if det_index is None or det_index < 0 else int(det_index)
-        # ByteTrack's second pass keeps an id alive on a low-score box. That box
-        # is not a new measurement: callers must not crop or re-label from it.
-        self.low_score = bool(low_score)
-
-    def __repr__(self) -> str:  # pragma: no cover - debug helper
-        return (
-            f"TrackOut(id={self.track_id}, box={self.box}, score={self.score:.3f}, "
-            f"det={self.det_index}, low={self.low_score})"
-        )
-
-
-class SessionTracker:
-    """Per-session tracker. Used by one session at a time, under its lock."""
-
-    def __init__(
-        self,
-        *,
-        high: float = 0.25,
-        low: float = 0.1,
-        new: float = 0.25,
-        buffer: int = 30,
-        match: float = 0.8,
-        fuse: bool = True,
-    ):
-        args = _default_args()
-        args.track_high_thresh = float(high)
-        args.track_low_thresh = float(low)
-        args.new_track_thresh = float(new)
-        args.track_buffer = int(buffer)
-        args.match_thresh = float(match)
-        args.fuse_score = bool(fuse)
-        self.args = args
-        self._tracker = _SessionByteTrack(args)
-
-    def update(
-        self,
-        boxes_xyxy: Sequence[Sequence[int]],
-        scores: Sequence[float],
-    ) -> List[TrackOut]:
-        boxes = np.asarray(boxes_xyxy, dtype=np.float32).reshape(-1, 4)
-        scores_array = np.asarray(scores, dtype=np.float32).reshape(-1)
-        if len(boxes) != len(scores_array):
-            raise ValueError("boxes and scores must have the same length")
-        detections = _Detections(boxes, scores_array)
-        rows = self._tracker.update(detections)
-        outputs: List[TrackOut] = []
-        if rows is None or len(rows) == 0:
-            return outputs
-        high = float(self.args.track_high_thresh)
-        for row in np.asarray(rows):
-            # [x1, y1, x2, y2, track_id, score, cls, idx]
-            score = float(row[5])
-            outputs.append(
-                TrackOut(
-                    track_id=int(row[4]),
-                    box=[int(round(row[0])), int(round(row[1])), int(round(row[2])), int(round(row[3]))],
-                    score=score,
-                    det_index=int(row[7]) if len(row) > 7 else None,
-                    low_score=score < high,
-                )
-            )
-        return outputs
-
-    def reset(self) -> None:
-        self._tracker.reset()
diff --git a/recognization/code/transport.py b/recognization/code/transport.py
deleted file mode 100644
index 0322306..0000000
--- a/recognization/code/transport.py
+++ /dev/null
@@ -1,268 +0,0 @@
-"""
-Request bounds and the LAN token check.
-
-A client can declare a tiny Content-Length and then keep writing, so the
-declared length is a hint and the read itself stops at the cap. The token
-never travels in a query string: proxies and access logs keep those.
-"""
-
-from __future__ import annotations
-
-import hmac
-import ipaddress
-import os
-import shutil
-import signal
-import socket
-import subprocess
-from typing import Any, Dict, Optional
-
-LOOPBACK_HOSTS = frozenset({"127.0.0.1", "::1", "localhost"})
-
-
-def is_loopback_host(host: Optional[str]) -> bool:
-    text = (host or "").strip().lower()
-    if text.startswith("[") and text.endswith("]"):
-        text = text[1:-1]
-    if "%" in text:  # fe80::1%lo0
-        text = text.split("%", 1)[0]
-    if text in LOOPBACK_HOSTS:
-        return True
-    try:
-        return ipaddress.ip_address(text).is_loopback
-    except ValueError:
-        return False
-
-
-def require_token_for_bind(host: Optional[str], token: Optional[str]) -> None:
-    """A non-loopback bind with no token is reachable by anyone on that network."""
-    if token or is_loopback_host(host):
-        return
-    raise SystemExit(
-        f"refusing to bind {host} without DORAYAKI_API_TOKEN "
-        "(loopback is the only bind that may run with auth off)"
-    )
-
-
-def token_matches(presented: Optional[str], token: Optional[str]) -> bool:
-    if not presented or not token:
-        return False
-    # compare_digest raises when the lengths differ. Fold the length in so a
-    # short guess neither throws nor returns early.
-    return hmac.compare_digest(
-        len(presented).to_bytes(8, "big") + presented.encode(),
-        len(token).to_bytes(8, "big") + token.encode(),
-    )
-
-
-def bearer_matches(authorization: Optional[str], token: str) -> bool:
-    if not authorization or not token:
-        return False
-    scheme, _, presented = authorization.partition(" ")
-    if scheme.lower() != "bearer" or not presented or " " in presented:
-        return False
-    return token_matches(presented, token)
-
-
-def declared_length(header: Optional[str]) -> Optional[int]:
-    if header is None or header == "":
-        return None
-    try:
-        length = int(header)
-    except (TypeError, ValueError):
-        return None
-    return length if length >= 0 else None
-
-
-async def read_bounded(source, max_bytes: int) -> bytes:
-    """Stop once the cap is passed. The caller rejects the oversize body.
-
-    A request stream is async-iterable. An UploadFile is not: it has read(n).
-    """
-    out = bytearray()
-    read = getattr(source, "read", None)
-    if read is not None and not hasattr(source, "__aiter__"):
-        while True:
-            chunk = await read(65536)
-            if not chunk:
-                break
-            out += chunk
-            if len(out) > max_bytes:
-                break
-        return bytes(out)
-    async for chunk in source:
-        if not chunk:
-            continue
-        out += chunk
-        if len(out) > max_bytes:
-            break
-    return bytes(out)
-
-
-class BodyLimit:
-    """Stop the ASGI receive once the body passes the cap.
-
-    A handler that checks Content-Length, or reads request.body(), runs after
-    the server has already buffered the stream. This counts bytes at receive
-    and returns 413 without handing the rest to the app. Content-Length is
-    not trusted: the stream is what counts, and reading stops at the cap.
-    If the app already started the response, the connection is aborted
-    instead of sending a second http.response.start. WebSocket frames are
-    not HTTP bodies and are left alone.
-    """
-
-    def __init__(self, app: Any, max_bytes: int) -> None:
-        self.app = app
-        self.max_bytes = int(max_bytes)
-
-    async def __call__(self, scope, receive, send) -> None:
-        if scope.get("type") != "http":
-            await self.app(scope, receive, send)
-            return
-
-        class TooLarge(Exception):
-            pass
-
-        seen = 0
-        started = False
-
-        async def limited() -> Dict[str, Any]:
-            nonlocal seen
-            message = await receive()
-            if message.get("type") != "http.request":
-                return message
-            body = message.get("body") or b""
-            seen += len(body)
-            if seen > self.max_bytes:
-                # The app must not see this chunk or any later one.
-                raise TooLarge
-            return message
-
-        async def tracked(message: Dict[str, Any]) -> None:
-            nonlocal started
-            if message.get("type") == "http.response.start":
-                started = True
-            await send(message)
-
-        try:
-            await self.app(scope, limited, tracked)
-        except TooLarge:
-            if started:
-                # The status line is already on the wire. Returning leaves the
-                # response unfinished so the server closes the connection
-                # instead of accepting a second http.response.start.
-                return
-            await send(
-                {
-                    "type": "http.response.start",
-                    "status": 413,
-                    "headers": [(b"content-type", b"application/json")],
-                }
-            )
-            await send(
-                {
-                    "type": "http.response.body",
-                    "body": b'{"error":{"code":"PAYLOAD_TOO_LARGE","message":"request body too large"}}',
-                }
-            )
-
-
-SERVICE_TYPE = "_dorayaki._tcp"
-
-
-def _lan_address() -> Optional[str]:
-    """Address a phone on this network can open. Loopback is not one.
-
-    The demo launcher uses `ipconfig getifaddr en0` then `en1`. A UDP connect
-    is the fallback when those interfaces are absent.
-    """
-    if shutil.which("ipconfig"):
-        for iface in ("en0", "en1"):
-            try:
-                out = subprocess.check_output(
-                    ["ipconfig", "getifaddr", iface], stderr=subprocess.DEVNULL, text=True
-                ).strip()
-            except (subprocess.CalledProcessError, OSError):
-                continue
-            if out and not is_loopback_host(out):
-                return out
-    try:
-        probe = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
-        try:
-            probe.connect(("192.0.2.1", 9))
-            found = probe.getsockname()[0]
-        finally:
-            probe.close()
-    except OSError:
-        return None
-    if not found or is_loopback_host(found):
-        return None
-    return found
-
-
-def advertise_host(bind_host: Optional[str]) -> Optional[str]:
-    """The address clients should browse. A loopback bind is not advertised.
-
-    A wildcard bind (0.0.0.0, ::) has no address of its own, so the record
-    uses the LAN address the demo launcher would hand a phone.
-    """
-    text = (bind_host or "").strip()
-    if text.startswith("[") and text.endswith("]"):
-        text = text[1:-1]
-    if is_loopback_host(text):
-        return None
-    if text in ("", "0.0.0.0", "::"):
-        return _lan_address()
-    try:
-        parsed = ipaddress.ip_address(text)
-    except ValueError:
-        return None
-    if parsed.is_loopback or parsed.is_unspecified:
-        return None if parsed.is_loopback else _lan_address()
-    return str(parsed)
-
-
-class ServiceAdvertiser:
-    """Register `_dorayaki._tcp` with the system mDNS daemon and drop it on close.
-
-    ponytail: macOS `dns-sd` only. A Linux host needs `avahi-publish` wired the
-    same way; do not add a Python mDNS package for this one record.
-    """
-
-    def __init__(self, host: str, port: int, name: str = "Dorayaki") -> None:
-        self.host = host
-        self.port = int(port)
-        self.name = name
-        self._process: Optional[subprocess.Popen] = None
-
-    def start(self) -> bool:
-        if self._process is not None:
-            return True
-        binary = shutil.which("dns-sd")
-        if not binary:
-            return False
-        self._process = subprocess.Popen(
-            [binary, "-P", self.name, SERVICE_TYPE, "local", str(self.port), self.host, self.host],
-            stdout=subprocess.DEVNULL,
-            stderr=subprocess.DEVNULL,
-            start_new_session=True,
-        )
-        return True
-
-    def close(self) -> None:
-        process = self._process
-        self._process = None
-        if process is None or process.poll() is not None:
-            return
-        try:
-            os.killpg(process.pid, signal.SIGTERM)
-        except ProcessLookupError:
-            return
-        try:
-            process.wait(timeout=2)
-        except subprocess.TimeoutExpired:
-            try:
-                os.killpg(process.pid, signal.SIGKILL)
-            except ProcessLookupError:
-                return
-            process.wait(timeout=2)
diff --git a/recognization/code/web/app.js b/recognization/code/web/app.js
deleted file mode 100644
index 5eb75cd..0000000
--- a/recognization/code/web/app.js
+++ /dev/null
@@ -1,1175 +0,0 @@
-/*
- * Dorayaki web client.
- *
- * A client of the recognition server, nothing else: live frames go out over
- * /v1/stream, stills go out over /v1/recognize. Point it at another server with
- * ?server=https://host:port. A token, when the server requires one, comes from
- * sessionStorage (key dorayaki.token) or the fragment #token=..., never the query.
- */
-
-const params = new URLSearchParams(location.search);
-const SERVER = (params.get('server') || '').replace(/\/$/, '');
-const TOKEN = readToken();
-
-function readToken() {
-    const stored = sessionStorage.getItem('dorayaki.token') || '';
-    const fragment = new URLSearchParams((location.hash || '').replace(/^#/, ''));
-    const fromHash = fragment.get('token') || '';
-    if (fromHash) {
-        sessionStorage.setItem('dorayaki.token', fromHash);
-        history.replaceState(null, '', location.pathname + location.search);
-    }
-    return fromHash || stored;
-}
-
-const I18N = {
-    en: {
-        subtitle: 'Smart Supermarket Shelf AI & Inventory Recognition',
-        live: 'Live', photo: 'Photo', start: 'Start', stop: 'Stop',
-        capture: 'Take photo', upload: 'Upload image', empty: 'Empty spaces',
-        stageEmpty: 'Start the live scan, or open Photo mode to inspect a shelf image.',
-        cardTitle: 'Product details', cardPlaceholder: 'Tap a product on the shelf to see its details.',
-        metricOn: 'On shelf', metricOut: 'Sold / dispatched', metricReceipt: 'Received', metricExpiry: 'Expires',
-        dispense: 'Dispense 1 unit', matchTitle: 'Feature matching',
-        matchPlaceholder: 'Tap a product, then “Show matching”.', showMatch: 'Show matching',
-        inviewLive: 'In view', inviewPhoto: 'On this photo',
-        inviewPlaceholder: 'Recognized products appear here.',
-        cameraTitle: 'Take shelf photo', snapshot: 'Capture',
-        offline: 'Offline', connecting: 'Connecting', liveBadge: 'Live', photoBadge: 'Photo',
-        recognized: 'Recognized', needsConfirm: 'Check this', unrecognized: 'Not in catalog',
-        identifying: 'Identifying…', detected: 'Detecting…', emptySlot: 'Empty shelf space',
-        emptySlotNote: 'No product detected here', candidatesTitle: 'Possible matches',
-        hintGlare: 'Glare — tilt the camera', hintBlur: 'Hold steady', hintTooFar: 'Move closer',
-        noCamera: 'Live camera needs https or localhost.',
-        cameraDenied: 'Camera access was denied.',
-        connectionLost: 'Connection lost — retrying…', unknownMethod: 'Unknown algorithm',
-        noServer: 'Server unreachable', confirmEmpty: 'Hide empty spaces', showEmpty: 'Show empty spaces',
-        facings: 'facings', inView: 'in view', products: 'products',
-        ocrTitle: 'Price tag', ocrPrice: 'Tag price', ocrRegular: 'Regular price',
-        ocrDiscount: 'Discount', ocrUnit: 'Unit price', ocrName: 'Tag name',
-        ocrState: 'Tag', ocrUnreadable: 'unreadable — move closer', ocrAbsent: 'no tag under this product',
-        ocrNote: 'Note', row: 'Row', more: 'more',
-        recon_confirmed: 'tag matches', recon_mismatch: 'tag differs', recon_unverified: 'tag unread',
-        recon_no_tag: 'no tag', recon_tag_only: 'tag only',
-        rule_missing_price_tag: 'missing price tag', rule_missing_tag_group: 'group without price tag',
-        rule_orphan_tag: 'tag with no product', rule_price_mismatch: 'price differs from catalog',
-        rule_duplicate_tag: 'tag shared by two products',
-    },
-    ja: {
-        subtitle: 'スマートスーパーマーケット棚認識 & 在庫管理AI',
-        live: 'ライブ', photo: '写真', start: '開始', stop: '停止',
-        capture: 'カメラ撮影', upload: '画像アップロード', empty: '空きスペース',
-        stageEmpty: 'ライブスキャンを開始するか、写真モードで棚画像を確認してください。',
-        cardTitle: '商品情報', cardPlaceholder: '棚の商品をタップすると詳細が表示されます。',
-        metricOn: '陳列在庫数', metricOut: '販売済 / 出庫数', metricReceipt: '受入日', metricExpiry: '賞味期限',
-        dispense: '1点出庫 / 購入する', matchTitle: '特徴点マッチング',
-        matchPlaceholder: '商品をタップして「照合を表示」を押してください。', showMatch: '照合を表示',
-        inviewLive: '表示中の商品', inviewPhoto: 'この写真の商品',
-        inviewPlaceholder: '認識した商品がここに表示されます。',
-        cameraTitle: '棚の写真を撮影', snapshot: '撮影する',
-        offline: '未接続', connecting: '接続中', liveBadge: 'ライブ', photoBadge: '写真',
-        recognized: '認識済み', needsConfirm: '要確認', unrecognized: '未登録',
-        identifying: '識別中…', detected: '検出中…', emptySlot: '棚の空きスペース',
-        emptySlotNote: 'ここに商品はありません', candidatesTitle: '候補',
-        hintGlare: '反射しています — 角度を変えてください', hintBlur: 'カメラを固定してください', hintTooFar: '近づいてください',
-        noCamera: 'ライブカメラには https または localhost が必要です。',
-        cameraDenied: 'カメラの使用が許可されていません。',
-        connectionLost: '接続が切れました — 再接続中…', unknownMethod: '不明なアルゴリズム',
-        noServer: 'サーバーに接続できません', confirmEmpty: '空きスペースを隠す', showEmpty: '空きスペースを表示',
-        facings: 'フェイス', inView: '表示中', products: '商品',
-        ocrTitle: '値札', ocrPrice: '値札の価格', ocrRegular: '通常価格',
-        ocrDiscount: '割引', ocrUnit: '単位価格', ocrName: '値札の商品名',
-        ocrState: '値札', ocrUnreadable: '読み取れません — 近づいてください', ocrAbsent: '商品の下に値札がありません',
-        ocrNote: '備考', row: '段', more: '件',
-        recon_confirmed: '一致', recon_mismatch: '不一致', recon_unverified: '未読取',
-        recon_no_tag: '値札なし', recon_tag_only: '値札のみ',
-        rule_missing_price_tag: '値札なし', rule_missing_tag_group: '値札のない商品が連続',
-        rule_orphan_tag: '商品のない値札', rule_price_mismatch: 'カタログ価格と不一致',
-        rule_duplicate_tag: '同じ値札が2商品',
-    },
-};
-
-const state = {
-    lang: 'en',
-    mode: 'live',
-    method: 'hybrid',
-    showEmpty: true,
-    catalog: new Map(),
-    catalogVersion: '',
-    ws: null,
-    wsState: 'idle',
-    video: null,
-    stream: null,
-    frames: 0,
-    results: 0,
-    rtts: [],
-    detectMs: [],
-    dropped: 0,
-    lastFrameAt: 0,
-    inFlight: false,
-    inflightLimit: 1,
-    sendWidth: 1280,
-    quality: 0.75,
-    tracks: new Map(),      // track_id -> {box, target, state, identity, product, lastSeen}
-    slots: [],
-    selectedTrack: null,
-    selectedDetection: null,
-    currentImageId: null,
-    photoDetections: [],
-    photoWidth: 0,
-    photoHeight: 0,
-    reconnectDelay: 1000,
-    sendTimer: null,
-    lastHint: null,
-};
-
-const el = (id) => document.getElementById(id);
-const t = (key) => (I18N[state.lang] || I18N.en)[key] || key;
-
-function api(path) {
-    return (SERVER || '') + path;
-}
-
-function headers() {
-    return TOKEN ? { Authorization: `Bearer ${TOKEN}` } : {};
-}
-
-// ---------------------------------------------------------------- rendering
-
-const overlay = el('overlay');
-const ctx = overlay.getContext('2d');
-const videoEl = el('video');
-const photoEl = el('photo');
-state.video = videoEl;
-let displayWidth = 0;
-let displayHeight = 0;
-
-function setCanvasSize(width, height) {
-    displayWidth = width;
-    displayHeight = height;
-    overlay.width = width;
-    overlay.height = height;
-}
-
-function chip(text, x, y, background, color) {
-    ctx.font = '700 12px "Plus Jakarta Sans", "Noto Sans JP", sans-serif';
-    const width = ctx.measureText(text).width + 12;
-    const height = 18;
-    const boxY = Math.max(0, y - height - 3);
-    ctx.fillStyle = background;
-    ctx.beginPath();
-    ctx.roundRect(x, boxY, width, height, 6);
-    ctx.fill();
-    ctx.fillStyle = color;
-    ctx.fillText(text, x + 6, boxY + 13);
-}
-
-function drawBox(box, { color, dashed = false, fill = null, width = 2 }) {
-    ctx.save();
-    ctx.lineWidth = width;
-    ctx.strokeStyle = color;
-    if (dashed) ctx.setLineDash([6, 4]);
-    if (fill) {
-        ctx.fillStyle = fill;
-        ctx.fillRect(box[0], box[1], box[2] - box[0], box[3] - box[1]);
-    }
-    ctx.strokeRect(box[0], box[1], box[2] - box[0], box[3] - box[1]);
-    ctx.restore();
-}
-
-function render() {
-    ctx.clearRect(0, 0, overlay.width, overlay.height);
-    const now = performance.now();
-
-    if (state.mode === 'photo') {
-        // Draw the photo into the canvas so annotations cannot drift from it.
-        const photo = el('photo');
-        if (photo.complete && photo.naturalWidth) {
-            ctx.drawImage(photo, 0, 0, overlay.width, overlay.height);
-        }
-        if (state.photoDetections.length) drawPhotoDetections();
-    }
-
-    for (const [trackId, track] of state.tracks) {
-        const target = track.target;
-        if (!track.box) track.box = [...target];
-        for (let i = 0; i < 4; i += 1) {
-            track.box[i] += (target[i] - track.box[i]) * 0.35;
-        }
-        const age = now - track.lastSeen;
-        const alpha = Math.max(0, 1 - age / 400);
-        if (alpha <= 0.02) continue;
-
-        const identity = track.identity || { state: 'unconfirmed' };
-        const selected = state.selectedTrack === trackId;
-        ctx.globalAlpha = alpha;
-        if (identity.state === 'recognized') {
-            drawBox(track.box, { color: selected ? '#10b981' : '#22c55e', width: selected ? 4 : 2, fill: 'rgba(34,197,94,0.10)' });
-            const name = track.product ? track.product.product_name : identity.sku_id;
-            const price = track.product ? ` ¥${Math.round(track.product.price)}` : '';
-            chip(`${name}${price}`, track.box[0], track.box[1], '#22c55e', '#ffffff');
-        } else if (identity.state === 'needs_confirmation') {
-            drawBox(track.box, { color: '#f59e0b', width: 2, dashed: true, fill: 'rgba(245,158,11,0.08)' });
-            chip(t('needsConfirm'), track.box[0], track.box[1], '#f59e0b', '#ffffff');
-        } else if (identity.state === 'unrecognized') {
-            drawBox(track.box, { color: '#94a3b8', width: 2 });
-        } else if (identity.state === 'unconfirmed') {
-            drawBox(track.box, { color: 'rgba(226,232,240,0.55)', width: 1, dashed: true });
-        } else {
-            drawBox(track.box, { color: 'rgba(148,163,184,0.8)', width: 1, dashed: true });
-        }
-        ctx.globalAlpha = 1;
-    }
-
-    if (state.mode === 'live' || state.mode === 'photo') {
-        const tagSource = state.mode === 'live'
-            ? [...state.tracks.values()].map((track) => track.identity && track.priceTag).filter(Boolean)
-            : state.photoDetections.map((d) => d.price_tag).filter(Boolean);
-        for (const tag of tagSource) {
-            if (!tag.box) continue;
-            const readable = tag.state === 'readable';
-            drawBox(tag.box, {
-                color: readable ? '#0ea5e9' : 'rgba(14,165,233,0.5)',
-                width: readable ? 2 : 1,
-                dashed: !readable,
-            });
-            if (readable && tag.price !== null && tag.price !== undefined) {
-                chip(`¥${tag.price}`, tag.box[0], tag.box[1], '#0ea5e9', '#ffffff');
-            }
-        }
-    }
-
-    if (state.showEmpty && state.mode === 'live') {
-        for (const slot of state.slots) {
-            drawBox(slot.box, { color: '#ef4444', width: 3, fill: 'rgba(239,68,68,0.20)' });
-            chip(`EMPTY ~${slot.missing_items_est}`, slot.box[0], slot.box[1], '#ef4444', '#ffffff');
-        }
-    }
-    requestAnimationFrame(render);
-}
-requestAnimationFrame(render);
-
-function drawPhotoDetections() {
-    for (const detection of state.photoDetections) {
-        const identity = detection.identity || {};
-        const selected = state.selectedDetection === detection.id;
-        if (identity.state === 'recognized') {
-            drawBox(detection.box, { color: selected ? '#10b981' : '#22c55e', width: selected ? 4 : 2, fill: 'rgba(34,197,94,0.08)' });
-            chip(identity.sku_id || '', detection.box[0], detection.box[1], '#22c55e', '#fff');
-        } else if (identity.state === 'needs_confirmation') {
-            drawBox(detection.box, { color: '#f59e0b', width: 2, dashed: true });
-        } else if (identity.state === 'unrecognized') {
-            drawBox(detection.box, { color: '#94a3b8', width: 2 });
-        } else {
-            drawBox(detection.box, { color: '#22c55e', width: 2 });
-        }
-    }
-    if (state.showEmpty) {
-        for (const slot of state.slots) {
-            drawBox(slot.box, { color: '#ef4444', width: 3, fill: 'rgba(239,68,68,0.20)' });
-            chip(`EMPTY ~${slot.missing_items_est}`, slot.box[0], slot.box[1], '#ef4444', '#ffffff');
-        }
-    }
-}
-
-// ---------------------------------------------------------------- i18n / chrome
-
-function applyLanguage() {
-    document.querySelectorAll('[id^="t-"]').forEach((node) => {
-        const key = node.id.slice(2).replace(/-([a-z])/g, (m, c) => c.toUpperCase());
-        const value = t(key);
-        if (value && value !== key) node.textContent = value;
-    });
-    el('langText').textContent = state.lang === 'en' ? '日本語 / JA' : 'English / EN';
-    el('inViewCount').textContent = String(state.mode === 'live' ? state.tracks.size : state.photoDetections.length);
-    updateInViewTitle();
-    updateEmptyButton();
-    setConnection(state.wsState);
-}
-
-function updateInViewTitle() {
-    el('t-inview-title').textContent = state.mode === 'live' ? t('inviewLive') : t('inviewPhoto');
-}
-
-function updateEmptyButton() {
-    el('t-empty').textContent = state.showEmpty ? t('confirmEmpty') : t('showEmpty');
-}
-
-function setConnection(status) {
-    state.wsState = status;
-    const pill = el('connPill');
-    pill.dataset.state = status;
-    const label = { idle: t('offline'), connecting: t('connecting'), live: t('liveBadge'), photo: t('photoBadge'), error: t('noServer') }[status] || t('offline');
-    el('connText').textContent = label;
-}
-
-function setStatus(kind, text) {
-    const badge = el('statusBadge');
-    badge.className = `status-badge badge-${{ recognized: 'success', needs: 'warning', danger: 'danger' }[kind] || 'neutral'}`;
-    badge.textContent = text;
-}
-
-function showHint(kind, text) {
-    const banner = el('hintBanner');
-    if (!text) { banner.hidden = true; return; }
-    banner.hidden = false;
-    banner.dataset.kind = kind || 'info';
-    banner.textContent = text;
-}
-
-// ---------------------------------------------------------------- HUD
-
-function updateHud() {
-    const hud = el('hud');
-    if (state.mode !== 'live' || !state.results) {
-        hud.textContent = state.mode === 'live' ? '—' : `${state.photoDetections.length} ${t('products')}`;
-        return;
-    }
-    const rtt = state.rtts.length ? median(state.rtts.slice(-30)) : 0;
-    const detect = state.detectMs.length ? median(state.detectMs.slice(-30)) : 0;
-    hud.dataset.detail = hud.dataset.detail === '1' ? '1' : '0';
-    if (hud.dataset.detail === '1') {
-        hud.textContent = `${state.results} frames · rtt ${rtt.toFixed(0)}ms · detect ${detect.toFixed(0)}ms · dropped ${state.dropped}`;
-    } else {
-        hud.textContent = `${(state.results / Math.max(1, (performance.now() - state.startedAt) / 1000)).toFixed(1)}/s · ${rtt.toFixed(0)}ms`;
-    }
-}
-
-function median(values) {
-    const sorted = [...values].sort((a, b) => a - b);
-    return sorted[Math.floor(sorted.length / 2)] || 0;
-}
-setInterval(updateHud, 500);
-
-function clearCard() {
-    el('cardPlaceholder').hidden = false;
-    el('cardBody').hidden = true;
-    el('scoreLabel').textContent = '';
-    setStatus('neutral', '');
-}
-
-// ---------------------------------------------------------------- catalog
-
-async function loadCatalog() {
-    try {
-        const response = await fetch(api('/v1/catalog'), { headers: headers() });
-        if (!response.ok) throw new Error(`catalog ${response.status}`);
-        const data = await response.json();
-        state.catalogVersion = data.catalog_version;
-        state.catalog = new Map(data.products.map((product) => [product.sku_id, product]));
-        renderInView();
-    } catch (error) {
-        console.warn('catalog load failed', error);
-    }
-}
-
-function productFor(skuId) {
-    return skuId ? state.catalog.get(skuId) : null;
-}
-
-function thumbUrl(skuId) {
-    return productFor(skuId) ? api(`/v1/catalog/${skuId}/image`) : '';
-}
-
-// ---------------------------------------------------------------- live mode
-
-async function listCameras() {
-    const select = el('sourceSelect');
-    select.replaceChildren();
-    if (!navigator.mediaDevices || !window.isSecureContext) {
-        const option = document.createElement('option');
-        option.value = 'file';
-        option.textContent = t('noCamera');
-        select.appendChild(option);
-    } else {
-        try {
-            const devices = await navigator.mediaDevices.enumerateDevices();
-            for (const device of devices.filter((d) => d.kind === 'videoinput')) {
-                const option = document.createElement('option');
-                option.value = device.deviceId;
-                option.textContent = device.label || 'Camera';
-                select.appendChild(option);
-            }
-        } catch (error) { /* permission not granted yet */ }
-    }
-    const fileOption = document.createElement('option');
-    fileOption.value = 'file';
-    fileOption.textContent = '🎞 ' + (state.lang === 'en' ? 'Video file…' : '動画ファイル…');
-    select.appendChild(fileOption);
-}
-
-function connectStream() {
-    if (state.ws && (state.ws.readyState === WebSocket.OPEN || state.ws.readyState === WebSocket.CONNECTING)) return;
-    setConnection('connecting');
-    const base = SERVER || `${location.protocol === 'https:' ? 'wss' : 'ws'}://${location.host}`;
-    const url = `${base.replace(/^http/, 'ws')}/v1/stream`;
-    const socket = new WebSocket(url);
-    socket.binaryType = 'arraybuffer';
-    state.ws = socket;
-
-    socket.onopen = () => {
-        state.reconnectDelay = 1000;
-        // Browsers cannot set Authorization on a WebSocket. The server hello
-        // arrives first; this frame is the token, and it is not a URL.
-        if (TOKEN) socket.send(JSON.stringify({ type: 'auth', token: TOKEN }));
-        setConnection('live');
-        socket.send(JSON.stringify({ type: 'config', method: state.method, empty_slots: state.showEmpty }));
-        startSending();
-    };
-    socket.onmessage = (event) => {
-        const message = JSON.parse(event.data);
-        if (message.type === 'result') handleResult(message);
-        else if (message.type === 'inspect_result') handleInspect(message);
-        else if (message.type === 'error' && message.message) console.warn('server error', message);
-    };
-    socket.onclose = () => {
-        stopSending();
-        if (state.mode === 'live' && state.stream) {
-            setConnection('error');
-            setTimeout(connectStream, state.reconnectDelay);
-            state.reconnectDelay = Math.min(10000, state.reconnectDelay * 2);
-        } else {
-            setConnection('idle');
-        }
-    };
-    socket.onerror = () => setConnection('error');
-}
-
-function startSending() {
-    stopSending();
-    state.startedAt = performance.now();
-    state.sendTimer = setInterval(sendFrame, 1000 / 30);
-}
-
-function stopSending() {
-    if (state.sendTimer) clearInterval(state.sendTimer);
-    state.sendTimer = null;
-    state.inFlight = false;
-}
-
-function sendFrame() {
-    const video = state.video;
-    if (!video || video.readyState < 2 || document.hidden) return;
-    if (state.inFlight && state.inflightLimit <= 1) return;
-    if (!state.ws || state.ws.readyState !== WebSocket.OPEN) return;
-
-    const sourceWidth = video.videoWidth;
-    const sourceHeight = video.videoHeight;
-    if (!sourceWidth || !sourceHeight) return;
-    const width = Math.min(state.sendWidth, sourceWidth);
-    const height = Math.round(sourceHeight * (width / sourceWidth));
-    const canvas = state.captureCanvas || (state.captureCanvas = document.createElement('canvas'));
-    canvas.width = width;
-    canvas.height = height;
-    const captureCtx = canvas.getContext('2d');
-    captureCtx.drawImage(video, 0, 0, width, height);
-    canvas.toBlob((blob) => {
-        if (!blob) return;
-        blob.arrayBuffer().then((buffer) => {
-            const frameId = ++state.frames & 0xffffffff;
-            const payload = new Uint8Array(4 + buffer.byteLength);
-            new DataView(payload.buffer).setUint32(0, frameId, false);
-            payload.set(new Uint8Array(buffer), 4);
-            state.ws.send(payload);
-            state.frameSentAt = new Map([[frameId, performance.now()]]);
-            state.inFlight = true;
-        });
-    }, 'image/jpeg', state.quality);
-}
-
-function renderCompliance(compliance) {
-    const strip = el('complianceStrip');
-    strip.replaceChildren();
-    if (!compliance) { strip.hidden = true; return; }
-    const rows = compliance.rows || [];
-    const hints = compliance.hints || [];
-    const chips = [];
-    for (const row of rows.slice(0, 3)) {
-        chips.push(`⚠ ${t('rule_' + row.rule)} · ${t('row')} ${row.row_index} (${row.missing})`);
-    }
-    if (rows.length > 3) chips.push(`+${rows.length - 3} ${t('more')}`);
-    const texts = chips.length ? chips : hints.map((hint) => hint.detail);
-    if (!texts.length) { strip.hidden = true; return; }
-    strip.hidden = false;
-    strip.dataset.kind = chips.length ? 'violation' : 'hint';
-    for (const text of texts) {
-        const item = document.createElement('span');
-        item.className = 'compliance-item';
-        item.textContent = text;
-        strip.appendChild(item);
-    }
-}
-
-function handleResult(message) {
-    state.results += 1;
-    state.inFlight = false;
-    const sentAt = state.frameSentAt && state.frameSentAt.get(message.frame_id);
-    if (sentAt) state.rtts.push(performance.now() - sentAt);
-    state.detectMs.push(message.timing_ms.detect);
-    state.dropped = message.stats.frames_dropped;
-
-    const seen = new Set();
-    for (const track of message.tracks) {
-        seen.add(track.track_id);
-        const existing = state.tracks.get(track.track_id);
-        state.tracks.set(track.track_id, {
-            box: existing ? existing.box : [...track.box],
-            target: track.box,
-            identity: track.identity,
-            priceTag: track.price_tag || null,
-            reconciliation: track.reconciliation || null,
-            product: track.product || productFor(track.identity.sku_id),
-            lastSeen: performance.now(),
-        });
-    }
-    for (const [trackId, track] of state.tracks) {
-        if (!seen.has(trackId) && performance.now() - track.lastSeen > 400) state.tracks.delete(trackId);
-    }
-    state.slots = message.empty_slots || [];
-    el('inViewCount').textContent = String(message.summary.in_view);
-
-    const hint = message.quality && message.quality.hint;
-    if (hint !== state.lastHint) {
-        state.lastHint = hint;
-        if (hint === 'glare') showHint('warning', t('hintGlare'));
-        else if (hint === 'blur') showHint('warning', t('hintBlur'));
-        else if (hint === 'too_far') showHint('warning', t('hintTooFar'));
-        else showHint(null, '');
-    }
-    renderCompliance(message.summary && message.summary.compliance);
-    renderInView();
-    if (state.selectedTrack !== null && !state.cardPinned) {
-        const track = state.tracks.get(state.selectedTrack);
-        if (track) showTrackCard(track);
-    }
-}
-
-// ---------------------------------------------------------------- photo mode
-
-async function loadSamples() {
-    try {
-        const response = await fetch(api('/v1/samples'), { headers: headers() });
-        const data = await response.json();
-        const select = el('sampleSelect');
-        select.replaceChildren();
-        for (const sample of data.samples) {
-            const option = document.createElement('option');
-            option.value = sample.id;
-            option.textContent = sample.label;
-            select.appendChild(option);
-        }
-        if (data.samples.length) loadSample(data.samples[0].id);
-    } catch (error) {
-        console.warn('samples failed', error);
-    }
-}
-
-async function loadSample(sampleId) {
-    const response = await fetch(api(`/v1/samples/${sampleId}`), { headers: headers() });
-    const blob = await response.blob();
-    const url = URL.createObjectURL(blob);
-    await recognizeBlob(blob, url);
-}
-
-async function recognizeBlob(blob, previewUrl) {
-    setConnection('photo');
-    setStatus('neutral', t('identifying'));
-    const photo = el('photo');
-    photo.src = previewUrl || URL.createObjectURL(blob);
-    photo.hidden = true;          // the canvas draws it
-    el('video').hidden = true;
-    el('stageEmpty').hidden = true;
-    await photo.decode().catch(() => {});
-
-    const response = await fetch(api('/v1/recognize?classify=false&empty_slots=true&tags=true'), {
-        method: 'POST',
-        headers: { 'Content-Type': 'image/jpeg', ...headers() },
-        body: blob,
-    });
-    if (!response.ok) {
-        setStatus('danger', t('noServer'));
-        return;
-    }
-    const data = await response.json();
-    state.currentImageId = data.image_id;
-    state.photoDetections = data.detections;
-    state.photoWidth = data.width;
-    state.photoHeight = data.height;
-    state.slots = data.empty_slots || [];
-    const cap = 2048;
-    const naturalWidth = photo.naturalWidth || data.width;
-    const naturalHeight = photo.naturalHeight || data.height;
-    const scale = Math.min(1, cap / Math.max(1, naturalWidth));
-    setCanvasSize(Math.round(naturalWidth * scale), Math.round(naturalHeight * scale));
-    el('inViewCount').textContent = String(data.detections.length);
-    renderCompliance(data.compliance);
-    setStatus('neutral', t('detected'));
-    classifyPhoto();
-}
-
-async function classifyPhoto(detectionIds = null) {
-    if (!state.currentImageId) return;
-    setStatus('neutral', t('identifying'));
-    const response = await fetch(api(`/v1/images/${state.currentImageId}/classify`), {
-        method: 'POST',
-        headers: { 'Content-Type': 'application/json', ...headers() },
-        body: JSON.stringify({ method: state.method, detection_ids: detectionIds }),
-    });
-    if (!response.ok) { setStatus('danger', t('noServer')); return; }
-    const data = await response.json();
-    const byId = new Map(data.identities.map((row) => [row.detection_id, row]));
-    for (const detection of state.photoDetections) {
-        const row = byId.get(detection.id);
-        if (!row) continue;
-        detection.identity = row.identity;
-        if (row.reconciliation) detection.reconciliation = row.reconciliation;
-        if (row.price_tag) detection.price_tag = row.price_tag;
-    }
-    const summary = data.summary || {};
-    renderCompliance(data.compliance);
-    setStatus('recognized', `${summary.recognized || 0} / ${data.identities.length}`);
-    renderInView();
-}
-
-function renderInView() {
-    const list = el('inViewList');
-    const counts = new Map();
-    if (state.mode === 'live') {
-        for (const track of state.tracks.values()) {
-            const identity = track.identity || {};
-            if (identity.state === 'recognized' && identity.sku_id) {
-                counts.set(identity.sku_id, (counts.get(identity.sku_id) || 0) + 1);
-            }
-        }
-    } else {
-        for (const detection of state.photoDetections) {
-            const identity = detection.identity || {};
-            if (identity.state === 'recognized' && identity.sku_id) {
-                counts.set(identity.sku_id, (counts.get(identity.sku_id) || 0) + 1);
-            }
-        }
-    }
-    list.replaceChildren();
-    if (!counts.size) {
-        const empty = document.createElement('div');
-        empty.className = 'placeholder small';
-        empty.textContent = t('inviewPlaceholder');
-        list.appendChild(empty);
-        return;
-    }
-    const ranked = [...counts.entries()].sort((a, b) => b[1] - a[1]);
-    for (const [skuId, facings] of ranked) {
-        const product = productFor(skuId);
-        const name = product ? product.product_name : skuId;
-        const price = product ? `¥${Math.round(product.price)}` : '';
-        const row = document.createElement('div');
-        row.className = 'inview-row';
-        row.dataset.sku = skuId;
-        const thumb = document.createElement('img');
-        thumb.className = 'inview-thumb';
-        thumb.alt = '';
-        thumb.src = thumbUrl(skuId);
-        const text = document.createElement('div');
-        text.className = 'inview-text';
-        const nameEl = document.createElement('div');
-        nameEl.className = 'inview-name';
-        nameEl.textContent = name;
-        const meta = document.createElement('div');
-        meta.className = 'inview-meta';
-        meta.textContent = `${price} · ${t('facings')} ${facings}`;
-        text.append(nameEl, meta);
-        const count = document.createElement('div');
-        count.className = 'inview-facings';
-        count.textContent = String(facings);
-        row.append(thumb, text, count);
-        row.addEventListener('click', () => selectBySku(skuId));
-        list.appendChild(row);
-    }
-}
-
-function selectBySku(skuId) {
-    if (state.mode === 'photo') {
-        const detection = state.photoDetections.find((d) => (d.identity || {}).sku_id === skuId);
-        if (detection) selectPhotoDetection(detection);
-        return;
-    }
-    for (const [trackId, track] of state.tracks) {
-        const identity = track.identity || {};
-        if (identity.sku_id === skuId) {
-            state.selectedTrack = trackId;
-            // Show the product this row names, even if the track has since moved
-            // to a neighbour: the row is the user's intent. Pinning stops the
-            // per-frame refresh from replacing the card the user just opened.
-            state.cardPinned = true;
-            showProductCard(productFor(skuId), identity);
-            break;
-        }
-    }
-}
-
-// ---------------------------------------------------------------- product card
-
-function showTrackCard(track) {
-    const identity = track.identity || {};
-    state.currentTag = track.priceTag || null;
-    state.currentReconciliation = track.reconciliation || null;
-    if (identity.state === 'recognized') {
-        const product = track.product || productFor(identity.sku_id);
-        showProductCard(product, identity);
-    } else if (identity.state === 'needs_confirmation') {
-        showCandidatesCard(identity);
-    } else if (identity.state === 'unrecognized') {
-        showPlainCard(t('unrecognized'), t('emptySlotNote'));
-    } else {
-        showPlainCard(t('detected'), '');
-    }
-}
-
-function renderOcrSection(tag, reconciliation) {
-    const section = el('ocrSection');
-    const chipEl = el('reconChip');
-    const rows = el('ocrRows');
-    if (!tag && !reconciliation) { section.hidden = true; return; }
-    section.hidden = false;
-    const state = (reconciliation || {}).state;
-    chipEl.dataset.state = state || 'unverified';
-    chipEl.textContent = t(`recon_${state || 'unverified'}`);
-    const lines = [];
-    if (tag) {
-        if (tag.price !== null && tag.price !== undefined) {
-            const bad = state === 'mismatch';
-            lines.push([t('ocrPrice'), `¥${tag.price}`, bad]);
-        }
-        if (tag.regular_price) lines.push([t('ocrRegular'), `¥${tag.regular_price}`, false]);
-        if (tag.discount) lines.push([t('ocrDiscount'), `${Math.round(tag.discount * 100)}%`, false]);
-        if (tag.unit_price) lines.push([t('ocrUnit'), `${tag.unit_price}`, false]);
-        if (tag.name) lines.push([t('ocrName'), tag.name, false]);
-        if (tag.state === 'unreadable') lines.push([t('ocrState'), t('ocrUnreadable'), true]);
-        if (tag.state === 'absent') lines.push([t('ocrState'), t('ocrAbsent'), false]);
-    }
-    if (reconciliation && reconciliation.message) lines.push([t('ocrNote'), reconciliation.message, state === 'mismatch']);
-    rows.replaceChildren();
-    for (const [key, value, bad] of lines) {
-        const row = document.createElement('div');
-        row.className = 'ocr-row';
-        const keyEl = document.createElement('span');
-        keyEl.className = 'ocr-key';
-        keyEl.textContent = key;
-        const valueEl = document.createElement('span');
-        valueEl.className = bad ? 'ocr-val bad' : 'ocr-val';
-        valueEl.textContent = value;
-        row.append(keyEl, valueEl);
-        rows.appendChild(row);
-    }
-}
-
-function showProductCard(product, identity) {
-    if (!product) { showPlainCard(t('detected'), ''); return; }
-    el('cardPlaceholder').hidden = true;
-    el('cardBody').hidden = false;
-    el('productSku').hidden = false;
-    el('productSku').textContent = product.sku_id;
-    el('productName').textContent = product.product_name;
-    el('productPrice').textContent = `¥${Math.round(product.price)}`;
-    el('productThumb').src = thumbUrl(product.sku_id);
-    el('productThumb').hidden = false;
-    el('metricOn').textContent = product.number_on;
-    el('metricOut').textContent = product.number_out;
-    el('metricReceipt').textContent = product.receipt_date || '—';
-    el('metricExpiry').textContent = product.expiration_date || '—';
-    el('candidates').hidden = true;
-    el('showMatchBtn').disabled = false;
-    const confidence = identity.confidence ? ` · ${(identity.confidence * 100).toFixed(0)}%` : '';
-    el('scoreLabel').textContent = identity.score ? `score ${identity.score}${confidence}` : '';
-    renderOcrSection(state.currentTag, state.currentReconciliation);
-    setStatus('recognized', t('recognized'));
-}
-
-function showCandidatesCard(identity) {
-    el('cardPlaceholder').hidden = true;
-    el('cardBody').hidden = false;
-    el('productSku').hidden = true;
-    el('productThumb').hidden = true;
-    el('productName').textContent = t('needsConfirm');
-    el('productPrice').textContent = '—';
-    el('productThumb').src = '';
-    el('metricOn').textContent = '—';
-    el('metricOut').textContent = '—';
-    el('metricReceipt').textContent = '—';
-    el('metricExpiry').textContent = '—';
-    const candidates = el('candidates');
-    candidates.replaceChildren();
-    const title = document.createElement('div');
-    title.className = 'candidates-title';
-    title.textContent = t('candidatesTitle');
-    candidates.appendChild(title);
-    for (const candidate of identity.candidates || []) {
-        const product = productFor(candidate.sku_id);
-        const row = document.createElement('div');
-        row.className = 'candidate-row';
-        const name = document.createElement('span');
-        name.textContent = product ? product.product_name : candidate.sku_id;
-        const score = document.createElement('span');
-        score.textContent = String(candidate.score);
-        row.append(name, score);
-        candidates.appendChild(row);
-    }
-    candidates.hidden = false;
-    el('showMatchBtn').disabled = false;
-    renderOcrSection(state.currentTag, state.currentReconciliation);
-    setStatus('needs', t('needsConfirm'));
-}
-
-function showPlainCard(title, note) {
-    el('cardPlaceholder').hidden = true;
-    el('cardBody').hidden = false;
-    el('productSku').hidden = true;
-    el('productThumb').hidden = true;
-    el('productName').textContent = title;
-    el('productPrice').textContent = '';
-    el('productThumb').src = '';
-    el('metricOn').textContent = '—';
-    el('metricOut').textContent = '—';
-    el('metricReceipt').textContent = '—';
-    el('metricExpiry').textContent = '—';
-    el('candidates').hidden = true;
-    el('showMatchBtn').disabled = true;
-    renderOcrSection(state.currentTag, state.currentReconciliation);
-    setStatus('danger', title);
-    if (note) el('productPrice').textContent = note;
-}
-
-// ---------------------------------------------------------------- interaction
-
-function stagePointToImage(event) {
-    const rect = overlay.getBoundingClientRect();
-    const scaleX = overlay.width / rect.width;
-    const scaleY = overlay.height / rect.height;
-    return [(event.clientX - rect.left) * scaleX, (event.clientY - rect.top) * scaleY];
-}
-
-overlay.addEventListener('click', (event) => {
-    const [x, y] = stagePointToImage(event);
-    if (state.mode === 'live') {
-        let best = null;
-        for (const [trackId, track] of state.tracks) {
-            const [x1, y1, x2, y2] = track.target;
-            if (x >= x1 && x <= x2 && y >= y1 && y <= y2) {
-                const area = (x2 - x1) * (y2 - y1);
-                if (!best || area < best.area) best = { trackId, track, area };
-            }
-        }
-        if (best) {
-            state.selectedTrack = best.trackId;
-            state.selectedDetection = null;
-            state.cardPinned = false;
-            showTrackCard(best.track);
-        }
-        return;
-    }
-    if (state.mode === 'photo') {
-        // The canvas backing store matches the server's pixel space (both scaled
-        // from the same image), so no extra transform is needed.
-        const px = x * (state.photoWidth / Math.max(1, overlay.width));
-        const py = y * (state.photoHeight / Math.max(1, overlay.height));
-        let best = null;
-        for (const detection of state.photoDetections) {
-            const [x1, y1, x2, y2] = detection.box;
-            if (px >= x1 && px <= x2 && py >= y1 && py <= y2) {
-                const area = (x2 - x1) * (y2 - y1);
-                if (!best || area < best.area) best = { detection, area };
-            }
-        }
-        if (best) selectPhotoDetection(best.detection);
-        else {
-            const slot = state.slots.find((s) => {
-                const [x1, y1, x2, y2] = s.box;
-                return px >= x1 && px <= x2 && py >= y1 && py <= y2;
-            });
-            if (slot) showEmptySlotCard(slot);
-        }
-    }
-});
-
-function photoPointToDetectionSpace(x, y) {
-    const photo = el('photo');
-    const scaleX = state.photoWidth / (photo.naturalWidth || state.photoWidth);
-    const scaleY = state.photoHeight / (photo.naturalHeight || state.photoHeight);
-    return [x * scaleX, y * scaleY];
-}
-
-function selectPhotoDetection(detection) {
-    state.selectedDetection = detection.id;
-    state.selectedTrack = null;
-    state.currentTag = detection.price_tag || null;
-    state.currentReconciliation = detection.reconciliation || null;
-    const identity = detection.identity || {};
-    if (identity.state === 'recognized') showProductCard(productFor(identity.sku_id), identity);
-    else if (identity.state === 'needs_confirmation') showCandidatesCard(identity);
-    else showPlainCard(t(identity.state === 'unrecognized' ? 'unrecognized' : 'detected'), '');
-    inspectCurrent(detection.box);
-}
-
-function showEmptySlotCard(slot) {
-    el('cardPlaceholder').hidden = true;
-    el('cardBody').hidden = false;
-    el('productSku').hidden = true;
-    el('productThumb').hidden = true;
-    el('productName').textContent = t('emptySlot');
-    el('productPrice').textContent = t('emptySlotNote');
-    el('productThumb').src = '';
-    el('metricOn').textContent = '0';
-    el('metricOut').textContent = `~${slot.missing_items_est}`;
-    el('metricReceipt').textContent = `row ${slot.row_index}`;
-    el('metricExpiry').textContent = `${slot.width}×${slot.height}px`;
-    el('candidates').hidden = true;
-    el('showMatchBtn').disabled = true;
-    setStatus('danger', t('emptySlot'));
-}
-
-async function inspectCurrent(box) {
-    if (!state.currentImageId || !box) return;
-    const response = await fetch(api(`/v1/images/${state.currentImageId}/inspect`), {
-        method: 'POST',
-        headers: { 'Content-Type': 'application/json', ...headers() },
-        body: JSON.stringify({ box, method: state.method }),
-    });
-    if (!response.ok) return;
-    handleInspect(await response.json());
-}
-
-function handleInspect(result) {
-    if (result.crop_jpeg_b64) {
-        el('productThumb').src = result.crop_jpeg_b64;
-    }
-    const box = el('matchBox');
-    box.replaceChildren();
-    if (result.match_visualization_jpeg_b64) {
-        const image = document.createElement('img');
-        image.alt = 'feature matches';
-        image.src = result.match_visualization_jpeg_b64;
-        box.appendChild(image);
-    } else {
-        const placeholder = document.createElement('span');
-        placeholder.className = 'match-placeholder';
-        placeholder.textContent = t('matchPlaceholder');
-        box.appendChild(placeholder);
-    }
-    const identity = result.identity || {};
-    const parts = [];
-    if (identity.score) parts.push(`score ${identity.score}`);
-    if (identity.confidence) parts.push(`confidence ${(identity.confidence * 100).toFixed(0)}%`);
-    if (result.visualized_sku_id) parts.push(`${result.is_candidate ? 'candidate' : 'match'} ${result.visualized_sku_id}`);
-    el('scoreLabel').textContent = parts.join(' · ');
-}
-
-el('showMatchBtn').addEventListener('click', () => {
-    if (state.mode === 'live' && state.selectedTrack !== null) {
-        const track = state.tracks.get(state.selectedTrack);
-        if (track && state.ws && state.ws.readyState === WebSocket.OPEN) {
-            state.ws.send(JSON.stringify({ type: 'inspect', track_id: state.selectedTrack, method: state.method, request_id: String(Date.now()) }));
-        }
-    } else if (state.mode === 'photo' && state.selectedDetection !== null) {
-        const detection = state.photoDetections.find((d) => d.id === state.selectedDetection);
-        if (detection) inspectCurrent(detection.box);
-    }
-});
-
-// ---------------------------------------------------------------- controls
-
-el('modeControl').addEventListener('click', (event) => {
-    const button = event.target.closest('.seg-btn');
-    if (!button) return;
-    setMode(button.dataset.mode);
-});
-
-function setMode(mode) {
-    state.mode = mode;
-    document.querySelectorAll('#modeControl .seg-btn').forEach((b) => b.classList.toggle('active', b.dataset.mode === mode));
-    el('liveControls').hidden = mode !== 'live';
-    el('photoControls').hidden = mode === 'live';
-    el('video').hidden = mode !== 'live';
-    el('photo').hidden = true;
-    state.tracks.clear();
-    state.slots = [];
-    state.photoDetections = [];
-    state.selectedTrack = null;
-    state.selectedDetection = null;
-    state.cardPinned = false;
-    el('stageEmpty').hidden = false;
-    clearCard();
-    updateInViewTitle();
-    if (mode === 'photo') {
-        setConnection('photo');
-        loadSamples();
-    } else {
-        stopCamera();
-        setConnection(state.ws && state.ws.readyState === WebSocket.OPEN ? 'live' : 'idle');
-    }
-    el('inViewCount').textContent = '0';
-    renderInView();
-}
-
-el('methodControl').addEventListener('click', async (event) => {
-    const button = event.target.closest('.seg-btn');
-    if (!button) return;
-    state.method = button.dataset.method;
-    document.querySelectorAll('#methodControl .seg-btn').forEach((b) => b.classList.toggle('active', b === button));
-    if (state.mode === 'live' && state.ws && state.ws.readyState === WebSocket.OPEN) {
-        state.ws.send(JSON.stringify({ type: 'config', method: state.method }));
-        state.tracks.clear();
-    } else if (state.mode === 'photo') {
-        await classifyPhoto();
-    }
-});
-
-el('startBtn').addEventListener('click', async () => {
-    const source = el('sourceSelect').value;
-    if (source === 'file') { pickVideoFile(); return; }
-    try {
-        state.stream = await navigator.mediaDevices.getUserMedia({
-            video: { deviceId: source ? { exact: source } : undefined, facingMode: { ideal: 'environment' }, width: { ideal: 1280 }, height: { ideal: 720 } },
-            audio: false,
-        });
-    } catch (error) {
-        showHint('danger', error.name === 'NotAllowedError' ? t('cameraDenied') : t('noCamera'));
-        return;
-    }
-    el('video').srcObject = state.stream;
-    videoEl.hidden = false;
-    photoEl.hidden = true;
-    el('stageEmpty').hidden = true;
-    await videoEl.play().catch(() => {});
-    setCanvasSize(videoEl.videoWidth || 1280, videoEl.videoHeight || 720);
-    el('startBtn').disabled = true;
-    el('stopBtn').disabled = false;
-    connectStream();
-    listCameras();
-});
-
-el('stopBtn').addEventListener('click', () => {
-    stopCamera();
-    if (state.ws) state.ws.close();
-    setConnection('idle');
-    el('stageEmpty').hidden = false;
-});
-
-function stopCamera() {
-    stopSending();
-    if (state.stream) {
-        state.stream.getTracks().forEach((track) => track.stop());
-        state.stream = null;
-    }
-    el('video').srcObject = null;
-    el('startBtn').disabled = false;
-    el('stopBtn').disabled = true;
-}
-
-function pickVideoFile() {
-    const input = document.createElement('input');
-    input.type = 'file';
-    input.accept = 'video/*';
-    input.onchange = async () => {
-        const file = input.files[0];
-        if (!file) return;
-        const url = URL.createObjectURL(file);
-        const video = el('video');
-        video.srcObject = null;
-        video.src = url;
-        video.loop = true;
-        video.muted = true;
-        video.hidden = false;
-        el('photo').hidden = true;
-        el('stageEmpty').hidden = true;
-        await video.play().catch(() => {});
-        setCanvasSize(video.videoWidth || 1280, video.videoHeight || 720);
-        el('startBtn').disabled = true;
-        el('stopBtn').disabled = false;
-        connectStream();
-    };
-    input.click();
-}
-
-el('emptyBtn').addEventListener('click', () => {
-    state.showEmpty = !state.showEmpty;
-    updateEmptyButton();
-    if (state.mode === 'live' && state.ws && state.ws.readyState === WebSocket.OPEN) {
-        state.ws.send(JSON.stringify({ type: 'config', empty_slots: state.showEmpty }));
-    }
-});
-
-el('sampleSelect').addEventListener('change', (event) => loadSample(event.target.value));
-el('uploadBtn').addEventListener('click', () => el('fileInput').click());
-el('fileInput').addEventListener('change', (event) => {
-    const file = event.target.files[0];
-    if (file) recognizeBlob(file);
-});
-
-el('captureBtn').addEventListener('click', async () => {
-    el('cameraModal').classList.add('open');
-    try {
-        state.cameraStream = await navigator.mediaDevices.getUserMedia({ video: { facingMode: { ideal: 'environment' }, width: { ideal: 1280 } }, audio: false });
-        el('cameraVideo').srcObject = state.cameraStream;
-    } catch (error) {
-        showHint('danger', t('cameraDenied'));
-        el('cameraModal').classList.remove('open');
-    }
-});
-el('cameraClose').addEventListener('click', closeCameraModal);
-el('snapshotBtn').addEventListener('click', () => {
-    const video = el('cameraVideo');
-    const canvas = document.createElement('canvas');
-    canvas.width = video.videoWidth || 1280;
-    canvas.height = video.videoHeight || 720;
-    canvas.getContext('2d').drawImage(video, 0, 0, canvas.width, canvas.height);
-    canvas.toBlob((blob) => {
-        closeCameraModal();
-        if (blob) recognizeBlob(blob);
-    }, 'image/jpeg', 0.9);
-});
-function closeCameraModal() {
-    el('cameraModal').classList.remove('open');
-    if (state.cameraStream) {
-        state.cameraStream.getTracks().forEach((track) => track.stop());
-        state.cameraStream = null;
-    }
-}
-
-el('dispenseBtn').addEventListener('click', async () => {
-    const sku = el('productSku').textContent;
-    if (!sku || sku === '—') return;
-    const response = await fetch(api(`/v1/catalog/${sku}/stock`), {
-        method: 'POST',
-        headers: { 'Content-Type': 'application/json', ...headers() },
-        body: JSON.stringify({ delta: -1 }),
-    });
-    if (!response.ok) return;
-    const data = await response.json();
-    el('metricOn').textContent = data.product.number_on;
-    el('metricOut').textContent = data.product.number_out;
-    state.catalog.set(data.product.sku_id, data.product);
-    renderInView();
-});
-
-el('langBtn').addEventListener('click', () => {
-    state.lang = state.lang === 'en' ? 'ja' : 'en';
-    applyLanguage();
-    listCameras();
-});
-
-el('hud').addEventListener('click', () => {
-    el('hud').dataset.detail = el('hud').dataset.detail === '1' ? '0' : '1';
-    updateHud();
-});
-
-document.addEventListener('visibilitychange', () => {
-    if (document.hidden) stopSending();
-    else if (state.mode === 'live' && state.stream) startSending();
-});
-
-// ---------------------------------------------------------------- boot
-
-applyLanguage();
-loadCatalog();
-setMode('live');
-listCameras();
diff --git a/recognization/code/web/index.html b/recognization/code/web/index.html
deleted file mode 100644
index 8e96ea5..0000000
--- a/recognization/code/web/index.html
+++ /dev/null
@@ -1,179 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>Dorayaki - Smart Supermarket Shelf AI</title>
-    <link rel="icon" href="data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 100 100'><ellipse cx='50' cy='55' rx='44' ry='24' fill='%23f59e0b'/><ellipse cx='50' cy='45' rx='42' ry='20' fill='%23b45309'/></svg>">
-    <link rel="preconnect" href="https://fonts.googleapis.com">
-    <link href="https://fonts.googleapis.com/css2?family=Plus+Jakarta+Sans:wght@400;500;600;700;800&family=Noto+Sans+JP:wght@400;500;700&display=swap" rel="stylesheet">
-    <link rel="stylesheet" href="styles.css">
-</head>
-<body>
-    <header>
-        <div class="brand">
-            <svg class="brand-logo-svg" viewBox="0 0 100 100" xmlns="http://www.w3.org/2000/svg">
-                <defs>
-                    <linearGradient id="pancakeTop" x1="0%" y1="0%" x2="0%" y2="100%">
-                        <stop offset="0%" stop-color="#f59e0b"/>
-                        <stop offset="100%" stop-color="#b45309"/>
-                    </linearGradient>
-                    <linearGradient id="ankoFill" x1="0%" y1="0%" x2="0%" y2="100%">
-                        <stop offset="0%" stop-color="#450a0a"/>
-                        <stop offset="100%" stop-color="#2d0606"/>
-                    </linearGradient>
-                    <linearGradient id="pancakeBottom" x1="0%" y1="0%" x2="0%" y2="100%">
-                        <stop offset="0%" stop-color="#d97706"/>
-                        <stop offset="100%" stop-color="#92400e"/>
-                    </linearGradient>
-                </defs>
-                <ellipse cx="50" cy="62" rx="42" ry="18" fill="url(#pancakeBottom)" />
-                <ellipse cx="50" cy="53" rx="43" ry="14" fill="url(#ankoFill)" />
-                <ellipse cx="50" cy="42" rx="44" ry="22" fill="url(#pancakeTop)" />
-                <ellipse cx="44" cy="35" rx="20" ry="8" fill="#fde68a" opacity="0.4" />
-                <path d="M 52,18 C 65,12 72,25 65,30 C 58,28 54,23 52,18 Z" fill="#22c55e" />
-            </svg>
-            <div>
-                <div class="brand-title">Dorayaki <span class="brand-jp">どら焼き</span></div>
-                <div class="brand-sub" id="t-subtitle">Smart Supermarket Shelf AI &amp; Inventory Recognition</div>
-            </div>
-        </div>
-        <div class="header-actions">
-            <span class="conn-pill" id="connPill" data-state="idle"><span class="dot"></span><span id="connText">Offline</span></span>
-            <button class="lang-switch-btn" id="langBtn" type="button">
-                <span>🌐</span> <span id="langText">日本語 / JA</span>
-            </button>
-        </div>
-    </header>
-
-    <main>
-        <section class="stage-panel">
-            <div class="toolbar">
-                <div class="toolbar-group">
-                    <div class="segmented" id="modeControl">
-                        <button class="seg-btn active" data-mode="live" type="button" id="t-mode-live">Live</button>
-                        <button class="seg-btn" data-mode="photo" type="button" id="t-mode-photo">Photo</button>
-                    </div>
-                    <div class="segmented" id="methodControl">
-                        <button class="seg-btn active" data-method="hybrid" type="button">Hybrid</button>
-                        <button class="seg-btn" data-method="surf" type="button">SURF</button>
-                        <button class="seg-btn" data-method="orb" type="button">ORB</button>
-                        <button class="seg-btn" data-method="brisk" type="button">BRISK</button>
-                    </div>
-                </div>
-                <div class="toolbar-group" id="liveControls">
-                    <select id="sourceSelect" class="select" title="Camera or video file"></select>
-                    <button class="tool-btn primary" id="startBtn" type="button" id-label="start">▶ <span id="t-start">Start</span></button>
-                    <button class="tool-btn" id="stopBtn" type="button" disabled>■ <span id="t-stop">Stop</span></button>
-                    <div class="hud" id="hud" title="Click for detail">--</div>
-                </div>
-                <div class="toolbar-group" id="photoControls" hidden>
-                    <select id="sampleSelect" class="select"></select>
-                    <button class="tool-btn" id="captureBtn" type="button">📷 <span id="t-capture">Take photo</span></button>
-                    <button class="tool-btn" id="uploadBtn" type="button">📁 <span id="t-upload">Upload image</span></button>
-                    <input type="file" id="fileInput" accept="image/*" hidden>
-                </div>
-                <div class="toolbar-group">
-                    <button class="tool-btn danger" id="emptyBtn" type="button">🔴 <span id="t-empty">Empty spaces</span></button>
-                </div>
-            </div>
-
-            <div class="stage" id="stage">
-                <video id="video" playsinline muted></video>
-                <img id="photo" alt="" hidden>
-                <canvas id="overlay"></canvas>
-                <div class="stage-empty" id="stageEmpty">
-                    <div class="stage-empty-icon">📷</div>
-                    <div id="t-stage-empty">Start the live scan, or open Photo mode to inspect a shelf image.</div>
-                </div>
-                <div class="hint-banner" id="hintBanner" hidden></div>
-                <div class="compliance-strip" id="complianceStrip" hidden></div>
-            </div>
-        </section>
-
-        <aside class="side-panel">
-            <div class="card">
-                <div class="card-header">
-                    <span id="t-card-title">Product details</span>
-                    <span class="status-badge badge-neutral" id="statusBadge"></span>
-                </div>
-                <div class="placeholder" id="cardPlaceholder">
-                    <div class="placeholder-icon">🏷️</div>
-                    <span id="t-card-placeholder">Tap a product on the shelf to see its details.</span>
-                </div>
-                <div class="card-body" id="cardBody" hidden>
-                    <div class="product-hero">
-                        <img id="productThumb" class="product-thumb" alt="" hidden>
-                        <div class="product-details">
-                            <span class="product-sku" id="productSku" hidden>—</span>
-                            <div class="product-name" id="productName"></div>
-                            <div class="product-price" id="productPrice"></div>
-                        </div>
-                    </div>
-                    <div class="candidates" id="candidates" hidden></div>
-                    <div class="stock-grid">
-                        <div class="stock-metric">
-                            <div class="metric-title" id="t-metric-on">On shelf</div>
-                            <div class="metric-val" id="metricOn"></div>
-                        </div>
-                        <div class="stock-metric">
-                            <div class="metric-title" id="t-metric-out">Sold / dispatched</div>
-                            <div class="metric-val" id="metricOut"></div>
-                        </div>
-                        <div class="stock-metric">
-                            <div class="metric-title" id="t-metric-receipt">Received</div>
-                            <div class="metric-val small" id="metricReceipt"></div>
-                        </div>
-                        <div class="stock-metric">
-                            <div class="metric-title" id="t-metric-expiry">Expires</div>
-                            <div class="metric-val small warn" id="metricExpiry"></div>
-                        </div>
-                    </div>
-                    <div class="ocr-section" id="ocrSection" hidden>
-                        <div class="ocr-header">
-                            <span id="t-ocr-title">Price tag</span>
-                            <span class="recon-chip" id="reconChip"></span>
-                        </div>
-                        <div class="ocr-rows" id="ocrRows"></div>
-                    </div>
-                    <button class="action-btn" id="dispenseBtn" type="button">🛒 <span id="t-dispense">Dispense 1 unit</span></button>
-                </div>
-            </div>
-
-            <div class="card">
-                <div class="card-header">
-                    <span id="t-match-title">Feature matching</span>
-                    <span class="score-label" id="scoreLabel"></span>
-                </div>
-                <div class="match-box" id="matchBox">
-                    <span class="match-placeholder" id="t-match-placeholder">Tap a product, then “Show matching”.</span>
-                </div>
-                <button class="tool-btn wide" id="showMatchBtn" type="button" disabled>🔍 <span id="t-show-match">Show matching</span></button>
-            </div>
-
-            <div class="card">
-                <div class="card-header">
-                    <span id="t-inview-title">On this photo</span>
-                    <span class="score-label" id="inViewCount">0</span>
-                </div>
-                <div class="inview-list" id="inViewList">
-                    <div class="placeholder small" id="t-inview-placeholder">Recognized products appear here.</div>
-                </div>
-            </div>
-        </aside>
-    </main>
-
-    <div class="modal-backdrop" id="cameraModal">
-        <div class="modal-window">
-            <div class="modal-header">
-                <div class="modal-title">📷 <span id="t-camera-title">Take shelf photo</span></div>
-                <button class="close-btn" id="cameraClose" type="button">&times;</button>
-            </div>
-            <div class="video-box"><video id="cameraVideo" autoplay playsinline></video></div>
-            <button class="action-btn" id="snapshotBtn" type="button">📸 <span id="t-snapshot">Capture</span></button>
-        </div>
-    </div>
-
-    <script type="module" src="app.js"></script>
-</body>
-</html>
diff --git a/recognization/code/web/styles.css b/recognization/code/web/styles.css
deleted file mode 100644
index acbcabc..0000000
--- a/recognization/code/web/styles.css
+++ /dev/null
@@ -1,253 +0,0 @@
-:root {
-    --bg-body: #f4fbf6;
-    --bg-card: #ffffff;
-    --primary-green: #22c55e;
-    --primary-dark: #15803d;
-    --primary-light: #dcfce7;
-    --primary-subtle: #f0fdf4;
-    --border-color: #d1fae5;
-    --border-strong: #86efac;
-    --text-main: #0f172a;
-    --text-muted: #64748b;
-    --amber: #f59e0b;
-    --amber-soft: #fef3c7;
-    --danger: #ef4444;
-    --danger-soft: #fee2e2;
-    --slate-soft: #e2e8f0;
-    --night: #0f172a;
-    --shadow-soft: 0 4px 20px -2px rgba(34, 197, 94, 0.08), 0 2px 6px -1px rgba(0, 0, 0, 0.04);
-    --radius-lg: 18px;
-    --radius-md: 12px;
-    --radius-sm: 8px;
-}
-
-* { box-sizing: border-box; margin: 0; padding: 0; }
-
-[hidden] { display: none !important; }
-
-body {
-    background-color: var(--bg-body);
-    color: var(--text-main);
-    min-height: 100vh;
-    display: flex;
-    flex-direction: column;
-    font-family: 'Plus Jakarta Sans', 'Noto Sans JP', sans-serif;
-}
-
-header {
-    background: var(--bg-card);
-    border-bottom: 1px solid var(--border-color);
-    padding: 14px 32px;
-    display: flex;
-    justify-content: space-between;
-    align-items: center;
-    position: sticky;
-    top: 0;
-    z-index: 100;
-}
-
-.brand { display: flex; align-items: center; gap: 14px; }
-.brand-logo-svg { width: 44px; height: 44px; filter: drop-shadow(0 2px 6px rgba(34, 197, 94, 0.25)); }
-.brand-title { font-size: 20px; font-weight: 800; color: #14532d; letter-spacing: -0.5px; display: flex; align-items: center; gap: 6px; }
-.brand-jp { font-size: 13px; font-weight: 600; color: #16a34a; background: var(--primary-light); padding: 2px 8px; border-radius: 6px; }
-.brand-sub { font-size: 12px; color: var(--text-muted); font-weight: 500; }
-
-.header-actions { display: flex; align-items: center; gap: 12px; }
-
-.lang-switch-btn {
-    background: var(--primary-subtle);
-    border: 1px solid var(--border-color);
-    color: var(--primary-dark);
-    padding: 6px 14px;
-    border-radius: 999px;
-    font-size: 12px;
-    font-weight: 700;
-    cursor: pointer;
-    display: flex;
-    align-items: center;
-    gap: 6px;
-    font-family: inherit;
-}
-.lang-switch-btn:hover { background: var(--primary-light); border-color: var(--primary-green); }
-
-.conn-pill {
-    display: flex; align-items: center; gap: 6px;
-    font-size: 11px; font-weight: 700; color: #166534;
-    background: var(--primary-subtle); border: 1px solid var(--border-color);
-    padding: 5px 12px; border-radius: 999px;
-}
-.conn-pill .dot { width: 7px; height: 7px; border-radius: 50%; background: var(--text-muted); }
-.conn-pill[data-state="live"] .dot { background: var(--primary-green); box-shadow: 0 0 6px var(--primary-green); }
-.conn-pill[data-state="photo"] .dot { background: #38bdf8; box-shadow: 0 0 6px #38bdf8; }
-.conn-pill[data-state="error"] .dot { background: var(--danger); box-shadow: 0 0 6px var(--danger); }
-.conn-pill[data-state="connecting"] .dot { background: var(--amber); box-shadow: 0 0 6px var(--amber); }
-
-main {
-    flex: 1;
-    padding: 20px 32px;
-    display: grid;
-    grid-template-columns: 1fr 420px;
-    gap: 24px;
-    align-items: start;
-}
-
-.stage-panel {
-    background: var(--bg-card);
-    border: 1px solid var(--border-color);
-    border-radius: var(--radius-lg);
-    padding: 18px;
-    display: flex;
-    flex-direction: column;
-    gap: 14px;
-    box-shadow: var(--shadow-soft);
-}
-
-.toolbar { display: flex; justify-content: space-between; align-items: center; flex-wrap: wrap; gap: 10px; padding-bottom: 12px; border-bottom: 1px solid var(--border-color); }
-.toolbar-group { display: flex; align-items: center; gap: 8px; flex-wrap: wrap; }
-
-.segmented { display: flex; background: var(--primary-subtle); border: 1px solid var(--border-color); border-radius: 10px; padding: 3px; gap: 2px; }
-.seg-btn { background: transparent; border: none; color: var(--text-muted); padding: 6px 12px; border-radius: 8px; font-size: 12px; font-weight: 700; cursor: pointer; font-family: inherit; }
-.seg-btn.active { background: var(--primary-green); color: white; box-shadow: 0 2px 8px rgba(34, 197, 94, 0.3); }
-
-.tool-btn {
-    background: var(--bg-card); border: 1px solid var(--border-color); color: var(--primary-dark);
-    padding: 7px 14px; border-radius: 10px; font-size: 12px; font-weight: 700; cursor: pointer;
-    display: inline-flex; align-items: center; gap: 6px; font-family: inherit;
-}
-.tool-btn:hover:not(:disabled) { background: var(--primary-subtle); border-color: var(--primary-green); }
-.tool-btn:disabled { opacity: 0.45; cursor: default; }
-.tool-btn.primary { background: var(--primary-green); border-color: var(--primary-green); color: white; }
-.tool-btn.danger { background: var(--danger-soft); border-color: #fca5a5; color: #b91c1c; }
-.tool-btn.wide { width: 100%; justify-content: center; }
-
-.select { background: var(--bg-card); color: var(--text-main); border: 1px solid var(--border-color); padding: 7px 12px; border-radius: 10px; font-size: 12px; font-weight: 600; font-family: inherit; max-width: 220px; }
-
-.hud { font-size: 11px; font-weight: 700; color: #166534; background: var(--primary-subtle); border: 1px solid var(--border-color); padding: 6px 12px; border-radius: 999px; cursor: pointer; font-variant-numeric: tabular-nums; }
-.hud[data-detail="1"] { border-radius: 10px; }
-
-.stage {
-    position: relative;
-    background: var(--night);
-    border-radius: 14px;
-    overflow: hidden;
-    height: 70vh;
-    min-height: 420px;
-    display: flex;
-    align-items: center;
-    justify-content: center;
-}
-.stage video, .stage img { max-width: 100%; max-height: 100%; display: block; }
-/* The canvas is the same contained box as the media it annotates: its backing
-   store is the media's own pixel size, so server coordinates map 1:1 and the
-   element rect (used for click mapping) is exactly the visible picture. */
-.stage canvas {
-    position: absolute; left: 50%; top: 50%; transform: translate(-50%, -50%);
-    max-width: 100%; max-height: 100%;
-}
-
-.stage-empty { position: absolute; inset: 0; display: flex; flex-direction: column; align-items: center; justify-content: center; gap: 10px; color: #cbd5e1; font-size: 13px; text-align: center; padding: 24px; }
-.stage-empty-icon { font-size: 34px; }
-
-.hint-banner {
-    position: absolute; bottom: 16px; left: 50%; transform: translateX(-50%);
-    background: rgba(255, 255, 255, 0.95); backdrop-filter: blur(8px);
-    border: 1px solid var(--primary-green); color: #14532d;
-    padding: 8px 20px; border-radius: 999px; font-size: 12px; font-weight: 700;
-    box-shadow: 0 4px 16px rgba(34, 197, 94, 0.2); pointer-events: none;
-}
-.hint-banner[data-kind="warning"] { border-color: var(--amber); color: #92400e; }
-.hint-banner[data-kind="danger"] { border-color: var(--danger); color: #991b1b; }
-
-.side-panel { display: flex; flex-direction: column; gap: 16px; }
-
-.card { background: var(--bg-card); border: 1px solid var(--border-color); border-radius: var(--radius-lg); padding: 18px; display: flex; flex-direction: column; gap: 14px; box-shadow: var(--shadow-soft); }
-.card-header { font-size: 12px; font-weight: 800; color: #166534; text-transform: uppercase; letter-spacing: 0.6px; display: flex; justify-content: space-between; align-items: center; padding-bottom: 8px; border-bottom: 1px solid var(--border-color); }
-.score-label { font-size: 12px; color: #16a34a; font-weight: 700; text-transform: none; letter-spacing: 0; }
-
-.placeholder { text-align: center; padding: 32px 10px; color: var(--text-muted); font-size: 13px; display: flex; flex-direction: column; gap: 8px; align-items: center; }
-.placeholder.small { padding: 16px 8px; font-size: 12px; }
-.placeholder-icon { font-size: 32px; }
-
-.card-body { display: flex; flex-direction: column; gap: 14px; }
-
-.product-hero { display: flex; gap: 14px; align-items: center; }
-.product-thumb { width: 92px; height: 116px; border-radius: var(--radius-md); object-fit: cover; border: 2px solid var(--primary-light); background: #f8fafc; }
-.product-details { flex: 1; display: flex; flex-direction: column; gap: 6px; min-width: 0; }
-.product-name { font-size: 16px; font-weight: 800; line-height: 1.3; }
-.product-sku { font-size: 11px; color: #16a34a; font-family: ui-monospace, monospace; background: var(--primary-light); padding: 2px 6px; border-radius: 4px; width: fit-content; font-weight: 700; }
-.product-price { font-size: 24px; font-weight: 800; color: var(--primary-dark); }
-
-.candidates { display: flex; flex-direction: column; gap: 6px; background: var(--amber-soft); border: 1px solid #fcd34d; border-radius: var(--radius-md); padding: 10px 12px; }
-.candidates-title { font-size: 11px; font-weight: 800; color: #92400e; text-transform: uppercase; letter-spacing: 0.5px; }
-.candidate-row { display: flex; justify-content: space-between; font-size: 12px; color: #78350f; font-weight: 600; }
-
-.stock-grid { display: grid; grid-template-columns: 1fr 1fr; gap: 10px; }
-.stock-metric { background: var(--primary-subtle); border: 1px solid var(--border-color); border-radius: var(--radius-md); padding: 10px 14px; }
-.metric-title { font-size: 11px; color: var(--text-muted); margin-bottom: 4px; font-weight: 600; }
-.metric-val { font-size: 16px; font-weight: 800; color: var(--primary-dark); }
-.metric-val.small { font-size: 13px; }
-.metric-val.warn { color: #b45309; }
-
-.action-btn {
-    width: 100%; padding: 12px; border: none; border-radius: var(--radius-md);
-    background: linear-gradient(135deg, #22c55e, #16a34a); color: white;
-    font-weight: 800; font-size: 14px; cursor: pointer; font-family: inherit;
-    display: inline-flex; align-items: center; justify-content: center; gap: 8px;
-    box-shadow: 0 4px 14px rgba(34, 197, 94, 0.3);
-}
-.action-btn:hover { transform: translateY(-1px); }
-
-.match-box { width: 100%; min-height: 150px; background: var(--night); border-radius: var(--radius-md); border: 1px solid var(--border-color); display: flex; align-items: center; justify-content: center; overflow: hidden; }
-.match-box img { width: 100%; height: 100%; object-fit: contain; }
-.match-placeholder { color: #94a3b8; font-size: 12px; padding: 20px; text-align: center; }
-
-.inview-list { display: flex; flex-direction: column; gap: 8px; max-height: 320px; overflow-y: auto; }
-.inview-row { display: flex; align-items: center; gap: 10px; padding: 8px; border-radius: var(--radius-md); cursor: pointer; }
-.inview-row:hover { background: var(--primary-subtle); }
-.inview-thumb { width: 34px; height: 44px; border-radius: 6px; object-fit: cover; border: 1px solid var(--border-color); background: #f8fafc; }
-.inview-text { flex: 1; min-width: 0; }
-.inview-name { font-size: 12px; font-weight: 700; white-space: nowrap; overflow: hidden; text-overflow: ellipsis; }
-.inview-meta { font-size: 11px; color: var(--text-muted); }
-.inview-facings { font-size: 12px; font-weight: 800; color: var(--primary-dark); }
-
-.status-badge { font-size: 11px; padding: 3px 10px; border-radius: 999px; font-weight: 700; text-transform: none; letter-spacing: 0; }
-.badge-neutral { background: var(--slate-soft); color: var(--text-muted); }
-.badge-success { background: var(--primary-light); color: var(--primary-dark); }
-.badge-warning { background: var(--amber-soft); color: #b45309; }
-.badge-danger { background: var(--danger-soft); color: #b91c1c; }
-
-.modal-backdrop { display: none; position: fixed; inset: 0; background: rgba(15, 23, 42, 0.6); backdrop-filter: blur(4px); z-index: 1000; align-items: center; justify-content: center; }
-.modal-backdrop.open { display: flex; }
-.modal-window { background: #fff; border-radius: 20px; width: 90%; max-width: 600px; padding: 22px; display: flex; flex-direction: column; gap: 14px; }
-.modal-header { display: flex; justify-content: space-between; align-items: center; }
-.modal-title { font-size: 17px; font-weight: 800; color: #14532d; }
-.close-btn { background: none; border: none; font-size: 24px; color: var(--text-muted); cursor: pointer; }
-.video-box { width: 100%; height: 340px; background: #000; border-radius: var(--radius-md); overflow: hidden; }
-.video-box video { width: 100%; height: 100%; object-fit: cover; }
-
-@media (max-width: 1024px) {
-    main { grid-template-columns: 1fr; padding: 16px; }
-    .stage { height: 56vh; min-height: 320px; }
-}
-
-.ocr-section { border-top: 1px solid var(--border-color); padding-top: 12px; display: flex; flex-direction: column; gap: 8px; }
-.ocr-header { display: flex; justify-content: space-between; align-items: center; font-size: 11px; font-weight: 800; color: #0369a1; text-transform: uppercase; letter-spacing: 0.5px; }
-.recon-chip { font-size: 10px; font-weight: 800; padding: 2px 8px; border-radius: 999px; text-transform: none; letter-spacing: 0; }
-.recon-chip[data-state="confirmed"] { background: var(--primary-light); color: var(--primary-dark); }
-.recon-chip[data-state="mismatch"] { background: var(--danger-soft); color: #b91c1c; }
-.recon-chip[data-state="unverified"], .recon-chip[data-state="no_tag"] { background: var(--slate-soft); color: var(--text-muted); }
-.recon-chip[data-state="tag_only"] { background: #e0f2fe; color: #0369a1; }
-.ocr-rows { display: flex; flex-direction: column; gap: 4px; }
-.ocr-row { display: flex; justify-content: space-between; gap: 10px; font-size: 12px; }
-.ocr-row .ocr-key { color: var(--text-muted); font-weight: 600; }
-.ocr-row .ocr-val { color: var(--text-main); font-weight: 700; text-align: right; }
-.ocr-row .ocr-val.bad { color: #b91c1c; }
-.compliance-strip {
-    position: absolute; top: 14px; left: 14px; right: 14px;
-    background: rgba(255, 255, 255, 0.94); backdrop-filter: blur(8px);
-    border: 1px solid #fca5a5; border-radius: 12px; padding: 8px 14px;
-    font-size: 12px; font-weight: 700; color: #b91c1c; display: flex; gap: 12px; flex-wrap: wrap;
-    box-shadow: 0 4px 16px rgba(0, 0, 0, 0.08); pointer-events: none;
-}
-.compliance-strip[data-kind="hint"] { border-color: var(--amber); color: #92400e; }
-.compliance-strip .compliance-item { display: inline-flex; align-items: center; gap: 6px; }
diff --git a/recognization/data/README.md b/recognization/data/README.md
deleted file mode 100644
index 037085f..0000000
--- a/recognization/data/README.md
+++ /dev/null
@@ -1,34 +0,0 @@
-# Recognition data
-
-Everything the identity work needs to be reproducible: sourced packshots,
-evaluation runs, and the audits that produced them. Nothing here is ephemeral;
-a run that matters is written here, not to a temp directory.
-
-## `packshots/source/`
-
-Front packshots as downloaded, before enrollment. `ATTRIBUTION.json` records the
-source URL, the product as printed, and the manufacturer for each. `ocr-reads.json`
-holds the OCR text each image produced, so a name/OCR refusal can be re-checked
-without re-running Vision.
-
-`rebuild_catalog.py` reads this directory and writes a catalog:
-
-    .venv/bin/python code/rebuild_catalog.py --source data/packshots/source \
-        --output stock-lopia-clean --csv stock-lopia/stock.csv --min-views 1 --replace
-
-## `evaluations/`
-
-One directory per session. Each holds the raw artifacts a claim was computed
-from, so a number in DECISION.md can be traced back to the run that produced it.
-
-| Directory | Contents |
-|---|---|
-| `stream/` | WebSocket track dumps from `stream_client.py`, scored by `score_stream.py`. |
-| `still/` | `/v1/recognize` responses for the still-image path. |
-| `catalog-audit/` | `rebuild_catalog.py --json` reports. |
-| `scratch/` | Intermediate images (rotations, crops) kept because they document a finding. |
-
-Scoring a stream dump:
-
-    .venv/bin/python code/score_stream.py data/evaluations/<session>/stream/<file>.json \
-        --labels eval/labels/image6.json --stock-dir stock-lopia-clean
diff --git a/recognization/data/evaluations/2026-09-29-session/catalog-audit/enroll-2026-09-29.json b/recognization/data/evaluations/2026-09-29-session/catalog-audit/enroll-2026-09-29.json
deleted file mode 100644
index aec216f..0000000
--- a/recognization/data/evaluations/2026-09-29-session/catalog-audit/enroll-2026-09-29.json
+++ /dev/null
@@ -1,76 +0,0 @@
-{
- "source": "data/packshots/source",
- "output": "stock-lopia-clean",
- "min_width": 120,
- "min_height": 160,
- "min_views": 1,
- "duplicate_cosine": 0.98,
- "min_name_ocr": 0.7,
- "ocr": "vision",
- "text_only_skus": [
-  "sku_l04",
-  "sku_l07",
-  "sku_l08",
-  "sku_l12"
- ],
- "text_discriminated_siblings": [
-  {
-   "sku": "sku_l09",
-   "view": "sku_l09.jpg",
-   "sibling_sku": "sku_l10",
-   "sibling_view": "sku_l10.jpg",
-   "cosine": 0.9817,
-   "name_similarity": 0.333
-  }
- ],
- "below_pyramid_floor": [],
- "completeness": {
-  "live_ready": null,
-  "reason": "no --shelf-tags given; the completeness rule needs the shelf's SKU list",
-  "enrolled": 8,
-  "text_only": [
-   "sku_l04",
-   "sku_l07",
-   "sku_l08",
-   "sku_l12"
-  ]
- },
- "skus": {
-  "sku_l01": [
-   "sku_l01.jpg"
-  ],
-  "sku_l02": [
-   "sku_l02.jpg"
-  ],
-  "sku_l03": [
-   "sku_l03.jpg"
-  ],
-  "sku_l05": [
-   "sku_l05.jpg"
-  ],
-  "sku_l06": [
-   "sku_l06.jpg"
-  ],
-  "sku_l09": [
-   "sku_l09.jpg"
-  ],
-  "sku_l10": [
-   "sku_l10.jpg"
-  ],
-  "sku_l11": [
-   "sku_l11.jpg"
-  ]
- },
- "refused": [
-  {
-   "sku": "sku_l04",
-   "view": "sku_l04.jpg",
-   "reason": "name/OCR 0.00 below 0.70: 明太手屋さんが つくつ た コ"
-  },
-  {
-   "sku": "sku_l07",
-   "view": "sku_l07.jpg",
-   "reason": "name/OCR 0.57 below 0.70: ラッキー マヨネーズ 調味料（アミノ酸）不使用"
-  }
- ]
-}
\ No newline at end of file
diff --git a/recognization/data/evaluations/2026-09-29-session/catalog-audit/eval-after-l10.json b/recognization/data/evaluations/2026-09-29-session/catalog-audit/eval-after-l10.json
deleted file mode 100644
index 01bdd94..0000000
--- a/recognization/data/evaluations/2026-09-29-session/catalog-audit/eval-after-l10.json
+++ /dev/null
@@ -1,221 +0,0 @@
-{
- "image": "add/image6.jpg",
- "stock_dir": "stock-lopia-clean",
- "labels": "eval/labels/image6.json",
- "provenance": {
-  "catalog_hash": "3f1653d9d962073af96121250006b9b6d86113556797bbee65de68b60e87a64d",
-  "labels_hash": "5aabf029921ced767d4e14a18e8bccb55812dd974852c56e0931a98fa10366cf",
-  "image_hash": "a08b89ba37d27c9d5a81bcd56bcd042e6baf54d2f182bbaaf47e97f14c9cd525",
-  "code_revision": "68061635d9fef3097609945bfd9ac1935791b398"
- },
- "facings": 111,
- "leakage": {
-  "excluded": 0,
-  "labels": []
- },
- "hub_share_limit": 0.3,
- "enrollment": {
-  "catalog_skus": [
-   "sku_l01",
-   "sku_l02",
-   "sku_l03",
-   "sku_l04",
-   "sku_l05",
-   "sku_l06",
-   "sku_l07",
-   "sku_l08",
-   "sku_l09",
-   "sku_l10",
-   "sku_l11",
-   "sku_l12"
-  ],
-  "enrolled_skus": [
-   "sku_l01",
-   "sku_l02",
-   "sku_l03",
-   "sku_l05",
-   "sku_l06",
-   "sku_l09",
-   "sku_l10",
-   "sku_l11"
-  ],
-  "unenrolled_skus": {
-   "sku_l07": 6,
-   "sku_l04": 1,
-   "sku_l08": 1
-  },
-  "facings_without_enrollment": 8,
-  "scored_facings": 109
- },
- "results": {
-  "full": [
-   {
-    "lane": "local",
-    "accept": 2.0,
-    "margin": 0.15,
-    "in_catalog": 61,
-    "out_catalog": 48,
-    "enrolled": 53,
-    "correct": 48,
-    "wrong": 9,
-    "rejected": 52,
-    "catalog_ceiling": 0.8689,
-    "identity_ceiling": 0.9245,
-    "decision_ceiling": 0.8113,
-    "end_to_end_coverage": 0.7869,
-    "false_accepts": 9,
-    "unknown_false_accepts": 8,
-    "unenrolled_false_accepts": 0,
-    "false_accept_rate": 0.1667,
-    "near_unknown_false_accept_rate": 0.0,
-    "precision": 0.8421,
-    "max_sku_share": 0.2281,
-    "hub_sku": "sku_l09",
-    "hub_alarm": false,
-    "histogram": {
-     "sku_l09": 13,
-     "sku_l11": 10,
-     "sku_l10": 9,
-     "sku_l01": 5,
-     "sku_l03": 5,
-     "sku_l07": 5,
-     "sku_l06": 4,
-     "sku_l02": 3,
-     "sku_l05": 3
-    },
-    "per_sku": {
-     "sku_l01": {
-      "correct": 5,
-      "total": 5
-     },
-     "sku_l02": {
-      "correct": 2,
-      "total": 4
-     },
-     "sku_l03": {
-      "correct": 4,
-      "total": 4
-     },
-     "sku_l04": {
-      "correct": 0,
-      "total": 1
-     },
-     "sku_l05": {
-      "correct": 3,
-      "total": 3
-     },
-     "sku_l06": {
-      "correct": 4,
-      "total": 4
-     },
-     "sku_l07": {
-      "correct": 5,
-      "total": 6
-     },
-     "sku_l08": {
-      "correct": 0,
-      "total": 1
-     },
-     "sku_l09": {
-      "correct": 11,
-      "total": 13
-     },
-     "sku_l10": {
-      "correct": 9,
-      "total": 11
-     },
-     "sku_l11": {
-      "correct": 5,
-      "total": 9
-     }
-    },
-    "ms_per_crop": 26.7
-   }
-  ],
-  "live": [
-   {
-    "lane": "local",
-    "accept": 2.0,
-    "margin": 0.15,
-    "in_catalog": 61,
-    "out_catalog": 48,
-    "enrolled": 53,
-    "correct": 41,
-    "wrong": 4,
-    "rejected": 64,
-    "catalog_ceiling": 0.8689,
-    "identity_ceiling": 0.8491,
-    "decision_ceiling": 0.6604,
-    "end_to_end_coverage": 0.6721,
-    "false_accepts": 4,
-    "unknown_false_accepts": 3,
-    "unenrolled_false_accepts": 0,
-    "false_accept_rate": 0.0625,
-    "near_unknown_false_accept_rate": 0.0,
-    "precision": 0.9111,
-    "max_sku_share": 0.2,
-    "hub_sku": "sku_l09",
-    "hub_alarm": false,
-    "histogram": {
-     "sku_l09": 9,
-     "sku_l10": 9,
-     "sku_l11": 7,
-     "sku_l07": 6,
-     "sku_l01": 4,
-     "sku_l03": 3,
-     "sku_l05": 3,
-     "sku_l06": 2,
-     "sku_l02": 1,
-     "sku_l04": 1
-    },
-    "per_sku": {
-     "sku_l01": {
-      "correct": 4,
-      "total": 5
-     },
-     "sku_l02": {
-      "correct": 1,
-      "total": 4
-     },
-     "sku_l03": {
-      "correct": 3,
-      "total": 4
-     },
-     "sku_l04": {
-      "correct": 1,
-      "total": 1
-     },
-     "sku_l05": {
-      "correct": 3,
-      "total": 3
-     },
-     "sku_l06": {
-      "correct": 2,
-      "total": 4
-     },
-     "sku_l07": {
-      "correct": 5,
-      "total": 6
-     },
-     "sku_l08": {
-      "correct": 0,
-      "total": 1
-     },
-     "sku_l09": {
-      "correct": 9,
-      "total": 13
-     },
-     "sku_l10": {
-      "correct": 9,
-      "total": 11
-     },
-     "sku_l11": {
-      "correct": 4,
-      "total": 9
-     }
-    },
-    "ms_per_crop": 20.9
-   }
-  ]
- }
-}
\ No newline at end of file
diff --git a/recognization/data/evaluations/2026-09-29-session/catalog-audit/source-audit.json b/recognization/data/evaluations/2026-09-29-session/catalog-audit/source-audit.json
deleted file mode 100644
index 7d34332..0000000
--- a/recognization/data/evaluations/2026-09-29-session/catalog-audit/source-audit.json
+++ /dev/null
@@ -1,70 +0,0 @@
-{
- "source": "data/packshots/source",
- "output": "/tmp/lopia-audit",
- "min_width": 120,
- "min_height": 160,
- "min_views": 1,
- "duplicate_cosine": 0.98,
- "min_name_ocr": 0.7,
- "ocr": "vision",
- "text_only_skus": [
-  "sku_l04",
-  "sku_l07",
-  "sku_l08",
-  "sku_l09",
-  "sku_l10",
-  "sku_l12"
- ],
- "text_discriminated_siblings": [],
- "below_pyramid_floor": [],
- "completeness": {
-  "live_ready": null,
-  "reason": "no --shelf-tags given; the completeness rule needs the shelf's SKU list",
-  "enrolled": 6,
-  "text_only": [
-   "sku_l04",
-   "sku_l07",
-   "sku_l08",
-   "sku_l09",
-   "sku_l10",
-   "sku_l12"
-  ]
- },
- "skus": {
-  "sku_l01": [
-   "sku_l01.jpg"
-  ],
-  "sku_l02": [
-   "sku_l02.jpg"
-  ],
-  "sku_l03": [
-   "sku_l03.jpg"
-  ],
-  "sku_l05": [
-   "sku_l05.jpg"
-  ],
-  "sku_l06": [
-   "sku_l06.jpg"
-  ],
-  "sku_l11": [
-   "sku_l11.jpg"
-  ]
- },
- "refused": [
-  {
-   "sku": "sku_l04",
-   "view": "sku_l04.jpg",
-   "reason": "name/OCR 0.00 below 0.70: 明太手屋さんが つくつ た コ"
-  },
-  {
-   "sku": "sku_l07",
-   "view": "sku_l07.jpg",
-   "reason": "name/OCR 0.57 below 0.70: ラッキー マヨネーズ 調味料（アミノ酸）不使用"
-  },
-  {
-   "sku": "sku_l09",
-   "view": "sku_l09.jpg",
-   "reason": "name/OCR 0.05 below 0.70: キューピーマヨネーズ KEWPIE MAYONNAISE ® 450g"
-  }
- ]
-}
\ No newline at end of file
diff --git a/recognization/data/evaluations/2026-09-29-session/pace-medians/result_1.json b/recognization/data/evaluations/2026-09-29-session/pace-medians/result_1.json
deleted file mode 100644
index d06acf6..0000000
--- a/recognization/data/evaluations/2026-09-29-session/pace-medians/result_1.json
+++ /dev/null
@@ -1,1461 +0,0 @@
-{
-  "windows": [
-    [
-      0,
-      640,
-      1280,
-      1360
-    ],
-    [
-      12,
-      640,
-      1292,
-      1360
-    ],
-    [
-      24,
-      640,
-      1304,
-      1360
-    ],
-    [
-      36,
-      640,
-      1316,
-      1360
-    ],
-    [
-      48,
-      640,
-      1328,
-      1360
-    ],
-    [
-      60,
-      640,
-      1340,
-      1360
-    ],
-    [
-      72,
-      640,
-      1352,
-      1360
-    ],
-    [
-      84,
-      640,
-      1364,
-      1360
-    ],
-    [
-      96,
-      640,
-      1376,
-      1360
-    ],
-    [
-      108,
-      640,
-      1388,
-      1360
-    ],
-    [
-      120,
-      640,
-      1400,
-      1360
-    ],
-    [
-      132,
-      640,
-      1412,
-      1360
-    ],
-    [
-      144,
-      640,
-      1424,
-      1360
-    ],
-    [
-      156,
-      640,
-      1436,
-      1360
-    ],
-    [
-      168,
-      640,
-      1448,
-      1360
-    ],
-    [
-      180,
-      640,
-      1460,
-      1360
-    ],
-    [
-      192,
-      640,
-      1472,
-      1360
-    ],
-    [
-      204,
-      640,
-      1484,
-      1360
-    ],
-    [
-      216,
-      640,
-      1496,
-      1360
-    ],
-    [
-      220,
-      640,
-      1500,
-      1360
-    ],
-    [
-      216,
-      640,
-      1496,
-      1360
-    ],
-    [
-      204,
-      640,
-      1484,
-      1360
-    ],
-    [
-      192,
-      640,
-      1472,
-      1360
-    ],
-    [
-      180,
-      640,
-      1460,
-      1360
-    ],
-    [
-      168,
-      640,
-      1448,
-      1360
-    ],
-    [
-      156,
-      640,
-      1436,
-      1360
-    ],
-    [
-      144,
-      640,
-      1424,
-      1360
-    ],
-    [
-      132,
-      640,
-      1412,
-      1360
-    ],
-    [
-      120,
-      640,
-      1400,
-      1360
-    ],
-    [
-      108,
-      640,
-      1388,
-      1360
-    ],
-    [
-      96,
-      640,
-      1376,
-      1360
-    ],
-    [
-      84,
-      640,
-      1364,
-      1360
-    ],
-    [
-      72,
-      640,
-      1352,
-      1360
-    ],
-    [
-      60,
-      640,
-      1340,
-      1360
-    ],
-    [
-      48,
-      640,
-      1328,
-      1360
-    ],
-    [
-      36,
-      640,
-      1316,
-      1360
-    ],
-    [
-      24,
-      640,
-      1304,
-      1360
-    ],
-    [
-      12,
-      640,
-      1292,
-      1360
-    ],
-    [
-      0,
-      640,
-      1280,
-      1360
-    ],
-    [
-      12,
-      640,
-      1292,
-      1360
-    ],
-    [
-      24,
-      640,
-      1304,
-      1360
-    ],
-    [
-      36,
-      640,
-      1316,
-      1360
-    ],
-    [
-      48,
-      640,
-      1328,
-      1360
-    ],
-    [
-      60,
-      640,
-      1340,
-      1360
-    ],
-    [
-      72,
-      640,
-      1352,
-      1360
-    ],
-    [
-      84,
-      640,
-      1364,
-      1360
-    ],
-    [
-      96,
-      640,
-      1376,
-      1360
-    ],
-    [
-      108,
-      640,
-      1388,
-      1360
-    ],
-    [
-      120,
-      640,
-      1400,
-      1360
-    ],
-    [
-      132,
-      640,
-      1412,
-      1360
-    ],
-    [
-      144,
-      640,
-      1424,
-      1360
-    ],
-    [
-      156,
-      640,
-      1436,
-      1360
-    ],
-    [
-      168,
-      640,
-      1448,
-      1360
-    ],
-    [
-      180,
-      640,
-      1460,
-      1360
-    ],
-    [
-      192,
-      640,
-      1472,
-      1360
-    ],
-    [
-      204,
-      640,
-      1484,
-      1360
-    ],
-    [
-      216,
-      640,
-      1496,
-      1360
-    ],
-    [
-      220,
-      640,
-      1500,
-      1360
-    ],
-    [
-      216,
-      640,
-      1496,
-      1360
-    ],
-    [
-      204,
-      640,
-      1484,
-      1360
-    ],
-    [
-      192,
-      640,
-      1472,
-      1360
-    ],
-    [
-      180,
-      640,
-      1460,
-      1360
-    ],
-    [
-      168,
-      640,
-      1448,
-      1360
-    ],
-    [
-      156,
-      640,
-      1436,
-      1360
-    ],
-    [
-      144,
-      640,
-      1424,
-      1360
-    ],
-    [
-      132,
-      640,
-      1412,
-      1360
-    ],
-    [
-      120,
-      640,
-      1400,
-      1360
-    ],
-    [
-      108,
-      640,
-      1388,
-      1360
-    ],
-    [
-      96,
-      640,
-      1376,
-      1360
-    ],
-    [
-      84,
-      640,
-      1364,
-      1360
-    ],
-    [
-      72,
-      640,
-      1352,
-      1360
-    ],
-    [
-      60,
-      640,
-      1340,
-      1360
-    ],
-    [
-      48,
-      640,
-      1328,
-      1360
-    ],
-    [
-      36,
-      640,
-      1316,
-      1360
-    ],
-    [
-      24,
-      640,
-      1304,
-      1360
-    ],
-    [
-      12,
-      640,
-      1292,
-      1360
-    ],
-    [
-      0,
-      640,
-      1280,
-      1360
-    ],
-    [
-      12,
-      640,
-      1292,
-      1360
-    ],
-    [
-      24,
-      640,
-      1304,
-      1360
-    ],
-    [
-      36,
-      640,
-      1316,
-      1360
-    ],
-    [
-      48,
-      640,
-      1328,
-      1360
-    ],
-    [
-      60,
-      640,
-      1340,
-      1360
-    ],
-    [
-      72,
-      640,
-      1352,
-      1360
-    ],
-    [
-      84,
-      640,
-      1364,
-      1360
-    ],
-    [
-      96,
-      640,
-      1376,
-      1360
-    ],
-    [
-      108,
-      640,
-      1388,
-      1360
-    ],
-    [
-      120,
-      640,
-      1400,
-      1360
-    ],
-    [
-      132,
-      640,
-      1412,
-      1360
-    ],
-    [
-      144,
-      640,
-      1424,
-      1360
-    ],
-    [
-      156,
-      640,
-      1436,
-      1360
-    ],
-    [
-      168,
-      640,
-      1448,
-      1360
-    ],
-    [
-      180,
-      640,
-      1460,
-      1360
-    ],
-    [
-      192,
-      640,
-      1472,
-      1360
-    ],
-    [
-      204,
-      640,
-      1484,
-      1360
-    ],
-    [
-      216,
-      640,
-      1496,
-      1360
-    ],
-    [
-      220,
-      640,
-      1500,
-      1360
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diff --git a/recognization/data/evaluations/2026-09-29-session/pace-medians/result_2.json b/recognization/data/evaluations/2026-09-29-session/pace-medians/result_2.json
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diff --git a/recognization/data/evaluations/2026-09-29-session/pace-medians/send_1.json b/recognization/data/evaluations/2026-09-29-session/pace-medians/send_1.json
deleted file mode 100644
index 29d49ac..0000000
--- a/recognization/data/evaluations/2026-09-29-session/pace-medians/send_1.json
+++ /dev/null
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diff --git a/recognization/data/evaluations/2026-09-29-session/pace-medians/send_2.json b/recognization/data/evaluations/2026-09-29-session/pace-medians/send_2.json
deleted file mode 100644
index 0ca17b2..0000000
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+++ /dev/null
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diff --git a/recognization/data/evaluations/2026-09-29-session/pace-medians/send_3.json b/recognization/data/evaluations/2026-09-29-session/pace-medians/send_3.json
deleted file mode 100644
index 019c7b7..0000000
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-        1274,
-        1245
-      ],
-      "label_frame_id": 103,
-      "result_frame_id": 118,
-      "inherited": false,
-      "restarts": 3
-    },
-    {
-      "track_id": 127,
-      "sku_id": "sku_l09",
-      "label_box": [
-        204,
-        758,
-        247,
-        971
-      ],
-      "label_frame_id": 26,
-      "result_frame_id": 34,
-      "inherited": false,
-      "restarts": 1
-    },
-    {
-      "track_id": 135,
-      "sku_id": "sku_l09",
-      "label_box": [
-        614,
-        760,
-        700,
-        972
-      ],
-      "label_frame_id": 101,
-      "result_frame_id": 104,
-      "inherited": true,
-      "restarts": 3
-    },
-    {
-      "track_id": 136,
-      "sku_id": "sku_l10",
-      "label_box": [
-        1126,
-        1054,
-        1197,
-        1245
-      ],
-      "label_frame_id": 50,
-      "result_frame_id": 57,
-      "inherited": false,
-      "restarts": 1
-    },
-    {
-      "track_id": 159,
-      "sku_id": "sku_l05",
-      "label_box": [
-        120,
-        672,
-        244,
-        734
-      ],
-      "label_frame_id": 49,
-      "result_frame_id": 53,
-      "inherited": true,
-      "restarts": 1
-    },
-    {
-      "track_id": 178,
-      "sku_id": "sku_l10",
-      "label_box": [
-        193,
-        1062,
-        300,
-        1258
-      ],
-      "label_frame_id": 41,
-      "result_frame_id": 41,
-      "inherited": true,
-      "restarts": 5
-    },
-    {
-      "track_id": 269,
-      "sku_id": "sku_l09",
-      "label_box": [
-        198,
-        758,
-        257,
-        971
-      ],
-      "label_frame_id": 52,
-      "result_frame_id": 58,
-      "inherited": false,
-      "restarts": 1
-    },
-    {
-      "track_id": 331,
-      "sku_id": "sku_l10",
-      "label_box": [
-        305,
-        1057,
-        438,
-        1256
-      ],
-      "label_frame_id": 112,
-      "result_frame_id": 121,
-      "inherited": true,
-      "restarts": 9
-    },
-    {
-      "track_id": 353,
-      "sku_id": "sku_l10",
-      "label_box": [
-        1116,
-        1056,
-        1191,
-        1244
-      ],
-      "label_frame_id": 112,
-      "result_frame_id": 121,
-      "inherited": false,
-      "restarts": 3
-    },
-    {
-      "track_id": 363,
-      "sku_id": "sku_l06",
-      "label_box": [
-        488,
-        675,
-        628,
-        742
-      ],
-      "label_frame_id": 68,
-      "result_frame_id": 77,
-      "inherited": false,
-      "restarts": 1
-    },
-    {
-      "track_id": 370,
-      "sku_id": "sku_l09",
-      "label_box": [
-        204,
-        758,
-        247,
-        971
-      ],
-      "label_frame_id": 64,
-      "result_frame_id": 73,
-      "inherited": false,
-      "restarts": 1
-    },
-    {
-      "track_id": 378,
-      "sku_id": "sku_l09",
-      "label_box": [
-        529,
-        758,
-        611,
-        971
-      ],
-      "label_frame_id": 89,
-      "result_frame_id": 100,
-      "inherited": false,
-      "restarts": 1
-    },
-    {
-      "track_id": 400,
-      "sku_id": "sku_l05",
-      "label_box": [
-        108,
-        670,
-        247,
-        730
-      ],
-      "label_frame_id": 86,
-      "result_frame_id": 91,
-      "inherited": true,
-      "restarts": 1
-    },
-    {
-      "track_id": 499,
-      "sku_id": "sku_l09",
-      "label_box": [
-        452,
-        759,
-        513,
-        968
-      ],
-      "label_frame_id": 96,
-      "result_frame_id": 97,
-      "inherited": true,
-      "restarts": 0
-    },
-    {
-      "track_id": 507,
-      "sku_id": "sku_l09",
-      "label_box": [
-        198,
-        753,
-        258,
-        972
-      ],
-      "label_frame_id": 84,
-      "result_frame_id": 92,
-      "inherited": false,
-      "restarts": 1
-    },
-    {
-      "track_id": 536,
-      "sku_id": "sku_l10",
-      "label_box": [
-        542,
-        1063,
-        629,
-        1248
-      ],
-      "label_frame_id": 121,
-      "result_frame_id": 121,
-      "inherited": true,
-      "restarts": 1
-    },
-    {
-      "track_id": 599,
-      "sku_id": "sku_l06",
-      "label_box": [
-        490,
-        679,
-        628,
-        744
-      ],
-      "label_frame_id": 101,
-      "result_frame_id": 114,
-      "inherited": false,
-      "restarts": 1
-    },
-    {
-      "track_id": 606,
-      "sku_id": "sku_l09",
-      "label_box": [
-        197,
-        755,
-        257,
-        972
-      ],
-      "label_frame_id": 116,
-      "result_frame_id": 116,
-      "inherited": true,
-      "restarts": 1
-    },
-    {
-      "track_id": 613,
-      "sku_id": "sku_l09",
-      "label_box": [
-        454,
-        763,
-        514,
-        969
-      ],
-      "label_frame_id": 115,
-      "result_frame_id": 115,
-      "inherited": true,
-      "restarts": 0
-    },
-    {
-      "track_id": 635,
-      "sku_id": "sku_l05",
-      "label_box": [
-        107,
-        653,
-        227,
-        737
-      ],
-      "label_frame_id": 111,
-      "result_frame_id": 121,
-      "inherited": false,
-      "restarts": 1
-    },
-    {
-      "track_id": 707,
-      "sku_id": "sku_l09",
-      "label_box": [
-        12,
-        752,
-        108,
-        972
-      ],
-      "label_frame_id": 116,
-      "result_frame_id": 121,
-      "inherited": true,
-      "restarts": 0
-    },
-    {
-      "track_id": 738,
-      "sku_id": "sku_l09",
-      "label_box": [
-        453,
-        762,
-        513,
-        970
-      ],
-      "label_frame_id": 118,
-      "result_frame_id": 121,
-      "inherited": true,
-      "restarts": 0
-    }
-  ]
-}
\ No newline at end of file
diff --git a/recognization/data/evaluations/2026-09-29-session/pace-medians/summary.json b/recognization/data/evaluations/2026-09-29-session/pace-medians/summary.json
deleted file mode 100644
index 2213e49..0000000
--- a/recognization/data/evaluations/2026-09-29-session/pace-medians/summary.json
+++ /dev/null
@@ -1,40 +0,0 @@
-{
- "send": {
-  "coverage": [
-   0.63,
-   0.704,
-   0.667
-  ],
-  "coverage_median": 0.667,
-  "precision": [
-   0.773,
-   0.884,
-   0.973
-  ],
-  "precision_median": 0.884,
-  "wrong": [
-   0,
-   0,
-   0
-  ]
- },
- "result": {
-  "coverage": [
-   0.63,
-   0.63,
-   0.704
-  ],
-  "coverage_median": 0.63,
-  "precision": [
-   0.857,
-   0.825,
-   0.9
-  ],
-  "precision_median": 0.857,
-  "wrong": [
-   0,
-   0,
-   0
-  ]
- }
-}
\ No newline at end of file
diff --git a/recognization/data/evaluations/2026-09-29-session/scratch/fa_detail.py b/recognization/data/evaluations/2026-09-29-session/scratch/fa_detail.py
deleted file mode 100644
index d7145fa..0000000
--- a/recognization/data/evaluations/2026-09-29-session/scratch/fa_detail.py
+++ /dev/null
@@ -1,27 +0,0 @@
-"""Which facings are falsely accepted, and as what."""
-import sys, cv2
-sys.path.insert(0, "code")
-from eval_identity import LocalLaneAdapter, load_labels, at_live_resolution, leaked, UNKNOWN, crop_of
-from identity_lanes import DecisionPolicy
-
-payload = load_labels("eval/labels/image6.json")
-image = cv2.imread("add/image6.jpg")
-facings = [f for f in payload["facings"] if f["label"] != "exclude"]
-facings = [f for f in facings if leaked(image, f["box"], "stock-lopia-clean") is None]
-enrolled = {"sku_l01","sku_l02","sku_l03","sku_l05","sku_l06","sku_l09","sku_l10","sku_l11"}
-
-for live in (False, True):
-    picture = at_live_resolution(image) if live else image
-    scaled = facings if not live else [
-        {**f, "box": [int(round(v * picture.shape[1] / image.shape[1])) for v in f["box"]]}
-        for f in facings]
-    lane = LocalLaneAdapter("stock-lopia-clean")
-    print(f"=== {'live' if live else 'full'} ===")
-    for f in scaled:
-        crop = crop_of(picture, f["box"])
-        if crop.size == 0: continue
-        ranked = lane.rank(crop)
-        d = lane.decide(crop, ranked, accept=2.0, margin=0.0, scale="local")
-        ans = d.get("best_sku") if d.get("identity_level") == "accepted" else None
-        if ans and ans != f["label"]:
-            print(f"  #{f['index']:>3} truth={f['label']:<9} answered={ans:<9} enrolled={f['label'] in enrolled}")
diff --git a/recognization/data/evaluations/2026-09-29-session/scratch/fa_text.py b/recognization/data/evaluations/2026-09-29-session/scratch/fa_text.py
deleted file mode 100644
index d7e4ad7..0000000
--- a/recognization/data/evaluations/2026-09-29-session/scratch/fa_text.py
+++ /dev/null
@@ -1,54 +0,0 @@
-"""What the falsely-accepted crops actually read, and what the text lane scores.
-
-The advisory says crops 82-97 are Kewpie Mayonnaise 450g - a product the
-catalog does not hold - and that no catalog row can therefore win the text
-ranking, so the veto at DecisionPolicy.decide never fires. This prints the
-reads and the scores that claim depends on.
-"""
-import csv
-import sys
-
-import cv2
-import numpy as np
-
-sys.path.insert(0, "code")
-from identity_lanes import TextLane, carries_identity  # noqa: E402
-from ocr import build_ocr_engine  # noqa: E402
-
-INDICES = [73, 82, 83, 84, 86, 94, 95, 96, 97]
-
-rows = []
-with open("stock-lopia-clean/stock.csv", encoding="utf-8") as handle:
-    for record in csv.reader(handle):
-        if len(record) >= 2 and record[0].startswith("sku_"):
-            rows.append({"sku_id": record[0], "product_name": record[1]})
-
-engine = build_ocr_engine("vision")
-lane = TextLane.from_catalog(rows, "stock-lopia-clean", ocr_engine=engine)
-
-labels = {f["index"]: f for f in __import__("json").load(open("eval/labels/image6.json"))["facings"]}
-image = cv2.imread("add/image6.jpg")
-
-tiles = []
-for index in INDICES:
-    facing = labels[index]
-    x1, y1, x2, y2 = facing["box"]
-    crop = image[y1:y2, x1:x2]
-    read = lane.read(crop)
-    scores = lane.scores_from_text(read)
-    top = sorted(scores.items(), key=lambda kv: -kv[1])[:3]
-    print(f"#{index:>3} truth={facing['label']:<9} carries={carries_identity(read)}")
-    print(f"      read: {read[:110]}")
-    print(f"      text top: {[(s, round(v, 3)) for s, v in top]}")
-
-    height, width = crop.shape[:2]
-    scale = 260.0 / max(1, height)
-    resized = cv2.resize(crop, (max(1, int(width * scale)), 260), interpolation=cv2.INTER_CUBIC)
-    canvas = np.full((300, 220, 3), 255, np.uint8)
-    canvas[30:290, : min(resized.shape[1], 220)] = resized[:, :220]
-    cv2.putText(canvas, str(index), (6, 22), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
-    tiles.append(canvas)
-
-grid = np.vstack([np.hstack(tiles[:5]), np.hstack(tiles[5:])])
-cv2.imwrite("data/evaluations/2026-09-29-session/scratch/fa_tiles.jpg", grid)
-print("tile:", grid.shape)
diff --git a/recognization/data/evaluations/2026-09-29-session/scratch/l04_label.jpg b/recognization/data/evaluations/2026-09-29-session/scratch/l04_label.jpg
deleted file mode 100644
index 1056b85..0000000
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diff --git a/recognization/data/evaluations/2026-09-29-session/scratch/l04_rot270.jpg b/recognization/data/evaluations/2026-09-29-session/scratch/l04_rot270.jpg
deleted file mode 100644
index efacca3..0000000
Binary files a/recognization/data/evaluations/2026-09-29-session/scratch/l04_rot270.jpg and /dev/null differ
diff --git a/recognization/data/evaluations/2026-09-29-session/scratch/l10_facing59.jpg b/recognization/data/evaluations/2026-09-29-session/scratch/l10_facing59.jpg
deleted file mode 100644
index 0806ed2..0000000
Binary files a/recognization/data/evaluations/2026-09-29-session/scratch/l10_facing59.jpg and /dev/null differ
diff --git a/recognization/data/evaluations/2026-09-29-session/scratch/l10_shelf_tag.jpg b/recognization/data/evaluations/2026-09-29-session/scratch/l10_shelf_tag.jpg
deleted file mode 100644
index 1ad63c4..0000000
Binary files a/recognization/data/evaluations/2026-09-29-session/scratch/l10_shelf_tag.jpg and /dev/null differ
diff --git a/recognization/data/evaluations/2026-09-29-session/scratch/l11_shelf_tag.jpg b/recognization/data/evaluations/2026-09-29-session/scratch/l11_shelf_tag.jpg
deleted file mode 100644
index 2268331..0000000
Binary files a/recognization/data/evaluations/2026-09-29-session/scratch/l11_shelf_tag.jpg and /dev/null differ
diff --git a/recognization/data/evaluations/2026-09-29-session/scratch/l11_tag_half0.jpg b/recognization/data/evaluations/2026-09-29-session/scratch/l11_tag_half0.jpg
deleted file mode 100644
index 6801bc9..0000000
Binary files a/recognization/data/evaluations/2026-09-29-session/scratch/l11_tag_half0.jpg and /dev/null differ
diff --git a/recognization/data/evaluations/2026-09-29-session/scratch/l11_tag_half1.jpg b/recognization/data/evaluations/2026-09-29-session/scratch/l11_tag_half1.jpg
deleted file mode 100644
index 372a5e0..0000000
Binary files a/recognization/data/evaluations/2026-09-29-session/scratch/l11_tag_half1.jpg and /dev/null differ
diff --git a/recognization/data/evaluations/2026-09-29-session/scratch/l11_tag_zoom.jpg b/recognization/data/evaluations/2026-09-29-session/scratch/l11_tag_zoom.jpg
deleted file mode 100644
index 36a2a23..0000000
Binary files a/recognization/data/evaluations/2026-09-29-session/scratch/l11_tag_zoom.jpg and /dev/null differ
diff --git a/recognization/data/evaluations/2026-09-29-session/scratch/size_gate.py b/recognization/data/evaluations/2026-09-29-session/scratch/size_gate.py
deleted file mode 100644
index e5c3e18..0000000
--- a/recognization/data/evaluations/2026-09-29-session/scratch/size_gate.py
+++ /dev/null
@@ -1,71 +0,0 @@
-"""Mass-only size contradiction, with full reads.
-
-The first pass counted every quantity token, including percentages, so a crop
-reading "カロリー 50% カット" looked like a 50 g package. Only mass units can
-contradict a package size, so this restricts to g/kg and prints the whole read
-for every accepted facing that carries one.
-"""
-import csv
-import json
-import re
-import sys
-
-import cv2
-
-sys.path.insert(0, "code")
-from eval_identity import LocalLaneAdapter, at_live_resolution, crop_of, leaked  # noqa: E402
-from identity_lanes import TextLane, normalize_text  # noqa: E402
-from ocr import build_ocr_engine  # noqa: E402
-
-MASS = re.compile(r"(\d+(?:\.\d+)?)\s*(KG|G)", re.IGNORECASE)
-# Package sizes this product class actually comes in. Below it are OCR fragments
-# of a longer number ("20" from 200); above it are concatenations ("5210").
-PLAUSIBLE = (100.0, 2000.0)
-
-
-def masses(text: str):
-    return {float(number) for number, _unit in MASS.findall(normalize_text(text))}
-
-
-rows = []
-with open("stock-lopia-clean/stock.csv", encoding="utf-8") as handle:
-    for record in csv.reader(handle):
-        if len(record) >= 2 and record[0].startswith("sku_"):
-            rows.append({"sku_id": record[0], "product_name": record[1]})
-names = {row["sku_id"]: row["product_name"] for row in rows}
-
-engine = build_ocr_engine("vision")
-text_lane = TextLane.from_catalog(rows, "stock-lopia-clean", ocr_engine=engine)
-local_lane = LocalLaneAdapter("stock-lopia-clean")
-
-payload = json.load(open("eval/labels/image6.json"))
-image = cv2.imread("add/image6.jpg")
-facings = [f for f in payload["facings"] if f["label"] != "exclude"]
-facings = [f for f in facings if leaked(image, f["box"], "stock-lopia-clean") is None]
-
-for live in (False, True):
-    picture = at_live_resolution(image) if live else image
-    scaled = facings if not live else [
-        {**f, "box": [int(round(v * picture.shape[1] / image.shape[1])) for v in f["box"]]}
-        for f in facings]
-    print(f"=== {'live' if live else 'full'} ===")
-    for facing in scaled:
-        crop = crop_of(picture, facing["box"])
-        if crop.size == 0:
-            continue
-        ranked = local_lane.rank(crop)
-        decision = local_lane.decide(crop, ranked, accept=2.0, margin=0.0, scale="local")
-        if decision.get("identity_level") != "accepted":
-            continue
-        answer = decision.get("best_sku")
-        read = text_lane.read(crop)
-        read_masses = masses(read)
-        name_masses = masses(names.get(answer, ""))
-        if not read_masses or not name_masses:
-            continue
-        plausible = {m for m in read_masses if PLAUSIBLE[0] <= m <= PLAUSIBLE[1]}
-        veto = bool(plausible) and not (read_masses & name_masses)
-        correct = "correct" if answer == facing["label"] else "WRONG"
-        print(f"  #{facing['index']:>3} {'VETO' if veto else 'keep'} {correct:<7} "
-              f"answer={answer:<9} read_m={sorted(read_masses)} name_m={sorted(name_masses)}")
-        print(f"        read: {read[:120]}")
diff --git a/recognization/data/evaluations/2026-09-29-session/scratch/unknown_row.jpg b/recognization/data/evaluations/2026-09-29-session/scratch/unknown_row.jpg
deleted file mode 100644
index a9fca04..0000000
Binary files a/recognization/data/evaluations/2026-09-29-session/scratch/unknown_row.jpg and /dev/null differ
diff --git a/recognization/data/evaluations/2026-09-29-session/server-smoke/final-summary.json b/recognization/data/evaluations/2026-09-29-session/server-smoke/final-summary.json
deleted file mode 100644
index 7d5b627..0000000
--- a/recognization/data/evaluations/2026-09-29-session/server-smoke/final-summary.json
+++ /dev/null
@@ -1,28 +0,0 @@
-{
- "endpoint": "/v1/recognize",
- "image": "add/image6.jpg",
- "catalog": "stock-lopia-clean",
- "gates": "LOCAL_ACCEPT=4.0 TEXT_MIN_SHARED=4",
- "still": {
-  "detections": 111,
-  "named": 44,
-  "matched": 44,
-  "correct": 42,
-  "wrong": 2,
-  "precision": 0.955
- },
- "stream": {
-  "seconds": 20,
-  "pan": 12,
-  "tracks_seen": 165,
-  "tracks_labelled": 62,
-  "label_changes": 0,
-  "unexplained_label_changes": 0,
-  "facings_named": "17/27",
-  "precision": 0.84,
-  "wrong": 0,
-  "unenrolled_false_accepts": 0,
-  "unknown_false_accepts": 8
- },
- "note": "Final state with every catalog correction and both calibrated accept lines live."
-}
\ No newline at end of file
diff --git a/recognization/data/evaluations/2026-09-29-session/server-smoke/gate-summary.json b/recognization/data/evaluations/2026-09-29-session/server-smoke/gate-summary.json
deleted file mode 100644
index 79deaf4..0000000
--- a/recognization/data/evaluations/2026-09-29-session/server-smoke/gate-summary.json
+++ /dev/null
@@ -1,24 +0,0 @@
-{
- "endpoint": "/v1/recognize",
- "image": "add/image6.jpg",
- "catalog": "stock-lopia-clean",
- "gates": "LOCAL_ACCEPT=4.0 TEXT_MIN_SHARED=4",
- "detections": 111,
- "named": 44,
- "matched": 44,
- "correct": 42,
- "wrong": 2,
- "precision": 0.955,
- "wrong_detail": [
-  "sku_l07 vs unknown",
-  "sku_l03 vs unknown"
- ],
- "previous": {
-  "gates": "2.0/2",
-  "named": 53,
-  "correct": 48,
-  "wrong": 5,
-  "precision": 0.906
- },
- "note": "Live still endpoint, box-IoU matched to the corrected labels. The stricter gates trade 9 names for 3 wrong labels."
-}
\ No newline at end of file
diff --git a/recognization/data/evaluations/2026-09-29-session/server-smoke/recognize-after-l12-rename.json b/recognization/data/evaluations/2026-09-29-session/server-smoke/recognize-after-l12-rename.json
deleted file mode 100644
index 2f1cc2f..0000000
--- a/recognization/data/evaluations/2026-09-29-session/server-smoke/recognize-after-l12-rename.json
+++ /dev/null
@@ -1 +0,0 @@
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deleted file mode 100644
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\ No newline at end of file
diff --git a/recognization/data/evaluations/2026-09-29-session/server-smoke/summary.json b/recognization/data/evaluations/2026-09-29-session/server-smoke/summary.json
deleted file mode 100644
index 6242ed7..0000000
--- a/recognization/data/evaluations/2026-09-29-session/server-smoke/summary.json
+++ /dev/null
@@ -1,23 +0,0 @@
-{
- "endpoint": "/v1/recognize",
- "image": "add/image6.jpg",
- "catalog": "stock-lopia-clean",
- "catalog_version": "b862c55f4623",
- "detections": 111,
- "named": 53,
- "named_without_product_info": 0,
- "view_suffixed_ids": [],
- "elapsed_s": 2.38,
- "by_sku": {
-  "sku_l10": 9,
-  "sku_l11": 5,
-  "sku_l01": 5,
-  "sku_l06": 4,
-  "sku_l09": 13,
-  "sku_l07": 6,
-  "sku_l02": 3,
-  "sku_l03": 5,
-  "sku_l05": 3
- },
- "note": "Final state: l06 current package enrolled (4/4 facings named, was 1/4 at live), l10/l11 400g, l12 renamed. l12 not named: no enrolled view."
-}
\ No newline at end of file
diff --git a/recognization/data/evaluations/2026-09-29-session/still/final_0.json b/recognization/data/evaluations/2026-09-29-session/still/final_0.json
deleted file mode 100644
index 0971533..0000000
--- a/recognization/data/evaluations/2026-09-29-session/still/final_0.json
+++ /dev/null
@@ -1,1631 +0,0 @@
-{
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diff --git a/recognization/data/evaluations/2026-09-29-session/still/final_2.json b/recognization/data/evaluations/2026-09-29-session/still/final_2.json
deleted file mode 100644
index 236034b..0000000
--- a/recognization/data/evaluations/2026-09-29-session/still/final_2.json
+++ /dev/null
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diff --git a/recognization/data/evaluations/2026-09-29-session/still/final_full.json b/recognization/data/evaluations/2026-09-29-session/still/final_full.json
deleted file mode 100644
index 9997f7f..0000000
--- a/recognization/data/evaluations/2026-09-29-session/still/final_full.json
+++ /dev/null
@@ -1,214 +0,0 @@
-{
- "image": "add/image6.jpg",
- "stock_dir": "stock-lopia-clean",
- "labels": "eval/labels/image6.json",
- "provenance": {
-  "catalog_hash": "10f55211435aa64c0ed4ca24a7b902ce4ed8a30299409680942b78ec9b3d08c8",
-  "labels_hash": "5aabf029921ced767d4e14a18e8bccb55812dd974852c56e0931a98fa10366cf",
-  "image_hash": "a08b89ba37d27c9d5a81bcd56bcd042e6baf54d2f182bbaaf47e97f14c9cd525",
-  "code_revision": "b6c87c45ea2aa3ed59f5764274341f3ffed30fe0"
- },
- "facings": 111,
- "leakage": {
-  "excluded": 0,
-  "labels": []
- },
- "hub_share_limit": 0.3,
- "enrollment": {
-  "catalog_skus": [
-   "sku_l01",
-   "sku_l02",
-   "sku_l03",
-   "sku_l04",
-   "sku_l05",
-   "sku_l06",
-   "sku_l07",
-   "sku_l08",
-   "sku_l09",
-   "sku_l10",
-   "sku_l11",
-   "sku_l12"
-  ],
-  "enrolled_skus": [
-   "sku_l01",
-   "sku_l02",
-   "sku_l03",
-   "sku_l05",
-   "sku_l06",
-   "sku_l09",
-   "sku_l11"
-  ],
-  "unenrolled_skus": {
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-   "sku_l07": 6,
-   "sku_l04": 1,
-   "sku_l08": 1
-  },
-  "facings_without_enrollment": 19,
-  "scored_facings": 109
- },
- "results": {
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-    "accept": 1.0,
-    "margin": 0.15,
-    "in_catalog": 61,
-    "out_catalog": 48,
-    "enrolled": 42,
-    "correct": 32,
-    "wrong": 77,
-    "rejected": 0,
-    "catalog_ceiling": 0.6885,
-    "identity_ceiling": 0.7619,
-    "decision_ceiling": 0.7619,
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-    "false_accepts": 77,
-    "unknown_false_accepts": 48,
-    "unenrolled_false_accepts": 19,
-    "false_accept_rate": 1.0,
-    "near_unknown_false_accept_rate": 1.0,
-    "precision": 0.2936,
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-    "hub_sku": "sku_l09",
-    "hub_alarm": false,
-    "histogram": {
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-     "sku_l05": 19,
-     "sku_l02": 15,
-     "sku_l03": 14,
-     "sku_l06": 13,
-     "sku_l11": 12,
-     "sku_l01": 8
-    },
-    "per_sku": {
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-      "total": 5
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-     "sku_l02": {
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-      "total": 4
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-     "sku_l03": {
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-     "sku_l06": {
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-     "sku_l07": {
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-      "total": 6
-     },
-     "sku_l08": {
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-      "total": 1
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-     "sku_l09": {
-      "correct": 13,
-      "total": 13
-     },
-     "sku_l10": {
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-      "total": 11
-     },
-     "sku_l11": {
-      "correct": 3,
-      "total": 9
-     }
-    },
-    "ms_per_crop": 15.4
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-    "out_catalog": 48,
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-    "wrong": 5,
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-    "end_to_end_coverage": 0.5902,
-    "false_accepts": 5,
-    "unknown_false_accepts": 4,
-    "unenrolled_false_accepts": 0,
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-    "near_unknown_false_accept_rate": 0.0,
-    "precision": 0.878,
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-    "hub_alarm": false,
-    "histogram": {
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-     "sku_l02": 3,
-     "sku_l05": 3
-    },
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-     "sku_l02": {
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-     "sku_l03": {
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-      "total": 4
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-     "sku_l04": {
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-     "sku_l07": {
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-     "sku_l08": {
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-     "sku_l09": {
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-     "sku_l11": {
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-      "total": 9
-     }
-    },
-    "ms_per_crop": 26.6
-   }
-  ]
- }
-}
\ No newline at end of file
diff --git a/recognization/data/evaluations/2026-09-29-session/still/final_rec.json b/recognization/data/evaluations/2026-09-29-session/still/final_rec.json
deleted file mode 100644
index 7691bd4..0000000
--- a/recognization/data/evaluations/2026-09-29-session/still/final_rec.json
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/recognization/data/evaluations/2026-09-29-session/stream-gated/tracks_2.json b/recognization/data/evaluations/2026-09-29-session/stream-gated/tracks_2.json
deleted file mode 100644
index 3b62006..0000000
--- a/recognization/data/evaluations/2026-09-29-session/stream-gated/tracks_2.json
+++ /dev/null
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diff --git a/recognization/data/evaluations/2026-09-29-session/stream-gated/tracks_3.json b/recognization/data/evaluations/2026-09-29-session/stream-gated/tracks_3.json
deleted file mode 100644
index 2b687d2..0000000
--- a/recognization/data/evaluations/2026-09-29-session/stream-gated/tracks_3.json
+++ /dev/null
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\ No newline at end of file
diff --git a/recognization/data/evaluations/2026-09-29-session/stream-gated/tracks_final.json b/recognization/data/evaluations/2026-09-29-session/stream-gated/tracks_final.json
deleted file mode 100644
index 22faf0e..0000000
--- a/recognization/data/evaluations/2026-09-29-session/stream-gated/tracks_final.json
+++ /dev/null
@@ -1,1807 +0,0 @@
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\ No newline at end of file
diff --git a/recognization/data/evaluations/2026-09-29-session/stream-gated/tracks_verified.json b/recognization/data/evaluations/2026-09-29-session/stream-gated/tracks_verified.json
deleted file mode 100644
index 99ce901..0000000
--- a/recognization/data/evaluations/2026-09-29-session/stream-gated/tracks_verified.json
+++ /dev/null
@@ -1,2365 +0,0 @@
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diff --git a/recognization/data/evaluations/2026-09-29-session/stream-gated/van_score.json b/recognization/data/evaluations/2026-09-29-session/stream-gated/van_score.json
deleted file mode 100644
index 3bc7919..0000000
--- a/recognization/data/evaluations/2026-09-29-session/stream-gated/van_score.json
+++ /dev/null
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\ No newline at end of file
diff --git a/recognization/data/evaluations/2026-09-29-session/stream-gated/van_tracks.json b/recognization/data/evaluations/2026-09-29-session/stream-gated/van_tracks.json
deleted file mode 100644
index 8a559ab..0000000
--- a/recognization/data/evaluations/2026-09-29-session/stream-gated/van_tracks.json
+++ /dev/null
@@ -1,1725 +0,0 @@
-{
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diff --git a/recognization/data/evaluations/2026-09-29-session/stream/bl_1.json b/recognization/data/evaluations/2026-09-29-session/stream/bl_1.json
deleted file mode 100644
index 6ff5be9..0000000
--- a/recognization/data/evaluations/2026-09-29-session/stream/bl_1.json
+++ /dev/null
@@ -1,2121 +0,0 @@
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index 0d29838..0000000
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diff --git a/recognization/data/evaluations/2026-09-29-session/stream/bl_3.json b/recognization/data/evaluations/2026-09-29-session/stream/bl_3.json
deleted file mode 100644
index d3029ab..0000000
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diff --git a/recognization/data/evaluations/2026-09-29-session/stream/fin_1.json b/recognization/data/evaluations/2026-09-29-session/stream/fin_1.json
deleted file mode 100644
index 01ae06b..0000000
--- a/recognization/data/evaluations/2026-09-29-session/stream/fin_1.json
+++ /dev/null
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diff --git a/recognization/data/evaluations/2026-09-29-session/stream/fin_2.json b/recognization/data/evaluations/2026-09-29-session/stream/fin_2.json
deleted file mode 100644
index f961444..0000000
--- a/recognization/data/evaluations/2026-09-29-session/stream/fin_2.json
+++ /dev/null
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diff --git a/recognization/data/evaluations/2026-09-29-session/stream/fin_3.json b/recognization/data/evaluations/2026-09-29-session/stream/fin_3.json
deleted file mode 100644
index 4135a06..0000000
--- a/recognization/data/evaluations/2026-09-29-session/stream/fin_3.json
+++ /dev/null
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diff --git a/recognization/data/evaluations/2026-09-29-session/stream/fin_4.json b/recognization/data/evaluations/2026-09-29-session/stream/fin_4.json
deleted file mode 100644
index 141d9f1..0000000
--- a/recognization/data/evaluations/2026-09-29-session/stream/fin_4.json
+++ /dev/null
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diff --git a/recognization/data/evaluations/2026-09-29-session/stream/final_tr.json b/recognization/data/evaluations/2026-09-29-session/stream/final_tr.json
deleted file mode 100644
index fa67991..0000000
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diff --git a/recognization/data/evaluations/2026-09-29-session/stream/full_1.json b/recognization/data/evaluations/2026-09-29-session/stream/full_1.json
deleted file mode 100644
index b3a7ff8..0000000
--- a/recognization/data/evaluations/2026-09-29-session/stream/full_1.json
+++ /dev/null
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diff --git a/recognization/data/evaluations/2026-09-29-session/stream/full_2.json b/recognization/data/evaluations/2026-09-29-session/stream/full_2.json
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index ffcb089..0000000
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-}
\ No newline at end of file
diff --git a/recognization/data/evaluations/2026-09-29-session/stream/full_3.json b/recognization/data/evaluations/2026-09-29-session/stream/full_3.json
deleted file mode 100644
index f10bd21..0000000
--- a/recognization/data/evaluations/2026-09-29-session/stream/full_3.json
+++ /dev/null
@@ -1,1424 +0,0 @@
-{
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-  "source": "add/image6.jpg",
-  "pace": "send",
-  "seconds": 20.1,
-  "tracks": []
-}
\ No newline at end of file
diff --git a/recognization/data/evaluations/2026-09-29-session/stream/fx_1.json b/recognization/data/evaluations/2026-09-29-session/stream/fx_1.json
deleted file mode 100644
index 1d1b9a9..0000000
--- a/recognization/data/evaluations/2026-09-29-session/stream/fx_1.json
+++ /dev/null
@@ -1,1697 +0,0 @@
-{
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diff --git a/recognization/data/evaluations/2026-09-29-session/stream/fx_2.json b/recognization/data/evaluations/2026-09-29-session/stream/fx_2.json
deleted file mode 100644
index 380f1d4..0000000
--- a/recognization/data/evaluations/2026-09-29-session/stream/fx_2.json
+++ /dev/null
@@ -1,1388 +0,0 @@
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-}
\ No newline at end of file
diff --git a/recognization/data/evaluations/2026-09-29-session/stream/fx_3.json b/recognization/data/evaluations/2026-09-29-session/stream/fx_3.json
deleted file mode 100644
index 00ae8f7..0000000
--- a/recognization/data/evaluations/2026-09-29-session/stream/fx_3.json
+++ /dev/null
@@ -1,1298 +0,0 @@
-{
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-  "source": "add/image6.jpg",
-  "pace": "send",
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-  "tracks": []
-}
\ No newline at end of file
diff --git a/recognization/data/evaluations/2026-09-29-session/stream/nl_1.json b/recognization/data/evaluations/2026-09-29-session/stream/nl_1.json
deleted file mode 100644
index 0dc8a01..0000000
--- a/recognization/data/evaluations/2026-09-29-session/stream/nl_1.json
+++ /dev/null
@@ -1,2455 +0,0 @@
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diff --git a/recognization/data/evaluations/2026-09-29-session/stream/nl_2.json b/recognization/data/evaluations/2026-09-29-session/stream/nl_2.json
deleted file mode 100644
index 7967ebe..0000000
--- a/recognization/data/evaluations/2026-09-29-session/stream/nl_2.json
+++ /dev/null
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diff --git a/recognization/data/evaluations/2026-09-29-session/stream/nl_3.json b/recognization/data/evaluations/2026-09-29-session/stream/nl_3.json
deleted file mode 100644
index 3b9054a..0000000
--- a/recognization/data/evaluations/2026-09-29-session/stream/nl_3.json
+++ /dev/null
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diff --git a/recognization/data/evaluations/2026-09-29-session/stream/tr_1.json b/recognization/data/evaluations/2026-09-29-session/stream/tr_1.json
deleted file mode 100644
index 2206624..0000000
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+++ /dev/null
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diff --git a/recognization/data/evaluations/2026-09-29-session/stream/tr_2.json b/recognization/data/evaluations/2026-09-29-session/stream/tr_2.json
deleted file mode 100644
index 84e5de1..0000000
--- a/recognization/data/evaluations/2026-09-29-session/stream/tr_2.json
+++ /dev/null
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-}
\ No newline at end of file
diff --git a/recognization/data/evaluations/2026-09-29-session/stream/tr_3.json b/recognization/data/evaluations/2026-09-29-session/stream/tr_3.json
deleted file mode 100644
index 8b5e8a7..0000000
--- a/recognization/data/evaluations/2026-09-29-session/stream/tr_3.json
+++ /dev/null
@@ -1,1334 +0,0 @@
-{
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-}
\ No newline at end of file
diff --git a/recognization/data/evaluations/2026-09-29-session/stream/tr_a.json b/recognization/data/evaluations/2026-09-29-session/stream/tr_a.json
deleted file mode 100644
index 7f67f98..0000000
--- a/recognization/data/evaluations/2026-09-29-session/stream/tr_a.json
+++ /dev/null
@@ -1,1376 +0,0 @@
-{
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-  "source": "add/image6.jpg",
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-}
\ No newline at end of file
diff --git a/recognization/data/evaluations/2026-09-29-session/stream/tr_b.json b/recognization/data/evaluations/2026-09-29-session/stream/tr_b.json
deleted file mode 100644
index 7873fd6..0000000
--- a/recognization/data/evaluations/2026-09-29-session/stream/tr_b.json
+++ /dev/null
@@ -1,1791 +0,0 @@
-{
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diff --git a/recognization/data/evaluations/2026-09-29-session/stream/tr_c.json b/recognization/data/evaluations/2026-09-29-session/stream/tr_c.json
deleted file mode 100644
index 20cd089..0000000
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+++ /dev/null
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-}
\ No newline at end of file
diff --git a/recognization/data/evaluations/2026-09-29-session/stream/tr_d.json b/recognization/data/evaluations/2026-09-29-session/stream/tr_d.json
deleted file mode 100644
index b70c920..0000000
--- a/recognization/data/evaluations/2026-09-29-session/stream/tr_d.json
+++ /dev/null
@@ -1,1382 +0,0 @@
-{
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diff --git a/recognization/data/evaluations/2026-09-29-session/stream/tr_e.json b/recognization/data/evaluations/2026-09-29-session/stream/tr_e.json
deleted file mode 100644
index 2735a28..0000000
--- a/recognization/data/evaluations/2026-09-29-session/stream/tr_e.json
+++ /dev/null
@@ -1,554 +0,0 @@
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\ No newline at end of file
diff --git a/recognization/data/evaluations/2026-09-29-session/stream/tracks.json b/recognization/data/evaluations/2026-09-29-session/stream/tracks.json
deleted file mode 100644
index b0c1ebe..0000000
--- a/recognization/data/evaluations/2026-09-29-session/stream/tracks.json
+++ /dev/null
@@ -1,1883 +0,0 @@
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diff --git a/recognization/data/evaluations/2026-09-29-session/stream/tracks_base.json b/recognization/data/evaluations/2026-09-29-session/stream/tracks_base.json
deleted file mode 100644
index 9cabee3..0000000
--- a/recognization/data/evaluations/2026-09-29-session/stream/tracks_base.json
+++ /dev/null
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\ No newline at end of file
diff --git a/recognization/data/evaluations/2026-09-29-session/stream/tracks_local_result.json b/recognization/data/evaluations/2026-09-29-session/stream/tracks_local_result.json
deleted file mode 100644
index f5739e1..0000000
--- a/recognization/data/evaluations/2026-09-29-session/stream/tracks_local_result.json
+++ /dev/null
@@ -1,721 +0,0 @@
-{
-  "source": "add/image6.jpg",
-  "pace": "result",
-  "seconds": 12.0,
-  "tracks": [
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-      "sku_id": "sku_l09",
-      "label_box": [
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-      "restarts": 2
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-      "sku_id": "sku_l09",
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-      "inherited": true,
-      "restarts": 2
-    },
-    {
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-      "sku_id": "sku_l09",
-      "label_box": [
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-      "label_frame_id": 0,
-      "result_frame_id": 115,
-      "inherited": true,
-      "restarts": 5
-    },
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-      "inherited": true,
-      "restarts": 0
-    },
-    {
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-      "sku_id": "sku_l09",
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-    }
-  ]
-}
\ No newline at end of file
diff --git a/recognization/data/evaluations/2026-09-29-session/stream/tracks_local_send.json b/recognization/data/evaluations/2026-09-29-session/stream/tracks_local_send.json
deleted file mode 100644
index ff1339d..0000000
--- a/recognization/data/evaluations/2026-09-29-session/stream/tracks_local_send.json
+++ /dev/null
@@ -1,791 +0,0 @@
-{
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-  "pace": "send",
-  "seconds": 12.0,
-  "tracks": [
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-      "sku_id": "sku_l09",
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-      "label_frame_id": 0,
-      "result_frame_id": 104,
-      "inherited": true,
-      "restarts": 2
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-    {
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-      "sku_id": "sku_l09",
-      "label_box": [
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-      "result_frame_id": 81,
-      "inherited": true,
-      "restarts": 2
-    },
-    {
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-      "sku_id": "sku_l09",
-      "label_box": [
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-      "label_frame_id": 0,
-      "result_frame_id": 108,
-      "inherited": true,
-      "restarts": 5
-    },
-    {
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-      "sku_id": "sku_l09",
-      "label_box": [
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-      "result_frame_id": 112,
-      "inherited": true,
-      "restarts": 0
-    },
-    {
-      "track_id": 6,
-      "sku_id": "sku_l09",
-      "label_box": [
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-      "result_frame_id": 25,
-      "inherited": true,
-      "restarts": 0
-    },
-    {
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-      "sku_id": "sku_l09",
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-      "inherited": true,
-      "restarts": 0
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-      "inherited": false,
-      "restarts": 0
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-      "inherited": true,
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-      "result_frame_id": 112,
-      "inherited": true,
-      "restarts": 1
-    },
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-      "inherited": true,
-      "restarts": 2
-    },
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-      "sku_id": "sku_l09",
-      "label_box": [
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-      "label_frame_id": 89,
-      "result_frame_id": 109,
-      "inherited": false,
-      "restarts": 0
-    },
-    {
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-      "sku_id": "sku_l09",
-      "label_box": [
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-      "inherited": false,
-      "restarts": 0
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-      "sku_id": "sku_l09",
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-      "inherited": true,
-      "restarts": 9
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-      "inherited": true,
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-    }
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-}
\ No newline at end of file
diff --git a/recognization/data/evaluations/2026-09-29-session/stream/tracks_mine.json b/recognization/data/evaluations/2026-09-29-session/stream/tracks_mine.json
deleted file mode 100644
index c739948..0000000
--- a/recognization/data/evaluations/2026-09-29-session/stream/tracks_mine.json
+++ /dev/null
@@ -1,1801 +0,0 @@
-{
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\ No newline at end of file
diff --git a/recognization/data/evaluations/2026-09-29-session/stream/tracks_nf.json b/recognization/data/evaluations/2026-09-29-session/stream/tracks_nf.json
deleted file mode 100644
index 686cbe7..0000000
--- a/recognization/data/evaluations/2026-09-29-session/stream/tracks_nf.json
+++ /dev/null
@@ -1,1837 +0,0 @@
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diff --git a/recognization/data/evaluations/2026-09-29-session/stream/tracks_noshift.json b/recognization/data/evaluations/2026-09-29-session/stream/tracks_noshift.json
deleted file mode 100644
index df6ec60..0000000
--- a/recognization/data/evaluations/2026-09-29-session/stream/tracks_noshift.json
+++ /dev/null
@@ -1,1977 +0,0 @@
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\ No newline at end of file
diff --git a/recognization/data/external-eval/README.md b/recognization/data/external-eval/README.md
deleted file mode 100644
index 44f1b6e..0000000
--- a/recognization/data/external-eval/README.md
+++ /dev/null
@@ -1,60 +0,0 @@
-# External evidence (2026-09-29)
-
-Independent-source media used to stress the identity pipeline. **None of this is
-Tier 2.** DECISION.md's Tier 2 is a second labelled store visit; these are
-`external_proxy` artifacts, and every manifest here says so.
-
-| Directory | What it holds |
-|---|---|
-| `other-mayo/` | 8 accepted images for `sku_l04`, `sku_l07`, `sku_l09` from 6 source domains, plus the scored result |
-| `l12-reconciliation/` | What `sku_l12` actually is, from the shelf tag and package pixels |
-| `kewpie/` | 12 accepted images for six SKUs from 6 source domains, plus the scored result. One is now enrolled, so it is excluded from scoring. |
-| `tier3-sources/` | Two reusable pan clips and 11 rejected candidates for a future Tier-3 replay |
-
-## Leak checks
-
-Two, both in `code/eval_external.py`, both run before any number is reported:
-
-- **Against the evaluation photo.** Every image is template-matched against
-  `add/image6.jpg`. The highest score across all 20 is 0.675 (`sku_l07_1`), and
-  the 0.95+ band that identifies a crop of the evaluation photo is empty.
-- **Against the catalog.** An image that has since been enrolled is refused
-  rather than scored. `kewpie/sku_l06_1.jpg` is one: it is now the enrolled l06
-  view, so scoring it would be training accuracy wearing a held-out label.
-
-## Result: 19 scored, 0 false accepts
-
-    .venv/bin/python code/eval_external.py --stock stock-lopia-clean \
-        data/external-eval/kewpie data/external-eval/other-mayo
-
-| Set | Scored | Correct | Abstain | False accepts | Leaked |
-|---|---|---|---|---|---|
-| `kewpie/` | 11 | 8 | 3 | **0** | 1 |
-| `other-mayo/` | 8 | 4 | 4 | **0** | 0 |
-| **Total** | **19** | **12** | **7** | **0** | **1** |
-
-Two things this establishes that the image6 shelf cannot:
-
-- **`sku_l07` is answered with no enrolled image.** Both l07 images accept
-  through the text lane alone: `sku_l07_1` reads ラッキー フヨネーズ, and
-  `sku_l07_2` reads the rear label's 製造者：丸和油脂株式会社 with 内容量：200g.
-  The catalog row is 丸和油脂 ラッキーマヨネーズ 200g, so the manufacturer and
-  the size together name it. This is the text lane's coverage claim - that a
-  product with no reference image can still be named - holding on data that was
-  not used to build it.
-- **The l04 abstentions are correct behaviour, not a miss.** l04 has no
-  enrolled image either, and its reads are weak: `sku_l04_2` reads only
-  コと旨 タ 味 (all rows score 0.0), and `sku_l04_4` reads the rear label with
-  やまや garbled to まや, scoring 0.379 - below the accept line. Abstaining on
-  a weak read is the designed outcome.
-
-`sku_l07_2` accepts with a negative local score (-0.55) on text evidence alone.
-That is the lane doing its job, but it is worth naming: the accept rests on two
-matched tokens (manufacturer + size), not on geometry.
-
-## What is still missing
-
-No external source gives per-SKU identity for the whole catalog, and no
-reusable clip combines the target category with shelf-distance text
-resolution. The Tier-2 and Tier-3 gates in DECISION.md still need the
-operator's own recorded walks.
diff --git a/recognization/data/external-eval/kewpie/eval_result.json b/recognization/data/external-eval/kewpie/eval_result.json
deleted file mode 100644
index 8937e85..0000000
--- a/recognization/data/external-eval/kewpie/eval_result.json
+++ /dev/null
@@ -1,150 +0,0 @@
-{
- "dataset": "kewpie",
- "catalog": "stock-lopia-clean",
- "note": "External proxy images, not Tier 2. Scored through the shipped LocalLaneAdapter + DecisionPolicy.",
- "rows": [
-  {
-   "file": "sku_l01_1.jpg",
-   "truth": "sku_l01",
-   "best": "sku_l01",
-   "confidence": 13.1618,
-   "status": "success",
-   "source": "local",
-   "verdict": "correct"
-  },
-  {
-   "file": "sku_l01_2.jpg",
-   "truth": "sku_l01",
-   "best": "sku_l01",
-   "confidence": 20.986,
-   "status": "success",
-   "source": "local",
-   "verdict": "correct"
-  },
-  {
-   "file": "sku_l02_1.jpg",
-   "truth": "sku_l02",
-   "best": "sku_l02",
-   "confidence": 10.4288,
-   "status": "success",
-   "source": "text",
-   "verdict": "correct"
-  },
-  {
-   "file": "sku_l02_2.jpg",
-   "truth": "sku_l02",
-   "best": "sku_l02",
-   "confidence": 7.542,
-   "status": "success",
-   "source": "local",
-   "verdict": "correct"
-  },
-  {
-   "file": "sku_l03_1.jpg",
-   "truth": "sku_l03",
-   "best": "sku_l03",
-   "confidence": 14.3581,
-   "status": "success",
-   "source": "local",
-   "verdict": "correct"
-  },
-  {
-   "file": "sku_l03_2.jpg",
-   "truth": "sku_l03",
-   "best": "sku_l03",
-   "confidence": 48.9767,
-   "status": "success",
-   "source": "local",
-   "verdict": "correct"
-  },
-  {
-   "file": "sku_l05_1.jpg",
-   "truth": "sku_l05",
-   "best": null,
-   "confidence": 0.0,
-   "status": "unknown",
-   "source": null,
-   "verdict": "abstain"
-  },
-  {
-   "file": "sku_l05_2.jpg",
-   "truth": "sku_l05",
-   "best": "sku_l05",
-   "confidence": -3.9674,
-   "status": "success",
-   "source": "text",
-   "verdict": "correct"
-  },
-  {
-   "file": "sku_l06_1.jpg",
-   "truth": "sku_l06",
-   "verdict": "leaked",
-   "leaked_into": "sku_l06",
-   "note": "enrolled in the catalog; scoring it would be training accuracy"
-  },
-  {
-   "file": "sku_l06_2.jpg",
-   "truth": "sku_l06",
-   "best": "sku_l06",
-   "confidence": 294.8985,
-   "status": "success",
-   "source": "local",
-   "verdict": "correct"
-  },
-  {
-   "file": "sku_l11_1.jpg",
-   "truth": "sku_l11",
-   "best": null,
-   "confidence": -29.9175,
-   "status": "unknown",
-   "source": null,
-   "verdict": "abstain"
-  },
-  {
-   "file": "sku_l11_2.jpg",
-   "truth": "sku_l11",
-   "best": null,
-   "confidence": 14.1552,
-   "status": "ambiguous",
-   "source": null,
-   "verdict": "abstain"
-  }
- ],
- "scored": 11,
- "leaked": 1,
- "by_sku": {
-  "sku_l01": {
-   "correct": 2,
-   "abstain": 0,
-   "wrong": 0
-  },
-  "sku_l02": {
-   "correct": 2,
-   "abstain": 0,
-   "wrong": 0
-  },
-  "sku_l03": {
-   "correct": 2,
-   "abstain": 0,
-   "wrong": 0
-  },
-  "sku_l05": {
-   "correct": 1,
-   "abstain": 1,
-   "wrong": 0
-  },
-  "sku_l06": {
-   "correct": 1,
-   "abstain": 0,
-   "wrong": 0
-  },
-  "sku_l11": {
-   "correct": 0,
-   "abstain": 2,
-   "wrong": 0
-  }
- },
- "correct": 8,
- "abstain": 3,
- "wrong": 0
-}
\ No newline at end of file
diff --git a/recognization/data/external-eval/kewpie/manifest.json b/recognization/data/external-eval/kewpie/manifest.json
deleted file mode 100644
index 0af7ee4..0000000
--- a/recognization/data/external-eval/kewpie/manifest.json
+++ /dev/null
@@ -1,191 +0,0 @@
-{
-  "dataset_type": "external_proxy",
-  "overall_note": "Independent-source Internet media for recognition stress testing. This is an external_proxy set: it is not held-out store-visit Tier 2 data and does not satisfy the DECISION.md Tier-2 capture protocol. Every accepted file was visually inspected for the exact Kewpie product and package size. Official Kewpie front packshots already enrolled in stock-lopia-clean were excluded. The two sku_l06 files are current-package retailer images showing 内臓脂肪を減らす キユーピーフィッテ; customer-listing photos showing the superseded 内臓脂肪ケア wrapper were inspected and rejected.",
-  "license_note": "All accepted files come from commercial retailer or customer marketplace pages without an explicit reuse grant. They are marked restricted-research for internal evaluation only and should not be redistributed.",
-  "coverage": {
-    "accepted_images": 12,
-    "accepted_skus": [
-      "sku_l01",
-      "sku_l02",
-      "sku_l03",
-      "sku_l05",
-      "sku_l06",
-      "sku_l11"
-    ],
-    "two_images_per_sku": [
-      "sku_l01",
-      "sku_l02",
-      "sku_l03",
-      "sku_l05",
-      "sku_l06",
-      "sku_l11"
-    ],
-    "two_source_domains_per_sku": [
-      "sku_l01",
-      "sku_l02",
-      "sku_l03",
-      "sku_l05",
-      "sku_l06",
-      "sku_l11"
-    ],
-    "not_found": []
-  },
-  "images": [
-    {
-      "file": "sku_l01_1.jpg",
-      "sku": "sku_l01",
-      "product": "キユーピー エッグケア 305g",
-      "source_page": "https://paypayfleamarket.yahoo.co.jp/item/z686464976",
-      "source_title": "キユーピー エッグケア 卵を使っていないマヨネーズタイプ 305g",
-      "media_url": "https://auctions.c.yimg.jp/images.auctions.yahoo.co.jp/image/dr000/auc0209/users/ad8016fc0a2ac99427f4bd970e286c7709594046/i-img1200x1200-1789785070735v2rkwx.jpg",
-      "width": 1200,
-      "height": 1200,
-      "view_type": "customer_front",
-      "license_status": "restricted-research",
-      "notes": "Customer-taken marketplace photo on a household surface. The front package visibly reads エッグケア and 305g; exact title corroborates the variant. Not the official Kewpie enrollment packshot. Source domain: paypayfleamarket.yahoo.co.jp; media host: auctions.c.yimg.jp."
-    },
-    {
-      "file": "sku_l01_2.jpg",
-      "sku": "sku_l01",
-      "product": "キユーピー エッグケア 305g",
-      "source_page": "https://www.amazon.co.jp/dp/B0BSVY8NNH",
-      "source_title": "キユーピー エッグケア (卵不使用) マヨネーズ タイプ 305g",
-      "media_url": "https://m.media-amazon.com/images/I/71lzmm5n5cL._AC_SL1500_.jpg",
-      "width": 1500,
-      "height": 1500,
-      "view_type": "retailer_in_use",
-      "license_status": "restricted-research",
-      "notes": "Retailer alternate image with the exact 305g package beside prepared potato salad; package text and size are visible. Not the enrolled plain front packshot. Source/media domain: amazon.co.jp / media-amazon.com."
-    },
-    {
-      "file": "sku_l02_1.jpg",
-      "sku": "sku_l02",
-      "product": "キユーピー アマニ油マヨネーズ 200g",
-      "source_page": "https://paypayfleamarket.yahoo.co.jp/item/z599237268",
-      "source_title": "キユーピー アマニ油マヨネーズ 200g 2個セット 血圧が高めの方に",
-      "media_url": "https://auctions.c.yimg.jp/images.auctions.yahoo.co.jp/image/dr000/auc0204/users/f571f9a659b398818c6c54e9862bf90f0e2d78ec/i-img900x1200-1777250065888ef77yp.jpg",
-      "width": 900,
-      "height": 1200,
-      "view_type": "customer_front_pair",
-      "license_status": "restricted-research",
-      "notes": "Customer-taken two-pack photo. Both fronts visibly read 血圧ケア アマニ油マヨネーズ and 200g; exact title corroborates size. Source domain: paypayfleamarket.yahoo.co.jp; media host: auctions.c.yimg.jp."
-    },
-    {
-      "file": "sku_l02_2.jpg",
-      "sku": "sku_l02",
-      "product": "キユーピー アマニ油マヨネーズ 200g",
-      "source_page": "https://www.amazon.co.jp/dp/B01F8L6KSQ",
-      "source_title": "キユーピー アマニ油マヨネーズ 200g [機能性表示食品]",
-      "media_url": "https://m.media-amazon.com/images/I/71s+50hXsPL._AC_SL1005_.jpg",
-      "width": 1005,
-      "height": 937,
-      "view_type": "back_label",
-      "license_status": "restricted-research",
-      "notes": "Retailer alternate rear-label view from the exact 200g listing. The unfolded back panel shows the アマニ油マヨネーズ label and 200g content declaration. Not a front enrollment image. Source/media domain: amazon.co.jp / media-amazon.com."
-    },
-    {
-      "file": "sku_l03_1.jpg",
-      "sku": "sku_l03",
-      "product": "キユーピー からしマヨネーズ 200g",
-      "source_page": "https://paypayfleamarket.yahoo.co.jp/item/z624032610",
-      "source_title": "キユーピー からしマヨネーズ 200g 4本セット",
-      "media_url": "https://auctions.c.yimg.jp/images.auctions.yahoo.co.jp/image/dr000/auc0206/users/198e53fdfc1edde3b4933f53fd4f0c02c3346cf5/i-img1200x714-1781002147108o12vd1.jpg",
-      "width": 1200,
-      "height": 714,
-      "view_type": "customer_front_bundle",
-      "license_status": "restricted-research",
-      "notes": "Customer-taken four-pack photo with visible からしマヨネーズ fronts; listing title fixes each unit at 200g. Household lighting and grouped packages differ from enrollment. Source domain: paypayfleamarket.yahoo.co.jp; media host: auctions.c.yimg.jp."
-    },
-    {
-      "file": "sku_l03_2.jpg",
-      "sku": "sku_l03",
-      "product": "キユーピー からしマヨネーズ 200g",
-      "source_page": "https://www.amazon.co.jp/dp/B0069K29EK",
-      "source_title": "キユーピー からしマヨネーズ 200g",
-      "media_url": "https://m.media-amazon.com/images/I/81ZODR6dv8L._AC_SL1500_.jpg",
-      "width": 1500,
-      "height": 1500,
-      "view_type": "retailer_in_use",
-      "license_status": "restricted-research",
-      "notes": "Retailer alternate image showing the exact 200g からしマヨネーズ package beside a prepared dish; the package front and 200g declaration are legible. Not the enrolled plain front packshot. Source/media domain: amazon.co.jp / media-amazon.com."
-    },
-    {
-      "file": "sku_l05_1.jpg",
-      "sku": "sku_l05",
-      "product": "キユーピー ゼロ ノンコレステロール 310g",
-      "source_page": "https://www.amazon.co.jp/dp/B0CBPWDXZJ",
-      "source_title": "キユーピー ゼロ ノンコレステロール マヨネーズ タイプ 310g",
-      "media_url": "https://m.media-amazon.com/images/I/81ykD7vVmIL._AC_SL1500_.jpg",
-      "width": 1305,
-      "height": 1500,
-      "view_type": "retailer_in_use_collage",
-      "license_status": "restricted-research",
-      "notes": "Retailer alternate usage collage with the exact キユーピーゼロ 310g package among prepared dishes. The composition differs materially from the enrolled front packshot. Source/media domain: amazon.co.jp / media-amazon.com."
-    },
-    {
-      "file": "sku_l05_2.jpg",
-      "sku": "sku_l05",
-      "product": "キユーピー ゼロ ノンコレステロール 310g",
-      "source_page": "https://store.shopping.yahoo.co.jp/eight-cat-store/s-b0cnsbqcly-20260717.html",
-      "source_title": "キユーピー ゼロ ノンコレステロール マヨネーズ タイプ 310g ×4個",
-      "media_url": "https://item-shopping.c.yimg.jp/i/n/eight-cat-store_s-b0cnsbqcly-20260717_4_d_20260717075244",
-      "width": 900,
-      "height": 900,
-      "view_type": "retailer_in_use_collage",
-      "license_status": "restricted-research",
-      "notes": "Independent retailer-hosted usage collage with the exact キユーピーゼロ 310g package shown among prepared dishes; the package front and 310g declaration are legible. Chosen over the same listing's plain front image so the set stays non-enrollment. Source domain: store.shopping.yahoo.co.jp; media host: item-shopping.c.yimg.jp."
-    },
-    {
-      "file": "sku_l06_1.jpg",
-      "sku": "sku_l06",
-      "product": "内臓脂肪を減らす キユーピーフィッテ 210g",
-      "source_page": "https://www.amazon.co.jp/dp/B08V2676LK",
-      "source_title": "キユーピー フィッテ 210g",
-      "media_url": "https://m.media-amazon.com/images/I/81zckoUh+vL._AC_SL1500_.jpg",
-      "width": 654,
-      "height": 1500,
-      "view_type": "retailer_current_package_front",
-      "license_status": "restricted-research",
-      "notes": "F-12 current-package evidence. The wrapper visibly says 内臓脂肪を減らす キユーピーフィッテ and 210g; the product page repeats 内臓脂肪を減らす and contains no 内臓脂肪ケア wording. This is not the superseded enrolled package. Source/media domain: amazon.co.jp / media-amazon.com."
-    },
-    {
-      "file": "sku_l06_2.jpg",
-      "sku": "sku_l06",
-      "product": "内臓脂肪を減らす キユーピーフィッテ 210g",
-      "source_page": "https://store.shopping.yahoo.co.jp/ladydrugheartshop/4901577083600.html",
-      "source_title": "キユーピー フィッテ 210g×5個",
-      "media_url": "https://item-shopping.c.yimg.jp/i/n/ladydrugheartshop_4901577083600_i_20260902221452",
-      "width": 500,
-      "height": 500,
-      "view_type": "retailer_current_package_front",
-      "license_status": "restricted-research",
-      "notes": "Second-domain F-12 corroboration. The current wrapper visibly says 内臓脂肪を減らす and 210g. Three PayPay Flea Market listings were also inspected, but all showed the superseded 内臓脂肪ケア wrapper and were rejected. Source domain: store.shopping.yahoo.co.jp; media host: item-shopping.c.yimg.jp."
-    },
-    {
-      "file": "sku_l11_1.jpg",
-      "sku": "sku_l11",
-      "product": "キユーピー ハーフ 400g",
-      "source_page": "https://paypayfleamarket.yahoo.co.jp/item/z692700538",
-      "source_title": "キユーピー ハーフ マヨネーズ 400g 2本セット カロリー50%カット",
-      "media_url": "https://auctions.c.yimg.jp/images.auctions.yahoo.co.jp/image/dr000/auc0209/users/67aace4c38f98ea5a750a86a39665a4c7bc9898d/i-img1200x1200-1790465979767g4gnwe.jpg",
-      "width": 1200,
-      "height": 1200,
-      "view_type": "customer_front_pair",
-      "license_status": "restricted-research",
-      "notes": "Customer-taken two-pack photo. Both fronts visibly show キユーピーハーフ; the title identifies each as 400g. Recorded as 400g, not the stock CSV's 450g wording. Source domain: paypayfleamarket.yahoo.co.jp; media host: auctions.c.yimg.jp."
-    },
-    {
-      "file": "sku_l11_2.jpg",
-      "sku": "sku_l11",
-      "product": "キユーピー ハーフ 400g",
-      "source_page": "https://www.amazon.co.jp/dp/B007XT4FIO",
-      "source_title": "キユーピー ハーフ 400g",
-      "media_url": "https://m.media-amazon.com/images/I/81u8+jlddyL._AC_SL1500_.jpg",
-      "width": 1406,
-      "height": 1500,
-      "view_type": "retailer_marketing_composite",
-      "license_status": "restricted-research",
-      "notes": "Retailer alternate marketing composite with the exact 400g package and product messaging. Not the plain official Kewpie front packshot. Recorded as 400g, matching the pictured package and listing title. Source/media domain: amazon.co.jp / media-amazon.com."
-    }
-  ]
-}
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-{
-  "task": "l12 reconciliation",
-  "owned_directory": "data/external-eval/l12-reconciliation",
-  "question": "The stock.csv row sku_l12 says '焼肉のたれ 中辛' (¥299). Facing #46's note says 'ユーラス 野菜をおいしく食べる旨たれ 380g; possibly what stock.csv calls l12'. Which product does the l12 row actually name?",
-
-  "conclusion": "l12 is the current ユーラス 野菜をおいしく食べる旨たれ 380g, not 焼肉のたれ 中辛. The shelf facing at #46-48 is that product, and the shelf tag directly beneath those facings reads 'ユーラス 野菜をおいしく食べる旨タレ (380g)' at ¥299 — the exact price the l12 row carries. The catalog name '焼肉のたれ 中辛' is wrong on every observable identity axis: the package and tag call it a vegetable tare (野菜をおいしく食べる旨たれ), the current package says お子さまOK・甘めテイスト rather than 中辛, and the shelf tag names ユーラス. A 2023 green-label predecessor with the same product title was 380ml, sold by 株式会社ユーラス, and carried JAN 4571233473887; that historical JAN must not be assigned to the current 380g white package without a current rear-label photo.",
-
-  "confidence": "high",
-  "confidence_detail": {
-    "l12_is_not_yakiniku_tare_chukara": "high — the shelf tag under the facings names the product, and the package front carries the same title; no yakiniku-tare package appears at that shelf position.",
-    "l12_is_yuurasu_umami_tare_380g": "high — the current shelf tag gives product title, supplier/brand (ユーラス), size (380g) and price (¥299), and the package front matches the title and the current white-label design seen in an independent 2024 in-use source.",
-    "historical_jan_4571233473887": "medium-high for the 2023 green-label predecessor only — read from the barcode photo and checksum-valid; it shares the 4571233473 prefix seen on 株式会社ユーラス's current official product pages. It is not verified for the current 380g white package.",
-    "current_jan": "unresolved — no current white-package rear-label photo was found."
-  },
-
-  "catalog_action": {
-    "recommended": "Rename sku_l12 from '焼肉のたれ 中辛' to 'ユーラス 野菜をおいしく食べる旨たれ 380g' and keep price ¥299. Record the brand/supplier as ユーラス. Do not populate a current JAN until the current white package's rear label is photographed.",
-    "do_not": "Do not enroll a 焼肉のたれ 中辛 packshot for l12, and do not treat the l12 row as a yakiniku-tare SKU in the text lane — its printed name signature would veto the correct product.",
-    "not_done_here": "No edit was made to stock-lopia-clean/stock.csv, stock-lopia/stock.csv, DECISION.md, or any shared manifest; this task owns only data/external-eval/l12-reconciliation/.",
-    "follow_up_needed": "Obtain one in-store rear-label photo of the current 380g white package to record its JAN, legal seller/manufacturer, and manufacturing plant."
-  },
-
-  "evidence": [
-    {
-      "claim": "The shelf facing at #46-48 is a tare bottle whose label reads 野菜をおいしく食べる旨たれ.",
-      "kind": "pixel-read",
-      "artifact": "detail-crops/facing46-label-10x.png",
-      "derived_from": "add/image6.jpg, facing #46 box [1285,794,1359,972]",
-      "note": "Read at 10x from the repo's own shelf photo. Same bottle continues at #47 and #48."
-    },
-    {
-      "claim": "The shelf tag directly beneath facings #46-48 reads 'ユーラス 野菜をおいしく食べる旨タレ (380g)' and ¥299.",
-      "kind": "pixel-read",
-      "artifact": "detail-crops/tag-card-full.png",
-      "derived_from": "add/image6.jpg, tag card at approx [1190,940,1500,1100]",
-      "note": "This is the load-bearing reconciliation: product title, supplier/brand, 380g size and ¥299 price all match the l12 row's slot and price."
-    },
-    {
-      "claim": "The 2023 green-label predecessor was 380ml and its seller was 株式会社ユーラス.",
-      "kind": "pixel-read",
-      "artifact": "detail-crops/seller-size-detail.png",
-      "derived_from": "media/lopia-review-ingredients.jpg (rear label of the 2023 package)",
-      "note": "Rear label gives 内容量 380ml and 販売者 株式会社ユーラス. Historical predecessor evidence, not the size record for the current 380g white package."
-    },
-    {
-      "claim": "The 2023 green-label predecessor carried JAN 4571233473887 (checksum valid).",
-      "kind": "pixel-read + arithmetic",
-      "artifact": "detail-crops/verify-jan-6x.png",
-      "derived_from": "media/lopia-review-nutrition.jpg (barcode panel, rotated to horizontal)",
-      "note": "GS1 check digit for 457123347388 computes to 7, matching the printed 13th digit. Predecessor package only."
-    },
-    {
-      "claim": "The photographed 2023 JAN shares the 4571233473 prefix with official 株式会社ユーラス product pages.",
-      "kind": "source-corroboration",
-      "artifact": "source-pages/euras-official-jan-example.html",
-      "source_page": "https://euras-global.com/products/items/4571233474440",
-      "note": "Euras's official page for なんでもかける胡麻にんにく prints JAN 4571233474440, corroborating that the predecessor's photographed JAN 4571233473887 is in the same company prefix range."
-    },
-    {
-      "claim": "The product is a sesame/garlic vegetable tare sold as a sweet-leaning (甘め) taste, not a 中辛 yakiniku tare.",
-      "kind": "source-text",
-      "artifact": "source-pages/yahoo-expert-article.html",
-      "source_page": "https://news.yahoo.co.jp/expert/articles/fe01fd24b57e314780c78342d5c55b6b34c8c030",
-      "note": "2024-11-25 article: 'ごまとにんにくが効いた甘めテイストのしょうゆたれ'. The current white package front in the same article's photo reads お子さまOK 甘めテイスト."
-    },
-    {
-      "claim": "The product is LOPIA's own-brand tare, priced ¥199 in Dec 2023, 380ml.",
-      "kind": "source-text",
-      "artifact": "source-pages/lopia-review.html",
-      "source_page": "https://lopia.web-cafe.net/yasaiwooishikutaberuumatare/",
-      "note": "Retailer-review site with its own package photos (2023-12-02). 'ロピアオリジナルの野菜をおいしく食べる旨たれ…お値段は199円で380ml入りです。'"
-    },
-    {
-      "claim": "株式会社ユーラス is a food importer and domestic private-brand developer, and lists 焼肉のたれ among its PB development categories.",
-      "kind": "source-text",
-      "artifact": "source-pages/euras-official-home.html",
-      "source_page": "https://euras-global.com/",
-      "note": "Relevant because it shows Euras does develop yakiniku tare as a category — so the l12 name is not absurd, merely not this package."
-    },
-    {
-      "claim": "The product does not appear in Euras's current official product catalog.",
-      "kind": "source-absence",
-      "artifact": "source-pages/euras-official-general-food.html",
-      "source_page": "https://euras-global.com/products/categories/general-food",
-      "note": "Checked the 一般食品 category listing (48 items) and the site root; no 旨たれ entry. Consistent with a retailer-exclusive PB item rather than a listed national product."
-    }
-  ],
-
-  "unresolved": [
-    "The current 380g white package's JAN, legal seller/manufacturer, and manufacturing plant are not visible in any source found. The historical JAN 4571233473887 belongs to the 2023 green-label 380ml predecessor and must not be copied forward without a current rear-label photo.",
-    "No official LOPIA or ユーラス product page exists for the current 旨たれ, so the current size/title/price record comes from the shelf tag and package pixels rather than a manufacturer database.",
-    "Whether LOPIA also stocks a genuine 焼肉のたれ 中辛 under some other SKU was not established; no such facing appears on the test shelf."
-  ],
-
-  "images": [
-    {
-      "file": "shelf-crops/facing46-48_wide.png",
-      "sku": "sku_l12",
-      "product": "ユーラス 野菜をおいしく食べる旨たれ 380g",
-      "source_page": "local capture: add/image6.jpg",
-      "media_url": "local: add/image6.jpg",
-      "width": 780,
-      "height": 720,
-      "view_type": "shelf",
-      "license_status": "restricted-research",
-      "notes": "Crop of the repo's own test-shelf photo covering facings #46-48 (the tare bottles) and the tag beneath them. Derived artifact; not an Internet download."
-    },
-    {
-      "file": "detail-crops/facing46-label-10x.png",
-      "sku": "sku_l12",
-      "product": "ユーラス 野菜をおいしく食べる旨たれ 380g",
-      "source_page": "local capture: add/image6.jpg",
-      "media_url": "local: add/image6.jpg",
-      "width": 720,
-      "height": 1000,
-      "view_type": "label-detail",
-      "license_status": "restricted-research",
-      "notes": "10x upscale of the facing #46 label band; the title 野菜をおいしく食べる旨たれ is legible here."
-    },
-    {
-      "file": "detail-crops/tag-card-full.png",
-      "sku": "sku_l12",
-      "product": "ユーラス 野菜をおいしく食べる旨たれ 380g",
-      "source_page": "local capture: add/image6.jpg",
-      "media_url": "local: add/image6.jpg",
-      "width": 1860,
-      "height": 960,
-      "view_type": "shelf-tag-detail",
-      "license_status": "restricted-research",
-      "notes": "6x crop of the complete price tag under facings #46-48: 'ユーラス 野菜をおいしく食べる旨タレ (380g)' and ¥299. The decisive link to the l12 row."
-    },
-    {
-      "file": "media/lopia-review-front.jpg",
-      "sku": "sku_l12",
-      "product": "ユーラス 野菜をおいしく食べる旨たれ 380ml (2023 predecessor)",
-      "source_page": "https://lopia.web-cafe.net/yasaiwooishikutaberuumatare/",
-      "media_url": "https://lopia.web-cafe.net/wp-content/uploads/2023/12/IMG_20231202_142328.jpg",
-      "width": 1000,
-      "height": 1333,
-      "view_type": "front",
-      "license_status": "restricted-research",
-      "notes": "2023-12-02 package photo (green label generation) from a LOPIA product-review site. Copyright the site author; archived for research only."
-    },
-    {
-      "file": "media/lopia-review-ingredients.jpg",
-      "sku": "sku_l12",
-      "product": "ユーラス 野菜をおいしく食べる旨たれ 380ml (2023 predecessor)",
-      "source_page": "https://lopia.web-cafe.net/yasaiwooishikutaberuumatare/",
-      "media_url": "https://lopia.web-cafe.net/wp-content/uploads/2023/12/IMG_20231202_142348.jpg",
-      "width": 1000,
-      "height": 1333,
-      "view_type": "back",
-      "license_status": "restricted-research",
-      "notes": "Rear label of the 2023 green-label predecessor: 内容量 380ml and 販売者 株式会社ユーラス. Historical package evidence; the current shelf tag specifies 380g."
-    },
-    {
-      "file": "media/lopia-review-nutrition.jpg",
-      "sku": "sku_l12",
-      "product": "ユーラス 野菜をおいしく食べる旨たれ 380ml (2023 predecessor)",
-      "source_page": "https://lopia.web-cafe.net/yasaiwooishikutaberuumatare/",
-      "media_url": "https://lopia.web-cafe.net/wp-content/uploads/2023/12/IMG_20231202_142337.jpg",
-      "width": 1000,
-      "height": 1333,
-      "view_type": "back",
-      "license_status": "restricted-research",
-      "notes": "Nutrition/barcode panel of the 2023 green-label predecessor; carries JAN 4571233473887. Do not copy this JAN to the current 380g white package without a current rear-label photo."
-    },
-    {
-      "file": "media/yahoo-expert-product-in-use.jpg",
-      "sku": "sku_l12",
-      "product": "ユーラス 野菜をおいしく食べる旨たれ 380g",
-      "source_page": "https://news.yahoo.co.jp/expert/articles/fe01fd24b57e314780c78342d5c55b6b34c8c030",
-      "media_url": "https://newsatcl-pctr.c.yimg.jp/t/iwiz-yn/creators/ryoko/02000829/image-1773281544599.jpeg",
-      "width": 2000,
-      "height": 1500,
-      "view_type": "in-use",
-      "license_status": "restricted-research",
-      "notes": "2024-11-25 in-use photo showing the current white-label package. Copyright the article author; archived for research only. Documents the package redesign between the two sources."
-    },
-    {
-      "file": "detail-crops/yahoo-newlabel-3x.png",
-      "sku": "sku_l12",
-      "product": "ユーラス 野菜をおいしく食べる旨たれ 380g",
-      "source_page": "https://news.yahoo.co.jp/expert/articles/fe01fd24b57e314780c78342d5c55b6b34c8c030",
-      "media_url": "https://newsatcl-pctr.c.yimg.jp/t/iwiz-yn/creators/ryoko/02000829/image-1773281544599.jpeg",
-      "width": 1680,
-      "height": 1920,
-      "view_type": "label-detail",
-      "license_status": "restricted-research",
-      "notes": "3x crop of the current white package; front reads お子さまOK 甘めテイスト, contradicting the catalog's 中辛."
-    },
-    {
-      "file": "detail-crops/verify-jan-6x.png",
-      "sku": "sku_l12",
-      "product": "ユーラス 野菜をおいしく食べる旨たれ 380ml (2023 predecessor)",
-      "source_page": "https://lopia.web-cafe.net/yasaiwooishikutaberuumatare/",
-      "media_url": "https://lopia.web-cafe.net/wp-content/uploads/2023/12/IMG_20231202_142337.jpg",
-      "width": 3240,
-      "height": 960,
-      "view_type": "barcode-detail",
-      "license_status": "restricted-research",
-      "notes": "6x rotated crop of the 2023 green-label predecessor barcode: 4571233473887. Checksum-valid; shares the 4571233473 prefix seen on 株式会社ユーラス official product pages. Not a verified current-package JAN."
-    },
-    {
-      "file": "detail-crops/seller-size-detail.png",
-      "sku": "sku_l12",
-      "product": "ユーラス 野菜をおいしく食べる旨たれ 380ml (2023 predecessor)",
-      "source_page": "https://lopia.web-cafe.net/yasaiwooishikutaberuumatare/",
-      "media_url": "https://lopia.web-cafe.net/wp-content/uploads/2023/12/IMG_20231202_142348.jpg",
-      "width": 1800,
-      "height": 1040,
-      "view_type": "label-detail",
-      "license_status": "restricted-research",
-      "notes": "3x crop of the 2023 green-label predecessor's rear-label seller and net-content lines."
-    }
-  ],
-
-  "source_archives": [
-    {
-      "file": "source-pages/lopia-review.html",
-      "source_page": "https://lopia.web-cafe.net/yasaiwooishikutaberuumatare/",
-      "license_status": "restricted-research",
-      "notes": "Archived HTML of the 2024-03-18 / updated 2024-06-22 review; documents the 2023 green-label 380ml predecessor at ¥199 and supplies its package photos."
-    },
-    {
-      "file": "source-pages/yahoo-expert-article.html",
-      "source_page": "https://news.yahoo.co.jp/expert/articles/fe01fd24b57e314780c78342d5c55b6b34c8c030",
-      "license_status": "restricted-research",
-      "notes": "Archived HTML of the 2024-11-25 Yahoo News expert article; independent in-use source and the current white-package photo."
-    },
-    {
-      "file": "source-pages/euras-official-home.html",
-      "source_page": "https://euras-global.com/",
-      "license_status": "restricted-research",
-      "notes": "Archived HTML of 株式会社ユーラス's official site; establishes the company as an importer/PB developer and lists 焼肉のたれ among its development categories."
-    },
-    {
-      "file": "source-pages/euras-official-general-food.html",
-      "source_page": "https://euras-global.com/products/categories/general-food",
-      "license_status": "restricted-research",
-      "notes": "Archived HTML of the official 一般食品 catalog listing; the 旨たれ is absent from it."
-    },
-    {
-      "file": "source-pages/euras-official-jan-example.html",
-      "source_page": "https://euras-global.com/products/items/4571233474440",
-      "license_status": "restricted-research",
-      "notes": "Archived HTML of an official Euras product page printing a JAN in the same 4571233473 prefix; corroborates the photographed predecessor barcode's ownership."
-    }
-  ],
-
-  "provenance": {
-    "tier": "external_proxy",
-    "tier_note": "These are independent Internet sources and the repo's own shelf photo, not the DECISION.md Tier-2 held-out store-visit protocol. Nothing here is claimed as Tier 2.",
-    "shelf_photo": "add/image6.jpg (the repository's existing test-shelf capture)",
-    "search_engines_used": "Brave Search, Google, Yahoo! Shopping, Bing (several providers were rate-limited or blocked during this task; Brave and Google produced the usable results)"
-  }
-}
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@@ -1,20 +0,0 @@
-<!DOCTYPE html><html lang="products"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="preload" as="image" href="/assets/euras_logo-kbD7mIbm.png"/><link rel="preload" as="image" href="/assets/lopia_white-COtcwHF9.png"/><link rel="preload" as="image" href="/assets/logo_oic_simple_white-JP5VVq3Q.svg"/><link rel="icon" type="image/png" href="/favicon.png"/><title>一般食品 - 商品一覧 | 株式会社ユーラス</title><link rel="canonical" href="https://euras-global.com/products/categories/general-food"/><link rel="modulepreload" href="/assets/manifest-561a08da.js"/><link rel="modulepreload" href="/assets/entry.client-BYUeBJ_y.js"/><link rel="modulepreload" href="/assets/jsx-runtime-BlMbTjDj.js"/><link rel="modulepreload" href="/assets/react-dom-ClQy4MTh.js"/><link rel="modulepreload" href="/assets/errorBoundaries-BoHOGHdt.js"/><link rel="modulepreload" href="/assets/root-BXtFc72M.js"/><link rel="modulepreload" href="/assets/lib-VOeodO5K.js"/><link rel="modulepreload" href="/assets/(_lang)-DVKFsZdG.js"/><link rel="modulepreload" href="/assets/href-BK4DXSLt.js"/><link rel="modulepreload" href="/assets/trans-DeW7rUVC.js"/><link rel="modulepreload" href="/assets/create-style-context-i5SvXlgj.js"/><link rel="modulepreload" href="/assets/hstack-VTkhGFE3.js"/><link rel="modulepreload" href="/assets/hstack-CiURqYta.js"/><link rel="modulepreload" href="/assets/createLucideIcon-BFdDM9e6.js"/><link rel="modulepreload" href="/assets/dist-o9I4pxau.js"/><link rel="modulepreload" href="/assets/factory-CFGLvu53.js"/><link rel="modulepreload" href="/assets/runtime-DkMp7cPG.js"/><link rel="modulepreload" href="/assets/icon-button-DV6UWvOJ.js"/><link rel="modulepreload" href="/assets/button-L3HlwiBN.js"/><link rel="modulepreload" href="/assets/(_lang).products.categories._category-CCEUSYug.js"/><link rel="modulepreload" href="/assets/Products-B6qIiuMG.js"/><link rel="modulepreload" href="/assets/HeroAreaSmall-jI7W8YYt.js"/><link 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button--variant_plain button--size_sm px_0 py_0 popover__trigger"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="white" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-globe" aria-hidden="true"><circle cx="12" cy="12" r="10"></circle><path d="M12 2a14.5 14.5 0 0 0 0 20 14.5 14.5 0 0 0 0-20"></path><path d="M2 12h20"></path></svg></button></div></nav></header><main><div lang="ja"><div class="h_73px lg:h_135px bg-c_gray.8 contain_content"><div class="z_-1 inset_calc(var(--blur-size)_*_-1) pos_fixed w_calc(100%_+_var(--blur-size)_*_2) h_calc(100%_+_var(--blur-size)_*_2) filter_auto blur_var(--blur-size) --blur-size_4px"><video src="/assets/background_movie_news-CHkzQJMv.mp4" autoPlay="" loop="" muted="" playsInline="" class="obj-f_cover w_100% h_100%"></video></div></div><div class="bg-c_gray.3"><div class="pos_relative max-w_8xl mx_auto px_4 md:px_6 lg:px_8 py_16"><div class="d_flex flex-d_column gap_16"><hgroup class="c_currentColor ta_center"><h2 class="heading textStyle_6xl c_currentColor ff_heading fw_normal">Product Selection</h2><p>一般食品</p></hgroup><div><div class="d_flex flex-d_column gap_16"><div class="d_grid grid-tc_repeat(auto-fill,_minmax(256px,_1fr)) gap_4"><a class="d_grid gap_8px" href="/products/items/4571233473160" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><div class="pos_absolute top_2 left_2 py_0.5 px_2 c_white fs_sm fw_bold bg-c_red.9">NEW</div><img width="384" height="384" class="w_100% h_auto" src="/assets/4571233473160-DG0cu3s3.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">強力小麦粉</h3><div class="card__description card__description--variant_outline lc_3">ふっくらとした食感と豊かな風味のパン作りに適した強力小麦粉です。パンをはじめ、ピザや餃子などにもご使用いただけます。</div></div></div></a><a class="d_grid gap_8px" href="/products/items/4571233473153" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><div class="pos_absolute top_2 left_2 py_0.5 px_2 c_white fs_sm fw_bold bg-c_red.9">NEW</div><img width="384" height="384" class="w_100% h_auto" src="/assets/4571233473153-Ctsm1uvc.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">薄力小麦粉</h3><div class="card__description card__description--variant_outline lc_3">ダマになりにくく、なめらかな仕上がりが特長の薄力小麦粉です。お菓子作りから天ぷら、お好み焼きまで幅広くご使用いただけます。</div></div></div></a><a class="d_grid gap_8px" href="/products/items/4571638860985" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><div class="pos_absolute top_2 left_2 py_0.5 px_2 c_white fs_sm fw_bold bg-c_red.9">NEW</div><img width="384" height="384" class="w_100% h_auto" src="/assets/4571638860985-BayUdpQQ.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">GOLDA トルコ産パスタ</h3><div class="card__description card__description--variant_outline lc_3">本場トルコで作られた、デュラム小麦セモリナ100％使用の本格パスタ。もちもちとした食感としっかりとしたコシが特長で、ソースとの相性も抜群です。大容量800g入りなので、ご家庭での普段使いはもちろん、まとめ調理にもおすすめ。毎日の食卓を手軽に本格パスタでお楽しみいただけます。</div></div></div></a><a class="d_grid gap_8px" href="/products/items/8006876100280" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><div class="pos_absolute top_2 left_2 py_0.5 px_2 c_white fs_sm fw_bold bg-c_red.9">NEW</div><img width="384" height="384" class="w_100% h_auto" src="/assets/8006876100280-CEAwCE3o.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">グストドーロイタリア産カットトマト缶</h3><div class="card__description card__description--variant_outline lc_3">旨みをそのまま缶に閉じ込めました。ほどよい酸味とコクが特徴で、パスタソース、煮込み料理、スープ、カレーなど幅広いメニューにお使いいただけます。</div></div></div></a><a class="d_grid gap_8px" href="/products/items/4571233478585" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><div class="pos_absolute top_2 left_2 py_0.5 px_2 c_white fs_sm fw_bold bg-c_red.9">NEW</div><img width="384" height="384" class="w_100% h_auto" src="/assets/4571233478585-Dw03NvPJ.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">さくさく天かす</h3><div class="card__description card__description--variant_outline lc_3">いかの旨みと香ばしさが楽しめる、サクサク食感の天かすです。さまざまな料理にひとふりするだけで、味わいと食感にアクセントを加えます。</div></div></div></a><a class="d_grid gap_8px" href="/products/items/4571233472996" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><div class="pos_absolute top_2 left_2 py_0.5 px_2 c_white fs_sm fw_bold bg-c_red.9">NEW</div><img width="384" height="384" class="w_100% h_auto" src="/assets/4571233472996-68_CpGWa.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">地中海 スパゲッティ 1kg</h3><div class="card__description card__description--variant_outline lc_3">ギリシャ産ならではのしっかりとしたコシと、ソースによく絡む食感が魅力のパスタです。</div></div></div></a><a class="d_grid gap_8px" href="/products/items/4571233476642" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><div class="pos_absolute top_2 left_2 py_0.5 px_2 c_white fs_sm fw_bold bg-c_red.9">NEW</div><img width="384" height="384" class="w_100% h_auto" src="/assets/4571233476642-D3yIky4y.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">SKYLEAF</div><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">SKYLEAFジャスミン茶</h3><div class="card__description card__description--variant_outline lc_3">中国原産の緑茶にジャスミンの花の香りを丁寧にまとわせた、華やかな香りと爽やかでキレのある味わいが特長です。</div></div></div></a><a class="d_grid gap_8px" href="/products/items/4571233476659" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><div class="pos_absolute top_2 left_2 py_0.5 px_2 c_white fs_sm fw_bold bg-c_red.9">NEW</div><img width="384" height="384" class="w_100% h_auto" src="/assets/4571233476659-DjAdjs11.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">SKYLEAF</div><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">SKYLEAF黒烏龍茶</h3><div class="card__description card__description--variant_outline lc_3">中国原産の烏龍茶を使用。半発酵茶ならではの芳醇な香りと、奥深い味わいが楽しめます。</div></div></div></a><a class="d_grid gap_8px" href="/products/items/4595316933853" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><div class="pos_absolute top_2 left_2 py_0.5 px_2 c_white fs_sm fw_bold bg-c_red.9">NEW</div><img width="384" height="384" class="w_100% h_auto" src="/assets/4595316933853-CQuIj4s3.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">無限ラー油</h3><div class="card__description card__description--variant_outline lc_3">フライドガーリックと胡麻の豊かな風味。思わず箸が止まらなくなる「無限ラー油」</div></div></div></a><a class="d_grid gap_8px" href="/products/items/4595316933907" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><img width="384" height="384" class="w_100% h_auto" src="/assets/4595316933907-CI2Oihfw.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">スリランカライオン 癒しの紅茶 アールグレイ</h3><div class="card__description card__description--variant_outline lc_3">スリランカ産100％のセイロンアールグレイティーを使用。ベルガモットの上品で華やかな香りが広がる、ティーバッグタイプの紅茶です。たっぷり楽しめる大容量100パック入り。</div></div></div></a><a class="d_grid gap_8px" href="/products/items/4595316933891" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><img width="384" height="384" class="w_100% h_auto" src="/assets/4595316933891-BokIyK2M.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">スリランカライオン 癒しの紅茶</h3><div class="card__description card__description--variant_outline lc_3">スリランカ産100％のセイロンブラックティーを使用。華やかな香りとすっきりとした味わいが魅力の紅茶を、便利なティーバッグで手軽にお楽しみいただけます。大容量100パック入り。</div></div></div></a><a class="d_grid gap_8px" href="/products/items/4571233477502" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><img width="384" height="384" class="w_100% h_auto" src="/assets/4571233477502-MsM7tUX0.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">アカシアはちみつ</h3><div class="card__description card__description--variant_outline lc_3">天然アカシアはちみつを使用。さっぱりとした上品な甘さと、透明感のある淡い黄金色が特徴です。</div></div></div></a><a class="d_grid gap_8px" href="/products/items/4571233473443" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><img width="384" height="384" class="w_100% h_auto" src="/assets/4571233473443-Di0KaBBI.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">純粋はちみつ</h3><div class="card__description card__description--variant_outline lc_3">天然はちみつを使用。トーストやホットケーキ、ヨーグルト、スムージーなど、さまざまなアレンジでお楽しみいただけます。</div></div></div></a><a class="d_grid gap_8px" href="/products/items/4571638861739" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><img width="384" height="384" class="w_100% h_auto" src="/assets/4571638861739-B3R56nWK.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">長崎産 焼きあご入りふりだし</h3><div class="card__description card__description--variant_outline lc_3">長崎産焼きあごをはじめ6種類の素材を使用。塩・砂糖・粉末醤油などで味を調え、化学調味料不使用で仕上げました。だしパックは破って調味料としてもご使用いただけます。</div></div></div></a><a class="d_grid gap_8px" href="/products/items/4973591491143" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><img width="384" height="384" class="w_100% h_auto" src="/assets/4973591491143-CLrSbikN.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">いわしとあおさ 無限ごはんプレミアム</h3><div class="card__description card__description--variant_outline lc_3">温かいご飯はもちろん、おにぎり、冷奴、オニオンスライス、オクラ、納豆など、いつもの料理にさっとふりかけるだけで手軽に使えます。</div></div></div></a><a class="d_grid gap_8px" href="/products/items/4571638862507" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><img width="384" height="384" class="w_100% h_auto" src="/assets/4571638862507-YcCDcyWK.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">ソフトふりかけしそわかめ200g</h3><div class="card__description card__description--variant_outline lc_3">温かいご飯はもちろん、冷めたご飯にもなじむソフトタイプのしそわかめふりかけです。おにぎりやパスタなど幅広いアレンジに活躍できます。</div></div></div></a><a class="d_grid gap_8px" href="/products/items/4571638861661" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><img width="384" height="384" class="w_100% h_auto" src="/assets/4571638861661-Cu0uxM_K.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">ハンガリー産純粋はちみつ720g</h3><div class="card__description card__description--variant_outline lc_3">ハンガリーの平野に咲く花々からミツバチが集めた香り豊かなはちみつ。クセのない上品な味をお楽しみください。</div></div></div></a><a class="d_grid gap_8px" href="/products/items/4970805011913" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><img width="384" height="384" class="w_100% h_auto" src="/assets/4970805011913-CJb5ZoYi.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">十六穀ごはん</h3><div class="card__description card__description--variant_outline lc_3">もち麦入り十六穀ごはん。十六種類の穀物が織りなす、香り豊かで多彩な食感をお楽しみいただけます。</div></div></div></a><a class="d_grid gap_8px" href="/products/items/4571233472231" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><img width="384" height="384" class="w_100% h_auto" src="/assets/4571233472231-D40dZXu5.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">お肉屋さんのソフトパン粉 530g</h3><div class="card__description card__description--variant_outline lc_3">口当たりの柔らかいソフトタイプのパン粉で、お店のようなサクサク食感をお楽しみいただけます。揚げ上がりは油切れが良く、軽い仕上がりになるのが特長です。</div></div></div></a><a class="d_grid gap_8px" href="/products/items/4571638860886" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><img width="384" height="384" class="w_100% h_auto" src="/assets/4571638860886-DXPp0GHE.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">枕崎産鰹節使用 ソフト削り</h3><div class="card__description card__description--variant_outline lc_3">道場六三郎推奨商品。鰹節に産地で有名な枕崎産の鰹節（荒節）使用して丹念に口どけしやすいように細かくソフトに削りました。いろいろな料理のトッピング用にもご利用いただけます。</div></div></div></a><a class="d_grid gap_8px" href="/products/items/4571638860893" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><img width="384" height="384" class="w_100% h_auto" src="/assets/4571638860893-c0JLL_ew.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">枕崎産鰹節使用 花かつお</h3><div class="card__description card__description--variant_outline lc_3">道場六三郎推奨商品。鰹節に産地で有名な枕崎産の鰹節（荒節）使用して、丹念に薄く削りました。だし取りから料理のトッピング用にもご利用いただける「花かつお」です。</div></div></div></a><a class="d_grid gap_8px" href="/products/items/4571233470909" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><img width="384" height="384" class="w_100% h_auto" src="/assets/4571233470909-B0r57H10.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">韓国海苔 12パック入り</h3><div class="card__description card__description--variant_outline lc_3">本場韓国産の原料を厳選し、国内の工場でごま油と塩を使って香ばしく仕上げました。個包装になっているためいつでもパリパリの食感が楽しめ、ご飯のお供やお酒のおつまみにはもちろん、細かくちぎってチョレギサラダやビビンバのトッピングにも最適です。</div></div></div></a><a class="d_grid gap_8px" href="/products/items/4970148012011" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><img width="384" height="384" class="w_100% h_auto" src="/assets/4970148012011-2ev8lNm-.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">国産わかめスープがごめ昆布入り</h3><div class="card__description card__description--variant_outline lc_3">道場六三郎推奨。北海道産がごめ昆布入り。たっぷり大容量（約25人前）楽しめます。</div></div></div></a><a class="d_grid gap_8px" href="/products/items/4571638862958" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><img width="384" height="384" class="w_100% h_auto" src="/assets/4571638862958-BSJ-dQYI.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">無限きざんジャー生姜</h3><div class="card__description card__description--variant_outline lc_3">シャキシャキ食感を残した刻みしょうがをなたね油で包み、しょうが本来の香りを最大限に引き出した風味豊かな調味油です。</div></div></div></a><a class="d_grid gap_8px" href="/products/items/4571638862941" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><img width="384" height="384" class="w_100% h_auto" src="/assets/4571638862941-DgMS8JfK.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">無限きざんジャーにんにく</h3><div class="card__description card__description--variant_outline lc_3">刻んだにんにくをなたね油に漬け込み、唐辛子と黒こしょうを加えて香ばしさを引き出した万能調味油です。</div></div></div></a><a class="d_grid gap_8px" href="/products/items/4571638862934" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><img width="384" height="384" class="w_100% h_auto" src="/assets/4571638862934-DkvKQaOB.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">無限ネギンジャー</h3><div class="card__description card__description--variant_outline lc_3">しょうがベースに葱油やごま油を加えて、香味を引き出した万能な調味料です。</div></div></div></a><a class="d_grid gap_8px" href="/products/items/4571638861395" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><img width="384" height="384" class="w_100% h_auto" src="/assets/4571638861395-Bbh5p3C5.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">鯖缶＜味噌煮＞</h3><div class="card__description card__description--variant_outline lc_3">手軽に使えて、便利な鯖缶です。</div></div></div></a><a class="d_grid gap_8px" href="/products/items/4571638861388" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><img width="384" height="384" class="w_100% h_auto" src="/assets/4571638861388-E5dCAUUI.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">鯖缶＜水煮＞</h3><div class="card__description card__description--variant_outline lc_3">手軽に使えて、便利な鯖缶です。</div></div></div></a><a class="d_grid gap_8px" href="/products/items/4571233474440" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><img width="384" height="384" class="w_100% h_auto" src="/assets/4571233474440-DR3YLE5X.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">なんでもかける胡麻にんにく</h3><div class="card__description card__description--variant_outline lc_3">癖になる胡麻とニンニク味わいがたまりません。</div></div></div></a><a class="d_grid gap_8px" href="/products/items/4571233478141" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><img width="384" height="384" class="w_100% h_auto" src="/assets/4571233478141-R9nOW_Dh.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">とまりそうでとまらないラー油ふりかけ</h3><div class="card__description card__description--variant_outline lc_3">辛い物好きにはたまらないラー油ふりかけラー油の辛さとにんにくの旨みが食欲をそそります。</div></div></div></a><a class="d_grid gap_8px" href="/products/items/4571233475980" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><img width="384" height="384" class="w_100% h_auto" src="/assets/4571233475980-CNXZ60wi.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">かけてオイシイ純正ごま油</h3><div class="card__description card__description--variant_outline lc_3">昔ながらの「圧絞製法」にこだわりごま本来の風味を活かしたごま油です。</div></div></div></a><a class="d_grid gap_8px" href="/products/items/4571233477052" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><img width="384" height="384" class="w_100% h_auto" src="/assets/4571233477052-hbR3Adkp.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">果実のおいしさそのまま国産みかんジャム</h3><div class="card__description card__description--variant_outline lc_3">国産みかんを贅沢に使用しました。ごろごろとした果肉が特長です。</div></div></div></a><a class="d_grid gap_8px" href="/products/items/4571233477045" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><img width="384" height="384" class="w_100% h_auto" src="/assets/4571233477045-Y80hoAyu.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">果実のおいしさそのまま国産りんごジャム</h3><div class="card__description card__description--variant_outline lc_3">国産りんごの食感を存分にお楽しみいただけます。</div></div></div></a><a class="d_grid gap_8px" href="/products/items/4571233478196" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><img width="384" height="384" class="w_100% h_auto" src="/assets/4571233478196-CM2J6uOF.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">讃岐そうめん</h3><div class="card__description card__description--variant_outline lc_3">のどごし抜群のそうめんです。</div></div></div></a><a class="d_grid gap_8px" href="/products/items/4571233478189" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><img width="384" height="384" class="w_100% h_auto" src="/assets/4571233478189-Cp1UEdY5.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">讃岐うどん</h3><div class="card__description card__description--variant_outline lc_3">のどごし抜群のうどんです。冷でも温でも楽しめます。</div></div></div></a><a class="d_grid gap_8px" href="/products/items/4571233473634" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><img width="384" height="384" class="w_100% h_auto" src="/assets/4571233473634-bTl2lfkZ.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div 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lc_3">タイ産のトウモロコシを収穫24時間以内にパッキング。シャキッとした歯ごたえ。</div></div></div></a><a class="d_grid gap_8px" href="/products/items/4571233474518" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><img width="384" height="384" class="w_100% h_auto" src="/assets/4571233474518-BgNnDSzm.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">SKYLEAF</div><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">SKYLEAF メープルシロップ アンバーリッチテイスト</h3><div class="card__description card__description--variant_outline lc_3">カナダ産メープルシロップ。ダークより繊細な風味のアンバー。透明ラベルを採用。</div></div></div></a><a class="d_grid gap_8px" href="/products/items/4571233474631" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><img width="384" height="384" class="w_100% h_auto" src="/assets/4571233474631-C-JLga9t.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">SKYLEAFOrganic</div><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">SKYLEAF</div><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">SKYLEAF オーガニックアガベシロップ</h3><div class="card__description card__description--variant_outline lc_3">有機JAS認証品。少量でも甘くて、後味スッキリ！100%天然由来の低GI甘味料です。</div></div></div></a><a class="d_grid gap_8px" href="/products/items/4571233476994" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><img width="384" height="384" class="w_100% h_auto" src="/assets/4571233476994-Drt8JePQ.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">SKYLEAF</div><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">SKYLEAF からだ想いのこめ油</h3><div class="card__description card__description--variant_outline lc_3">酸化しにくく、保存性に優れたこめ油です。玄米にも含まれるγ-オリザノールや抗酸化作用で知られるビタミンEなどが含まれています。</div></div></div></a><a class="d_grid gap_8px" href="/products/items/4571638861791" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><img width="384" height="384" class="w_100% h_auto" src="/assets/4571638861791-Ck13OfX_.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">うま辛っ 台湾穂先メンマ</h3><div class="card__description card__description--variant_outline lc_3">ピリ辛の味わいとメンマの豊かな風味が後を引く、やみつきになるおいしさです。</div></div></div></a><a class="d_grid gap_8px" href="/products/items/9421030362899" data-discover="true"><div class="card__root card__root--variant_outline pos_relative"><img width="384" height="384" class="w_100% h_auto" src="/assets/9421030362899-wI4ufuX4.webp" alt="product" loading="lazy"/><div class="gap_2 p_4 card__body card__body--variant_outline"><div class="d_flex ai_center gap_8px flex-d_row grid-g_2 rg_1 flex-wrap_wrap"><div class="py_0.5 px_1 c_white fs_sm bg-c_black.a10">一般食品</div></div><h3 class="card__title card__title--variant_outline tov_ellipsis ov_hidden white-space_nowrap">Kiwi マヌカハニー UMF 6+ MGO 115+</h3><div class="card__description card__description--variant_outline lc_3">ニュージーランド産マヌカハニーの純度と品質を保証するUMF認証取得。抗菌作用が特長。</div></div></div></a></div></div></div></div></div></div><div class="bg-c_gray.1"><div class="pos_relative max-w_8xl mx_auto px_4 md:px_6 lg:px_8 py_16"><div class="d_flex flex-d_column gap_16"><hgroup class="c_currentColor ta_center"><h2 class="heading textStyle_6xl c_currentColor ff_heading fw_normal">Food Category</h2><p>商品カテゴリー</p></hgroup><div><ul class="d_flex ai_center gap_8px flex-d_row flex-wrap_wrap"><li class="flex_1 min-w_7em"><a class="button button--variant_solid button--size_md w_full" href="/products/categories/skyleaf" data-discover="true">SKYLEAF</a></li><li class="flex_1 min-w_7em"><a class="button button--variant_solid button--size_md w_full" href="/products/categories/rokusaburo-michiba" data-discover="true">道場六三郎</a></li><li class="flex_1 min-w_7em"><a class="button button--variant_solid button--size_md w_full" href="/products/categories/general-food" 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530g\",\"口当たりの柔らかいソフトタイプのパン粉で、お店のようなサクサク食感をお楽しみいただけます。揚げ上がりは油切れが良く、軽い仕上がりになるのが特長です。\",\"4970805011913\",\"/assets/4970805011913-CJb5ZoYi.webp\",[58],\"十六穀ごはん\",\"もち麦入り十六穀ごはん。十六種類の穀物が織りなす、香り豊かで多彩な食感をお楽しみいただけます。\",\"4571638861661\",\"/assets/4571638861661-Cu0uxM_K.webp\",[58],\"ハンガリー産純粋はちみつ720g\",\"ハンガリーの平野に咲く花々からミツバチが集めた香り豊かなはちみつ。クセのない上品な味をお楽しみください。\",\"4571638862507\",\"/assets/4571638862507-YcCDcyWK.webp\",[58],\"ソフトふりかけしそわかめ200g\",\"温かいご飯はもちろん、冷めたご飯にもなじむソフトタイプのしそわかめふりかけです。おにぎりやパスタなど幅広いアレンジに活躍できます。\",\"4973591491143\",\"/assets/4973591491143-CLrSbikN.webp\",[58],\"いわしとあおさ 無限ごはんプレミアム\",\"温かいご飯はもちろん、おにぎり、冷奴、オニオンスライス、オクラ、納豆など、いつもの料理にさっとふりかけるだけで手軽に使えます。\",\"4571638861739\",\"/assets/4571638861739-B3R56nWK.webp\",[58],\"長崎産 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トルコ産パスタ\",\"本場トルコで作られた、デュラム小麦セモリナ100％使用の本格パスタ。もちもちとした食感としっかりとしたコシが特長で、ソースとの相性も抜群です。大容量800g入りなので、ご家庭での普段使いはもちろん、まとめ調理にもおすすめ。毎日の食卓を手軽に本格パスタでお楽しみいただけます。\",\"4571233473153\",\"/assets/4571233473153-Ctsm1uvc.webp\",[58],\"薄力小麦粉\",\"ダマになりにくく、なめらかな仕上がりが特長の薄力小麦粉です。お菓子作りから天ぷら、お好み焼きまで幅広くご使用いただけます。\",\"4571233473160\",\"/assets/4571233473160-DG0cu3s3.webp\",[58],\"強力小麦粉\",\"ふっくらとした食感と豊かな風味のパン作りに適した強力小麦粉です。パンをはじめ、ピザや餃子などにもご使用いただけます。\",\"common\",{\"_336\":337,\"_338\":339,\"_340\":341,\"_342\":343,\"_344\":345,\"_346\":347,\"_348\":349,\"_350\":351,\"_352\":353,\"_354\":355,\"_356\":357,\"_358\":359,\"_360\":361},\"lang\",\"ja\",\"header.home\",\"ホーム\",\"header.news\",\"お知らせ\",\"header.products\",\"商品情報\",\"header.profile\",\"会社情報\",\"header.recruit\",\"求人情報\",\"header.contact\",\"お問い合わせ\",\"header.openMenu\",\"メニューを開く\",\"header.closeMenu\",\"閉じる\",\"header.shopping\",\"お買い物\",\"footer.tagline\",\"海外商品調達、国内PB商品開発のユーラス\",\"footer.copyright\",\"Copyright ©️ 2024 EURAS Co., Ltd.\",\"footer.lopiaAlt\",\"株式会社ロピア\",\"footer.oicAlt\",\"株式会社OICグループ\"]\n");</script><!--$--><script>window.__reactRouterContext.streamController.close();</script><!--/$--><!--/$--><script>(function(){function c(){var b=a.contentDocument||(a.contentWindow&&a.contentWindow.document);if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a4248d4c1962d53e',t:'MTc5MDYxNzQyMg=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var 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deleted file mode 100644
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ai_center jc_flex-end gap_8px h_100%"><div class="d_flex flex-d_column jc_center gap_8px flex_1"><div class="pos_relative max-w_6xl mx_auto px_4 md:px_6 lg:px_8 h_fit-content"><hgroup class="d_flex flex-d_column gap_8"><div class="d_flex flex-d_column gap_8px"><div class="c_white fs_xl"></div><h1 class="heading textStyle_4xl xl:textStyle_6xl c_white ff-s_&#x27;palt&#x27; fw_semibold ls_wide lh_1.5 wb_keep-all ov-wrap_anywhere">「おいしい」をつなぎ、広げ、世界中で新たな食文化を創り出す</h1></div><p class="c_white tw_balance ls_widest lh_1.5 white-space_pre-line">「おいしい」は、人の心を動かす。人の心を動かすには、想像を超える挑戦が必要だ。
-
-私たちは、世界中の「おいしい」を研究し、掛け合わせ、
-果敢に、新たな価値を生み出し、つなぎ広げることで、
-日本と世界の人びとの食生活を、ワクワク・ドキドキするものへと変えていく。</p></hgroup></div></div><svg xmlns="http://www.w3.org/2000/svg" width="128" height="128" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-chevron-down c_#fff9" aria-hidden="true"><path d="m6 9 6 6 6-6"></path></svg></div></div><div class="bg-c_gray.1"><div class="pos_relative max-w_6xl mx_auto px_4 md:px_6 lg:px_8 py_16"><div class="d_flex flex-d_column gap_16"><hgroup class="c_currentColor ta_center"><h2 class="heading textStyle_6xl c_currentColor ff_heading fw_normal">Code Of Conduct</h2><p>行動規範</p></hgroup><div><div class="d_flex flex-d_column gap_16"><ul class="mx_auto ps_20px fs_xl lh_1.6 li-t_disc d_flex flex-d_column gap_2"><li>お客様とお取引先様の 「おいしい」「たのしい」「うれしい」を実現する。</li><li>枠にとらわれず、 広く価値を出すことに、 果敢に挑戦し続ける。</li><li>自ら行動して得た「知見」と、本質を見抜く「見識」を持つ、プロフェッショナルたれ。</li></ul></div></div></div></div></div><div class="bg-c_gray.3"><div class="pos_relative max-w_6xl mx_auto px_4 md:px_6 lg:px_8 py_16"><div class="d_flex flex-d_column gap_16"><hgroup class="c_currentColor ta_center"><h2 class="heading textStyle_6xl c_currentColor ff_heading fw_normal">Services</h2><p>事業内容</p></hgroup><div><div class="d_flex flex-d_column gap_16"><div class="d_flex flex-d_column gap_8px gap_8px mx_auto fs_xl lh_1.6 ov-wrap_anywhere"><p>ユーラスは、世界各地から食料品、飲料、酒類などの調達、
-国内でのプライベートブラント（PB）商品の開発を主要な事業としています。
-何よりもナショナルブランド（NB）商品と同等以上の品質であることを重視し、
-商品開発及び調達を行なっています。</p></div></div></div></div></div></div><div class="bg-c_gray.3"><div class="pos_relative max-w_6xl mx_auto px_4 md:px_6 lg:px_8 py_16"><div class="d_flex flex-d_column gap_16"><div><div class="d_flex flex-d_column gap_16"><div class="d_flex flex-d_column sm:flex-d_row gap_8"><div class="flex_1"><picture><source type="image/webp" srcSet="/assets/%E6%96%B0%E9%AE%AE%E3%81%AA%E8%87%AA%E5%AE%B6%E8%A3%BD%E3%83%91%E3%82%B9%E3%82%BF-m3FKiGR4.webp 410w"/><img alt="" width="410" height="410" src="/assets/%E6%96%B0%E9%AE%AE%E3%81%AA%E8%87%AA%E5%AE%B6%E8%A3%BD%E3%83%91%E3%82%B9%E3%82%BF-cn1bioO5.jpeg"/></picture></div><div class="d_flex flex-d_column gap_8 flex_2"><h2 class="heading textStyle_3xl fw_medium">世界各地からの食料品、
-飲料、酒類などの調達</h2><p>世界各地から、年間約150アイテムの食料品、飲料、酒類などを調達しています。価格、品質に優れていることは当然として、各国のトレンド商品や特徴的な商品などを発掘し、お客様に“食”の楽しさや新たな“発見”を提供していきます。</p><div class="bd_1px_solid bd-c_gray.7 py_2 px_4 fs_sm">［調達事例］<br/>パスタ（イタリア）、トマト缶（イタリア）、ワイン（フランス、イタリア、チリなど）、豚肉（スペイン）、牛肉（アメリカ）、パイナップル（台湾）、バナナ（台湾）、EXバージンオリーブオイル（スペイン）、豆板醤（中国）、ティラミス（イタリア）、クッキー（トルコ）など</div></div></div><div class="d_flex flex-d_column sm:flex-d_row gap_8"><div class="flex_1"><picture><source type="image/webp" srcSet="/assets/img_euras_001-BVBtG3AP.webp 410w"/><img alt="" width="410" height="410" src="/assets/img_euras_001-mbNO7m0d.jpeg"/></picture></div><div class="d_flex flex-d_column gap_8 flex_2"><h2 class="heading textStyle_3xl fw_medium">国内でのPB商品の開発</h2><p>国内食品メーカーと提携して、自社企画のプライベートブランド（PB）商品を開発しています。ナショナルブランド（NB）商品と同等品質以上のものをより安く提供するだけでなく、これまでのNB商品にはない新しいコンセプトの商品の開発をめざしています。</p><div class="bd_1px_solid bd-c_gray.7 py_2 px_4 fs_sm">［開発事例］<br/>焼肉のたれ、万能和風出汁、寄せ鍋つゆ、ドレッシング、コーヒー豆、お味噌汁、つまみ・珍味、冷凍食品各種、はちみつ、菓子類、パン生地（デニッシュ生地）、缶詰各種など</div></div></div><div class="d_flex flex-d_column sm:flex-d_row gap_8"><div class="flex_1"><picture><source type="image/webp" srcSet="/assets/euras_main_07_03-C6UXQxsZ.webp 410w"/><img alt="" width="410" height="410" src="/assets/euras_main_07_03-DEB97NQf.jpeg"/></picture></div><div class="d_flex flex-d_column gap_8 flex_2"><h2 class="heading textStyle_3xl fw_medium">お客様の健康な食生活を大切に</h2><p>ユーラスではお客様の健康的な食生活を重視しております。 代表例として、「千代の一番万能和風だし」は鹿児島産かつお荒節や北海道産利尻昆布など国産の原料のみを使用しています。 甘味料、保存料、着色料などを使用しておりません。</p></div></div></div></div></div></div></div></div></main><footer class="pos_sticky top_var(--dvh) py_2 px_4 bg-c_gray.11"><div class="d_flex flex-d_column sm:flex-d_row gap_4"><div class="mr_auto"><img 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fw_medium">なんでもかける胡麻にんにく</h1><picture><source type="image/webp" srcSet="/assets/4571233474440-DR3YLE5X.webp 384w, /assets/4571233474440-CuLQHdjf.webp 512w, /assets/4571233474440-DLsf9SKb.webp 768w, /assets/4571233474440-jlOgk9AS.webp 1024w"/><img class="w_100% h_auto" alt="" loading="lazy" width="1024" height="1024" src="/assets/4571233474440-jlOgk9AS.webp"/></picture><p>癖になる胡麻とニンニク味わいがたまりません。</p><dl class="d_flex flex-d_column gap_8 sm:gap_2px"><div class="d_flex flex-d_column sm:flex-d_row ai_stretch gap_0 va_middle"><dt class="d_grid ai_center w_64 py_4 px_12 c_white fw_bold va_middle bg-c_gray.10 text-align-last_justify">JANコード</dt><dd class="w_full py_4 px_4 bg-c_white">4571233474440</dd></div><div class="d_flex flex-d_column sm:flex-d_row ai_stretch gap_0 va_middle"><dt class="d_grid ai_center w_64 py_4 px_12 c_white fw_bold va_middle bg-c_gray.10 text-align-last_justify">内容量</dt><dd class="w_full py_4 px_4 bg-c_white">110g</dd></div></dl><div class="d_flex flex-d_column gap_8px"><div>タグ</div><ul class="d_flex flex-d_row gap_8px"><li><a class="button button--variant_solid button--size_md" href="/products/categories/general-food" data-discover="true">一般食品</a></li></ul></div></div></div></div><div class="bg-c_gray.1"><div class="pos_relative max-w_8xl mx_auto px_4 md:px_6 lg:px_8 py_16"><div class="d_flex flex-d_column gap_16"><hgroup class="c_currentColor ta_center"><h2 class="heading textStyle_6xl c_currentColor ff_heading fw_normal">Food Category</h2><p>商品カテゴリー</p></hgroup><div><ul class="d_flex ai_center gap_8px flex-d_row flex-wrap_wrap"><li class="flex_1 min-w_7em"><a class="button button--variant_solid button--size_md w_full" href="/products/categories/skyleaf" data-discover="true">SKYLEAF</a></li><li class="flex_1 min-w_7em"><a class="button button--variant_solid button--size_md w_full" href="/products/categories/rokusaburo-michiba" data-discover="true">道場六三郎</a></li><li class="flex_1 min-w_7em"><a class="button button--variant_solid 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-	<h1 class="c-postTitle__ttl">【ごまが沢山！！】ロピアの野菜をおいしく食べる旨たれ</h1>
-			<time class="c-postTitle__date u-thin" datetime="2024-03-19" aria-hidden="true">
-			<span class="__y">2024</span>
-			<span class="__md">3/19</span>
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-<div class="p-toc -double"><span class="p-toc__ttl">目次</span></div><h2 class="wp-block-heading">ロピアオリジナルの野菜をおいしく食べる旨たれ</h2>
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-<figure class="wp-block-image size-large"><img decoding="async" width="768" height="1024" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" data-src="https://lopia.web-cafe.net/wp-content/uploads/2023/12/IMG_20231202_142328-768x1024.jpg" alt="" class="wp-image-3219 lazyload" data-srcset="https://lopia.web-cafe.net/wp-content/uploads/2023/12/IMG_20231202_142328-768x1024.jpg 768w, https://lopia.web-cafe.net/wp-content/uploads/2023/12/IMG_20231202_142328-225x300.jpg 225w, https://lopia.web-cafe.net/wp-content/uploads/2023/12/IMG_20231202_142328.jpg 1000w" sizes="(max-width: 768px) 100vw, 768px"  data-aspectratio="768/1024"><noscript><img decoding="async" width="768" height="1024" src="https://lopia.web-cafe.net/wp-content/uploads/2023/12/IMG_20231202_142328-768x1024.jpg" alt="" class="wp-image-3219" srcset="https://lopia.web-cafe.net/wp-content/uploads/2023/12/IMG_20231202_142328-768x1024.jpg 768w, https://lopia.web-cafe.net/wp-content/uploads/2023/12/IMG_20231202_142328-225x300.jpg 225w, https://lopia.web-cafe.net/wp-content/uploads/2023/12/IMG_20231202_142328.jpg 1000w" sizes="(max-width: 768px) 100vw, 768px" ></noscript></figure>
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-
-
-<p class="wp-block-paragraph">今回紹介する商品はロピアオリジナルの野菜をおいしく食べる旨たれです。</p>
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-
-<p class="wp-block-paragraph">お値段は199円で380ml入りです。</p>
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-
-<p class="wp-block-paragraph">本当に野菜がおいしくなるのでしょうか&#8230;？</p>
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-<h2 class="wp-block-heading">原材料名</h2>
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-<figure class="wp-block-image size-large"><img decoding="async" width="768" height="1024" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" data-src="https://lopia.web-cafe.net/wp-content/uploads/2023/12/IMG_20231202_142348-768x1024.jpg" alt="" class="wp-image-3238 lazyload" data-srcset="https://lopia.web-cafe.net/wp-content/uploads/2023/12/IMG_20231202_142348-768x1024.jpg 768w, https://lopia.web-cafe.net/wp-content/uploads/2023/12/IMG_20231202_142348-225x300.jpg 225w, https://lopia.web-cafe.net/wp-content/uploads/2023/12/IMG_20231202_142348.jpg 1000w" sizes="(max-width: 768px) 100vw, 768px"  data-aspectratio="768/1024"><noscript><img decoding="async" width="768" height="1024" src="https://lopia.web-cafe.net/wp-content/uploads/2023/12/IMG_20231202_142348-768x1024.jpg" alt="" class="wp-image-3238" srcset="https://lopia.web-cafe.net/wp-content/uploads/2023/12/IMG_20231202_142348-768x1024.jpg 768w, https://lopia.web-cafe.net/wp-content/uploads/2023/12/IMG_20231202_142348-225x300.jpg 225w, https://lopia.web-cafe.net/wp-content/uploads/2023/12/IMG_20231202_142348.jpg 1000w" sizes="(max-width: 768px) 100vw, 768px" ></noscript></figure>
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-
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-<p class="wp-block-paragraph"></p>
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-<h2 class="wp-block-heading">栄養成分表示（100g当たり）</h2>
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-<figure class="wp-block-image size-large"><img decoding="async" width="768" height="1024" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" data-src="https://lopia.web-cafe.net/wp-content/uploads/2023/12/IMG_20231202_142337-768x1024.jpg" alt="" class="wp-image-3220 lazyload" data-srcset="https://lopia.web-cafe.net/wp-content/uploads/2023/12/IMG_20231202_142337-768x1024.jpg 768w, https://lopia.web-cafe.net/wp-content/uploads/2023/12/IMG_20231202_142337-225x300.jpg 225w, https://lopia.web-cafe.net/wp-content/uploads/2023/12/IMG_20231202_142337.jpg 1000w" sizes="(max-width: 768px) 100vw, 768px"  data-aspectratio="768/1024"><noscript><img decoding="async" width="768" height="1024" src="https://lopia.web-cafe.net/wp-content/uploads/2023/12/IMG_20231202_142337-768x1024.jpg" alt="" class="wp-image-3220" srcset="https://lopia.web-cafe.net/wp-content/uploads/2023/12/IMG_20231202_142337-768x1024.jpg 768w, https://lopia.web-cafe.net/wp-content/uploads/2023/12/IMG_20231202_142337-225x300.jpg 225w, https://lopia.web-cafe.net/wp-content/uploads/2023/12/IMG_20231202_142337.jpg 1000w" sizes="(max-width: 768px) 100vw, 768px" ></noscript></figure>
-
-
-
-<h2 class="wp-block-heading">ロピアオリジナルのドレッシング</h2>
-
-
-
-<p class="wp-block-paragraph">ロピアオリジナルのドレッシングはたくさんあります。</p>
-
-
-
-<p class="wp-block-paragraph">どれもオシャレでおいしいものばかりです！</p>
-
-
-
-<p class="wp-block-paragraph">以前に本サイトでもいくつかご紹介しているのでそちらも見てみてください！</p>
-
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-
-<p class="wp-block-paragraph"><a href="https://lopia.web-cafe.net/kobudoresshinngu/">コブサラダドレッシング</a>、<a href="https://lopia.web-cafe.net/gomadoresshinngu/">ごまドレッシング</a>、<a href="https://lopia.web-cafe.net/vegeoil/">ベジグレットドレッシング</a>、<a href="https://lopia.web-cafe.net/oniondressing/">たまねぎドレッシング</a></p>
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-
-
-<h2 class="wp-block-heading">実食</h2>
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-<figure class="wp-block-image size-full"><img decoding="async" width="1000" height="750" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" data-src="https://lopia.web-cafe.net/wp-content/uploads/2023/12/IMG_20231202_183410.jpg" alt="" class="wp-image-3222 lazyload" data-srcset="https://lopia.web-cafe.net/wp-content/uploads/2023/12/IMG_20231202_183410.jpg 1000w, https://lopia.web-cafe.net/wp-content/uploads/2023/12/IMG_20231202_183410-300x225.jpg 300w, https://lopia.web-cafe.net/wp-content/uploads/2023/12/IMG_20231202_183410-768x576.jpg 768w" sizes="(max-width: 1000px) 100vw, 1000px"  data-aspectratio="1000/750"><noscript><img decoding="async" width="1000" height="750" src="https://lopia.web-cafe.net/wp-content/uploads/2023/12/IMG_20231202_183410.jpg" alt="" class="wp-image-3222" srcset="https://lopia.web-cafe.net/wp-content/uploads/2023/12/IMG_20231202_183410.jpg 1000w, https://lopia.web-cafe.net/wp-content/uploads/2023/12/IMG_20231202_183410-300x225.jpg 300w, https://lopia.web-cafe.net/wp-content/uploads/2023/12/IMG_20231202_183410-768x576.jpg 768w" sizes="(max-width: 1000px) 100vw, 1000px" ></noscript></figure>
-
-
-
-<p class="wp-block-paragraph">レタス、にんじん、お豆、ちりめんじゃこのサラダにかけてみました。</p>
-
-
-
-<p class="wp-block-paragraph">ねりごまと粒のごまの二種類が入っていてとても風味が良いです。</p>
-
-
-
-<p class="wp-block-paragraph">本当に野菜がおいしくなりました！</p>
-
-
-
-<p class="wp-block-paragraph">気になった方はぜひ購入してみて下さいね。</p>
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-<p class="wp-block-paragraph"></p>
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-												<b class="fn"><a href="https://lopia.web-cafe.net/doresshinnguhenn/" class="url" rel="ugc">ロピアのオススメ商品ベスト5！！【ドレッシング編】 | コスパ最強『ロピア』～激安商品レビューサイト～</a></b> <span class="says">より:</span>					</div><!-- .comment-author -->
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eYthLZ"><div class="sc-3e34fa82-3 jCjqfe"><div class="sc-3e34fa82-4 DxpiH"><time>2024/11/25(月) 7:01</time></div></div><div class="sc-3e34fa82-17 fRLlWY"><div class="sc-92b61d1d-1 jBOjKQ"><div class="riff-flex riff-flex-row"><div class="riff-Stack__item riff-flex riff-flex-row riff-ml-2"><a class="sc-92b61d1d-0 kzGGXt riff-SocialButtonX__root riff-Clickable__root riff-inline-flex riff-justify-center riff-items-center riff-relative riff-pr-0 riff-pl-0 riff-w-6 riff-h-6 riff-text-medium riff-font-bold riff-leading-none riff-rounded-tl-full riff-rounded-tr-full riff-rounded-bl-full riff-rounded-br-full riff-appearance-none riff-outline-none" aria-label="X" 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riff-opacity-0 riff-pointer-events-none"></div></a></div></div></div></div></div></div></header><figure class="sc-3e34fa82-14 jFRNmO"><picture><source type="image/webp" srcSet="https://newsatcl-pctr.c.yimg.jp/t/iwiz-yn/creators/ryoko/02000829/title-1773107580378.jpeg?exp=10800&amp;fmt=webp"/><source type="image/jpeg" srcSet="https://newsatcl-pctr.c.yimg.jp/t/iwiz-yn/creators/ryoko/02000829/title-1773107580378.jpeg?exp=10800"/><img src="https://newsatcl-pctr.c.yimg.jp/t/iwiz-yn/creators/ryoko/02000829/title-1773107580378.jpeg?exp=10800" width="640" height="360" alt="" class="sc-3e34fa82-15 cpvCIx"/></picture><figcaption class="sc-3e34fa82-16 dOLKvn">ロピアの旨たれでつくるお弁当</figcaption></figure><section class="sc-c3a29e54-1 hbHapG"><div data-ual-view-type="detail" data-ual="service:news;apptype:web;opttype:pc;content_id:fe01fd24b57e314780c78342d5c55b6b34c8c030;id_type:shannon_article;mtestid:mfn_98466=art28t2&amp;mfn_99717=art30t2&amp;mfn_18800=ttlg2t2"><p data-role="paragraph">忙しい朝、時間がなくてお弁当作りがプレッシャーに感じることはありませんか？でも大丈夫！今回は、<b data-role="bring-attention">ロピアの「野菜をおいしく食べる旨たれ」</b>を使って、時短に成功！さらに、前日に作り置きしたおかずを活用することで、<b data-role="bring-attention">忙しい朝でもラクラク</b>です。盛り付けのコツもご紹介しますので、ぜひチェックしてくださいね。</p><h2 data-role="heading-primary">1. メインおかず：旨たれを使ったジューシーチキンステーキ</h2><figure data-role="image"><img src=https://newsatcl-pctr.c.yimg.jp/t/iwiz-yn/creators/ryoko/02000829/image-1773281544448.jpeg?fill=1&fc=fff&exp=10800 alt="チキンステーキを盛り付けた所"><figcaption>チキンステーキを盛り付けた所</figcaption></figure><p data-role="paragraph">このお弁当のメインは、ロピアの旨たれを使った簡単チキンステーキ。</p><h2 data-role="heading-primary">野菜をおいしく食べる旨たれ</h2><figure data-role="image"><img src=https://newsatcl-pctr.c.yimg.jp/t/iwiz-yn/creators/ryoko/02000829/image-1773281544599.jpeg?fill=1&fc=fff&exp=10800 alt="野菜をおいしく食べる旨たれ"><figcaption>野菜をおいしく食べる旨たれ</figcaption></figure><p data-role="paragraph">今回使ったのは、ロピアで販売されている『野菜をおいしく食べる旨たれ』です。ごまとにんにくが効いた甘めテイストのしょうゆたれで、子どもにも大好評でした。鶏肉にタレを絡めて焼くだけで、<b data-role="bring-attention">深いコクと甘辛い味付けが</b>楽しめます。時間がない朝は鶏肉を焼いてからたれをかけるだけでも<b data-role="bring-attention">深い味わい</b>が楽しめますよ。コレ1本でしっかり味が決まり、<b data-role="bring-attention">まるで手の込んだ料理のように</b>仕上がります。</p><h2 data-role="heading-primary">2. 作り置きで朝はラクラク</h2><figure data-role="image"><img src=https://newsatcl-pctr.c.yimg.jp/t/iwiz-yn/creators/ryoko/02000829/image-1773281544774.jpeg?fill=1&fc=fff&exp=10800 alt="ちくわの炒め物"><figcaption>ちくわの炒め物</figcaption></figure><p data-role="paragraph">副菜には、前日に準備したちくわの炒め物やオクラのごま和えを詰めます。冷蔵庫から取り出して電子レンジで加熱後に詰めるだけなので、朝の時間を大幅に短縮できますよ。</p><figure data-role="image"><img src=https://newsatcl-pctr.c.yimg.jp/t/iwiz-yn/creators/ryoko/02000829/image-1773281544920.jpeg?fill=1&fc=fff&exp=10800 alt="オクラのごま和え"><figcaption>オクラのごま和え</figcaption></figure><p data-role="paragraph">作り置きのポイントは、<b data-role="bring-attention">冷めてもおいしく食べられる味付け</b>にすること。少し濃いめの味付けや、ごまなど風味の強い食材を使うとおいしく仕上がりますよ。</p><h2 data-role="heading-primary">3. 彩りとバランスを考えた詰め方</h2><figure data-role="image"><img src=https://newsatcl-pctr.c.yimg.jp/t/iwiz-yn/creators/ryoko/02000829/image-1773281545077.jpeg?fill=1&fc=fff&exp=10800 alt="彩りを考えながら詰める"><figcaption>彩りを考えながら詰める</figcaption></figure><p data-role="paragraph">トマトやレタスなどの生野菜を加えることで、お弁当全体の彩りがパッと明るくなります。サッと洗ったら、キッチンペーパーでしっかりと水気を拭くのがポイントです。さらに、栄養バランスも考えて、主食・主菜・副菜をバランスよく配置することを心がけました。詰め方を工夫するだけで、おいしそうに見えるお弁当に仕上がりますよ。</p><figure data-role="youtube" id="T2uEPpTaA1s"><iframe width="100%" height="315" src="https://www.youtube.com/embed/T2uEPpTaA1s?rel=0&amp;wmode=transparent" frameborder="0" wmode="opaque" allowfullscreen></iframe></figure><p data-role="paragraph-empty"></p><h2 data-role="heading-primary">忙しい朝は無理せずに！</h2><figure data-role="image"><img src=https://newsatcl-pctr.c.yimg.jp/t/iwiz-yn/creators/ryoko/02000829/image-1773281545228.jpeg?fill=1&fc=fff&exp=10800 alt="簡単弁当"><figcaption>簡単弁当</figcaption></figure><p data-role="paragraph">今回ご紹介したのはロピアの旨たれですが、家にあるちょっと残ったドレッシングやタレなどでも簡単においしいステーキが作れます。ロピアの旨たれを活用した時短お弁当、冷めてもおいしいのでぜひ作ってみてください。毎日のお弁当づくりは無理しないことが1番！「忙しい朝」が少しでも楽になるヒントになりますように！</p><p data-role="paragraph">【合わせて読みたい記事はこちら】</p><p data-role="paragraph"><a data-role="anchor" href="https://news.yahoo.co.jp/expert/articles/fde98af4b3b8f39bc8cc54032b75e61a42779204" target="_blank" rel="nofollow noopener noreferrer">【お弁当】秋の味覚さつまいもで簡単おかず！冷めてもおいしい鶏肉の甘酢炒めの作り方とコツを紹介！</a></p><div data-role="panel"><p><a data-role="anchor" href="https://news.yahoo.co.jp/expert/creators/ryoko" target="_blank" rel="nofollow noopener noreferrer">プロフィールをフォロー</a>していただくと、毎日のお弁当や料理などのお役立ち情報のほか、おすすめのカフェや購入スイーツなどをお届けします！<a data-role="anchor" href="https://www.instagram.com/ryoko_stp/" target="_blank" rel="nofollow noopener noreferrer">Instagram</a>ではお弁当の詰め方やカフェ紹介をリールでご紹介しています。<a data-role="anchor" href="https://news.yahoo.co.jp/expert/creators/ryoko" target="_blank" rel="nofollow noopener noreferrer">プロフィール</a>からぜひチェックしてくださいね。</p></div></div></section></div><div style="height:56px"></div></article><div class="sc-d5870784-0 eExSln"><div><div class="sc-d5870784-1 gDNdsR"><div class="sc-d5870784-4 cxKTAH"><a href="https://news.yahoo.co.jp/users/expert/ryoko" data-cl-params="_cl_vmodule:expert_bottom;_cl_link:profimg;" class="sc-d5870784-5 hWwpNS"><picture><source type="image/webp" srcSet="https://news-pctr.c.yimg.jp/uUzvQ3lML_bkIqyakc1vFlHZ-lVjSCI3npXVYKE5vkgK0hRmLwT_-ubgwki4aCOCxmm9t7MBjkSwtlS-BtuAC_Ub7lExPAiRWBt7f9qW9F8zBB-Jmv66okFCQsO-c_sEX6A4MSOfB2Wlo0HIrKkK3wCj5AMi0EWByDh0D5SgwJ5X2Re-d6_cReEA0N5_A3t4"/><source type="image/jpeg" srcSet="https://news-pctr.c.yimg.jp/uUzvQ3lML_bkIqyakc1vFlHZ-lVjSCI3npXVYKE5vkgK0hRmLwT_-ubgwki4aCOCxmm9t7MBjkSwtlS-BtuAC_Ub7lExPAiRWBt7f9qW9F8zBB-Jmv66okFCQsO-c_sEX6A4MSOfB2Wlo0HIrKkK3_jvMpAkbUth_opJA91aI33E7cwddGc94rArWgppd2GA"/><img loading="lazy" 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{"userToken":{"value":"Tqe6agAAAAApCnvJL34xcl3OHnlLFoGbi1nR8IbP8HJGHD_RBL7zfo-GCXymTFEqorKMaluDxj0M--a2geWAepuiZ82BzuGJ"},"expertArticle":{"id":"0102000829","contentId":"fe01fd24b57e314780c78342d5c55b6b34c8c030","authorId":"ryoko","paid":false,"title":{"text":"【お弁当】忙しい朝でもラクラク！旨たれを使ったジューシーチキンステーキ\u0026盛り付けのコツ！","textInHashTag":[{"type":"text","text":"【お弁当】忙しい朝でもラクラク！旨たれを使ったジューシーチキンステーキ\u0026盛り付けのコツ！","titleLink":null}],"imageUrl":{"webp":"https://newsatcl-pctr.c.yimg.jp/t/iwiz-yn/creators/ryoko/02000829/title-1773107580378.jpeg?exp=10800\u0026fmt=webp","jpeg":"https://newsatcl-pctr.c.yimg.jp/t/iwiz-yn/creators/ryoko/02000829/title-1773107580378.jpeg?exp=10800","isDefault":false},"caption":"ロピアの旨たれでつくるお弁当"},"summary":"忙しい朝、時間がなくてお弁当作りがプレッシャーに感じることはありませんか？でも大丈夫！今回は、ロピアの「野菜をおいしく食べる旨たれ」を使って、時短に成功！さらに、前日に作り置きしたおかずを活用すること","body":"\u003cp data-role=\"paragraph\"\u003e忙しい朝、時間がなくてお弁当作りがプレッシャーに感じることはありませんか？でも大丈夫！今回は、\u003cb data-role=\"bring-attention\"\u003eロピアの「野菜をおいしく食べる旨たれ」\u003c/b\u003eを使って、時短に成功！さらに、前日に作り置きしたおかずを活用することで、\u003cb data-role=\"bring-attention\"\u003e忙しい朝でもラクラク\u003c/b\u003eです。盛り付けのコツもご紹介しますので、ぜひチェックしてくださいね。\u003c/p\u003e\u003ch2 data-role=\"heading-primary\"\u003e1. メインおかず：旨たれを使ったジューシーチキンステーキ\u003c/h2\u003e\u003cfigure data-role=\"image\"\u003e\u003cimg src=https://newsatcl-pctr.c.yimg.jp/t/iwiz-yn/creators/ryoko/02000829/image-1773281544448.jpeg?fill=1\u0026fc=fff\u0026exp=10800 alt=\"チキンステーキを盛り付けた所\"\u003e\u003cfigcaption\u003eチキンステーキを盛り付けた所\u003c/figcaption\u003e\u003c/figure\u003e\u003cp data-role=\"paragraph\"\u003eこのお弁当のメインは、ロピアの旨たれを使った簡単チキンステーキ。\u003c/p\u003e\u003ch2 data-role=\"heading-primary\"\u003e野菜をおいしく食べる旨たれ\u003c/h2\u003e\u003cfigure data-role=\"image\"\u003e\u003cimg src=https://newsatcl-pctr.c.yimg.jp/t/iwiz-yn/creators/ryoko/02000829/image-1773281544599.jpeg?fill=1\u0026fc=fff\u0026exp=10800 alt=\"野菜をおいしく食べる旨たれ\"\u003e\u003cfigcaption\u003e野菜をおいしく食べる旨たれ\u003c/figcaption\u003e\u003c/figure\u003e\u003cp data-role=\"paragraph\"\u003e今回使ったのは、ロピアで販売されている『野菜をおいしく食べる旨たれ』です。ごまとにんにくが効いた甘めテイストのしょうゆたれで、子どもにも大好評でした。鶏肉にタレを絡めて焼くだけで、\u003cb data-role=\"bring-attention\"\u003e深いコクと甘辛い味付けが\u003c/b\u003e楽しめます。時間がない朝は鶏肉を焼いてからたれをかけるだけでも\u003cb data-role=\"bring-attention\"\u003e深い味わい\u003c/b\u003eが楽しめますよ。コレ1本でしっかり味が決まり、\u003cb data-role=\"bring-attention\"\u003eまるで手の込んだ料理のように\u003c/b\u003e仕上がります。\u003c/p\u003e\u003ch2 data-role=\"heading-primary\"\u003e2. 作り置きで朝はラクラク\u003c/h2\u003e\u003cfigure data-role=\"image\"\u003e\u003cimg src=https://newsatcl-pctr.c.yimg.jp/t/iwiz-yn/creators/ryoko/02000829/image-1773281544774.jpeg?fill=1\u0026fc=fff\u0026exp=10800 alt=\"ちくわの炒め物\"\u003e\u003cfigcaption\u003eちくわの炒め物\u003c/figcaption\u003e\u003c/figure\u003e\u003cp data-role=\"paragraph\"\u003e副菜には、前日に準備したちくわの炒め物やオクラのごま和えを詰めます。冷蔵庫から取り出して電子レンジで加熱後に詰めるだけなので、朝の時間を大幅に短縮できますよ。\u003c/p\u003e\u003cfigure data-role=\"image\"\u003e\u003cimg src=https://newsatcl-pctr.c.yimg.jp/t/iwiz-yn/creators/ryoko/02000829/image-1773281544920.jpeg?fill=1\u0026fc=fff\u0026exp=10800 alt=\"オクラのごま和え\"\u003e\u003cfigcaption\u003eオクラのごま和え\u003c/figcaption\u003e\u003c/figure\u003e\u003cp data-role=\"paragraph\"\u003e作り置きのポイントは、\u003cb data-role=\"bring-attention\"\u003e冷めてもおいしく食べられる味付け\u003c/b\u003eにすること。少し濃いめの味付けや、ごまなど風味の強い食材を使うとおいしく仕上がりますよ。\u003c/p\u003e\u003ch2 data-role=\"heading-primary\"\u003e3. 彩りとバランスを考えた詰め方\u003c/h2\u003e\u003cfigure data-role=\"image\"\u003e\u003cimg src=https://newsatcl-pctr.c.yimg.jp/t/iwiz-yn/creators/ryoko/02000829/image-1773281545077.jpeg?fill=1\u0026fc=fff\u0026exp=10800 alt=\"彩りを考えながら詰める\"\u003e\u003cfigcaption\u003e彩りを考えながら詰める\u003c/figcaption\u003e\u003c/figure\u003e\u003cp data-role=\"paragraph\"\u003eトマトやレタスなどの生野菜を加えることで、お弁当全体の彩りがパッと明るくなります。サッと洗ったら、キッチンペーパーでしっかりと水気を拭くのがポイントです。さらに、栄養バランスも考えて、主食・主菜・副菜をバランスよく配置することを心がけました。詰め方を工夫するだけで、おいしそうに見えるお弁当に仕上がりますよ。\u003c/p\u003e\u003cfigure data-role=\"youtube\" id=\"T2uEPpTaA1s\"\u003e\u003ciframe width=\"100%\" height=\"315\" src=\"https://www.youtube.com/embed/T2uEPpTaA1s?rel=0\u0026amp;wmode=transparent\" frameborder=\"0\" wmode=\"opaque\" allowfullscreen\u003e\u003c/iframe\u003e\u003c/figure\u003e\u003cp data-role=\"paragraph-empty\"\u003e\u003c/p\u003e\u003ch2 data-role=\"heading-primary\"\u003e忙しい朝は無理せずに！\u003c/h2\u003e\u003cfigure data-role=\"image\"\u003e\u003cimg src=https://newsatcl-pctr.c.yimg.jp/t/iwiz-yn/creators/ryoko/02000829/image-1773281545228.jpeg?fill=1\u0026fc=fff\u0026exp=10800 alt=\"簡単弁当\"\u003e\u003cfigcaption\u003e簡単弁当\u003c/figcaption\u003e\u003c/figure\u003e\u003cp data-role=\"paragraph\"\u003e今回ご紹介したのはロピアの旨たれですが、家にあるちょっと残ったドレッシングやタレなどでも簡単においしいステーキが作れます。ロピアの旨たれを活用した時短お弁当、冷めてもおいしいのでぜひ作ってみてください。毎日のお弁当づくりは無理しないことが1番！「忙しい朝」が少しでも楽になるヒントになりますように！\u003c/p\u003e\u003cp data-role=\"paragraph\"\u003e【合わせて読みたい記事はこちら】\u003c/p\u003e\u003cp data-role=\"paragraph\"\u003e\u003ca data-role=\"anchor\" href=\"https://news.yahoo.co.jp/expert/articles/fde98af4b3b8f39bc8cc54032b75e61a42779204\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003e【お弁当】秋の味覚さつまいもで簡単おかず！冷めてもおいしい鶏肉の甘酢炒めの作り方とコツを紹介！\u003c/a\u003e\u003c/p\u003e\u003cdiv data-role=\"panel\"\u003e\u003cp\u003e\u003ca data-role=\"anchor\" href=\"https://news.yahoo.co.jp/expert/creators/ryoko\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003eプロフィールをフォロー\u003c/a\u003eしていただくと、毎日のお弁当や料理などのお役立ち情報のほか、おすすめのカフェや購入スイーツなどをお届けします！\u003ca data-role=\"anchor\" href=\"https://www.instagram.com/ryoko_stp/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003eInstagram\u003c/a\u003eではお弁当の詰め方やカフェ紹介をリールでご紹介しています。\u003ca data-role=\"anchor\" href=\"https://news.yahoo.co.jp/expert/creators/ryoko\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003eプロフィール\u003c/a\u003eからぜひチェックしてくださいね。\u003c/p\u003e\u003c/div\u003e","link":"https://news.yahoo.co.jp/expert/articles/fe01fd24b57e314780c78342d5c55b6b34c8c030","publishedAt":{"date":"2024/11/25(月)","time":"7:01"},"comment":{"permission":false,"isTerminated":false}},"expertAuthorForArticleDetail":{"id":"ryoko","ppid":"QWA_6og0X6vNPAg8ZZ6Prk6vdnufbhXRk0tCrWfJimFBLrGu00","name":"ryoko","role":"creator","profileUrl":"https://news.yahoo.co.jp/users/expert/ryoko","profileImageUrl":{"webp":"https://news-pctr.c.yimg.jp/uUzvQ3lML_bkIqyakc1vFlHZ-lVjSCI3npXVYKE5vkgK0hRmLwT_-ubgwki4aCOCxmm9t7MBjkSwtlS-BtuAC_Ub7lExPAiRWBt7f9qW9F8zBB-Jmv66okFCQsO-c_sEX6A4MSOfB2Wlo0HIrKkK3wCj5AMi0EWByDh0D5SgwJ5X2Re-d6_cReEA0N5_A3t4","jpeg":"https://news-pctr.c.yimg.jp/uUzvQ3lML_bkIqyakc1vFlHZ-lVjSCI3npXVYKE5vkgK0hRmLwT_-ubgwki4aCOCxmm9t7MBjkSwtlS-BtuAC_Ub7lExPAiRWBt7f9qW9F8zBB-Jmv66okFCQsO-c_sEX6A4MSOfB2Wlo0HIrKkK3_jvMpAkbUth_opJA91aI33E7cwddGc94rArWgppd2GA"},"title":"カフェ・スイーツ・グルメライター","description":"横浜・鎌倉を中心に、カフェやレストラン、人気のアフタヌーンティーやテイクアウトで楽しめるスイーツなど、“日常にときめきを添えてくれる場所とモノ”を発信中。駅近の話題店から、ふらりと立ち寄りたくなる静かな隠れ家まで。話題性だけでなく、居心地や満足感まで大切にしながら、“また行きたい”と思えるお店をご紹介しています。夫と子ども2人の4人家族。子育てが少し落ち着き、「私も何か始めたい！」そう思ったことをきっかけに発信をスタート。現在は年間360本以上の記事を執筆。読んでくださる方の日常がふっと軽くなるような、小さな癒しと“ごほうび時間”をお届けします。","publicArticleCount":763,"bestExpertAwards":[],"isExpertViewingOwnArticle":false},"articleReaction":{"articleReactions":{"clear":2,"newpov":2,"info":1},"serviceId":"expert","categoryId":"life","isArticleReactionError":false},"expertRecentArticleList":{"list":[{"contentId":"a1e203fe6ad41fbf46fed38e9011b4f1f803f9f8","title":"隠れ名店を見つけた気分！お肉屋さんのようなサラダと絶品メニューに感動！話題の洋食ランチをレポ！","link":"https://news.yahoo.co.jp/expert/articles/a1e203fe6ad41fbf46fed38e9011b4f1f803f9f8","thumbnails":[{"aspectRatio":"6:5","longSize":192,"webpUrl":"https://newsatcl-pctr.c.yimg.jp/t/iwiz-yn/creators/ryoko/02730686/title-1790576292864.jpeg?pri=l\u0026w=192\u0026h=160\u0026order=c2r\u0026cx=312\u0026cy=0\u0026cw=1296\u0026ch=1080\u0026exp=10800\u0026fmt=webp","jpegUrl":"https://newsatcl-pctr.c.yimg.jp/t/iwiz-yn/creators/ryoko/02730686/title-1790576292864.jpeg?pri=l\u0026w=192\u0026h=160\u0026order=c2r\u0026cx=312\u0026cy=0\u0026cw=1296\u0026ch=1080\u0026exp=10800"},{"aspectRatio":"original","longSize":192,"webpUrl":"https://newsatcl-pctr.c.yimg.jp/t/iwiz-yn/creators/ryoko/02730686/title-1790576292864.jpeg?pri=l\u0026w=192\u0026h=108\u0026exp=10800\u0026fmt=webp","jpegUrl":"https://newsatcl-pctr.c.yimg.jp/t/iwiz-yn/creators/ryoko/02730686/title-1790576292864.jpeg?pri=l\u0026w=192\u0026h=108\u0026exp=10800"}],"isPay":false,"authorId":"ryoko","authorName":"ryoko","authorTitle":null,"authorThumbUrl":null,"createTime":{"date":"9/28(月)","time":"15:21"},"isCommentCountFetching":false},{"contentId":"58c11a591b9b1e98b47db8c4b7dfa2e8dd55d0a0","title":"【2026最新版横浜カフェ】友達と行きたい！ランチもスイーツも楽しめるおしゃれカフェ3選！","link":"https://news.yahoo.co.jp/expert/articles/58c11a591b9b1e98b47db8c4b7dfa2e8dd55d0a0","thumbnails":[{"aspectRatio":"6:5","longSize":192,"webpUrl":"https://newsatcl-pctr.c.yimg.jp/t/iwiz-yn/creators/ryoko/02729837/title-1790481567076.jpeg?pri=l\u0026w=192\u0026h=160\u0026order=c2r\u0026cx=312\u0026cy=0\u0026cw=1296\u0026ch=1080\u0026exp=10800\u0026fmt=webp","jpegUrl":"https://newsatcl-pctr.c.yimg.jp/t/iwiz-yn/creators/ryoko/02729837/title-1790481567076.jpeg?pri=l\u0026w=192\u0026h=160\u0026order=c2r\u0026cx=312\u0026cy=0\u0026cw=1296\u0026ch=1080\u0026exp=10800"},{"aspectRatio":"original","longSize":192,"webpUrl":"https://newsatcl-pctr.c.yimg.jp/t/iwiz-yn/creators/ryoko/02729837/title-1790481567076.jpeg?pri=l\u0026w=192\u0026h=108\u0026exp=10800\u0026fmt=webp","jpegUrl":"https://newsatcl-pctr.c.yimg.jp/t/iwiz-yn/creators/ryoko/02729837/title-1790481567076.jpeg?pri=l\u0026w=192\u0026h=108\u0026exp=10800"}],"isPay":false,"authorId":"ryoko","authorName":"ryoko","authorTitle":null,"authorThumbUrl":null,"createTime":{"date":"9/27(日)","time":"13:09"},"isCommentCountFetching":false},{"contentId":"e3efc292512008ddd0d61a2373653b24dee32cf9","title":"鎌倉・長谷の古民家そば処で昼懐石！庭を眺めながら味わう「さくら」","link":"https://news.yahoo.co.jp/expert/articles/e3efc292512008ddd0d61a2373653b24dee32cf9","thumbnails":[{"aspectRatio":"6:5","longSize":192,"webpUrl":"https://newsatcl-pctr.c.yimg.jp/t/iwiz-yn/creators/ryoko/02729180/title-1790403033070.jpeg?pri=l\u0026w=192\u0026h=160\u0026order=c2r\u0026cx=272\u0026cy=0\u0026cw=1128\u0026ch=940\u0026exp=10800\u0026fmt=webp","jpegUrl":"https://newsatcl-pctr.c.yimg.jp/t/iwiz-yn/creators/ryoko/02729180/title-1790403033070.jpeg?pri=l\u0026w=192\u0026h=160\u0026order=c2r\u0026cx=272\u0026cy=0\u0026cw=1128\u0026ch=940\u0026exp=10800"},{"aspectRatio":"original","longSize":192,"webpUrl":"https://newsatcl-pctr.c.yimg.jp/t/iwiz-yn/creators/ryoko/02729180/title-1790403033070.jpeg?pri=l\u0026w=192\u0026h=107\u0026exp=10800\u0026fmt=webp","jpegUrl":"https://newsatcl-pctr.c.yimg.jp/t/iwiz-yn/creators/ryoko/02729180/title-1790403033070.jpeg?pri=l\u0026w=192\u0026h=107\u0026exp=10800"}],"isPay":false,"author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diff --git a/recognization/data/external-eval/other-mayo/eval_result.json b/recognization/data/external-eval/other-mayo/eval_result.json
deleted file mode 100644
index 075f01f..0000000
--- a/recognization/data/external-eval/other-mayo/eval_result.json
+++ /dev/null
@@ -1,101 +0,0 @@
-{
- "dataset": "other-mayo",
- "catalog": "stock-lopia-clean",
- "note": "External proxy images, not Tier 2. Scored through the shipped LocalLaneAdapter + DecisionPolicy.",
- "rows": [
-  {
-   "file": "sku_l04_1.jpg",
-   "truth": "sku_l04",
-   "best": null,
-   "confidence": 0.0,
-   "status": "rejected",
-   "source": null,
-   "verdict": "abstain"
-  },
-  {
-   "file": "sku_l04_2.jpg",
-   "truth": "sku_l04",
-   "best": null,
-   "confidence": 0.0,
-   "status": "unknown",
-   "source": null,
-   "verdict": "abstain"
-  },
-  {
-   "file": "sku_l04_3.jpg",
-   "truth": "sku_l04",
-   "best": null,
-   "confidence": 0.3294,
-   "status": "ambiguous",
-   "source": null,
-   "verdict": "abstain"
-  },
-  {
-   "file": "sku_l04_4.jpg",
-   "truth": "sku_l04",
-   "best": null,
-   "confidence": -1.6712,
-   "status": "unknown",
-   "source": null,
-   "verdict": "abstain"
-  },
-  {
-   "file": "sku_l07_1.jpg",
-   "truth": "sku_l07",
-   "best": "sku_l07",
-   "confidence": 0.0,
-   "status": "success",
-   "source": "text",
-   "verdict": "correct"
-  },
-  {
-   "file": "sku_l07_2.jpg",
-   "truth": "sku_l07",
-   "best": "sku_l07",
-   "confidence": -0.5503,
-   "status": "success",
-   "source": "text",
-   "verdict": "correct"
-  },
-  {
-   "file": "sku_l09_1.jpg",
-   "truth": "sku_l09",
-   "best": "sku_l09",
-   "confidence": 27.9546,
-   "status": "success",
-   "source": "local",
-   "verdict": "correct"
-  },
-  {
-   "file": "sku_l09_2.jpg",
-   "truth": "sku_l09",
-   "best": "sku_l09",
-   "confidence": 0.975,
-   "status": "success",
-   "source": "text",
-   "verdict": "correct"
-  }
- ],
- "scored": 8,
- "leaked": 0,
- "by_sku": {
-  "sku_l04": {
-   "correct": 0,
-   "abstain": 4,
-   "wrong": 0
-  },
-  "sku_l07": {
-   "correct": 2,
-   "abstain": 0,
-   "wrong": 0
-  },
-  "sku_l09": {
-   "correct": 2,
-   "abstain": 0,
-   "wrong": 0
-  }
- },
- "correct": 4,
- "abstain": 4,
- "wrong": 0
-}
\ No newline at end of file
diff --git a/recognization/data/external-eval/other-mayo/manifest.json b/recognization/data/external-eval/other-mayo/manifest.json
deleted file mode 100644
index 9a09962..0000000
--- a/recognization/data/external-eval/other-mayo/manifest.json
+++ /dev/null
@@ -1,181 +0,0 @@
-{
-  "dataset_type": "external_proxy",
-  "overall_note": "Independent-source Internet media for recognition stress testing. These files are external_proxy evidence only: they are not held-out store-visit Tier 2 data and do not satisfy the DECISION.md Tier-2 capture protocol. Exact SKU, product name, package size, and visible label details were checked before acceptance; mismatched variants and sizes were rejected.",
-  "license_note": "All accepted media comes from commercial retailer or purchase-verified review/marketplace pages with no reuse grant. Treat every file as restricted-research: internal evaluation only, not redistributable.",
-  "identity_research": {
-    "sku_l04": {
-      "csv_name_status": "correct",
-      "catalog_name": "やまや めんたいマヨネーズタイプ 200g",
-      "printed_front_text": "博多の味 やまや / 明太子屋さんがつくった / めんたいマヨネーズタイプ / 200g",
-      "jan": "4903085063846",
-      "seller_and_package_brand": "株式会社やまやコミュニケーションズ / やまや",
-      "manufacturing_site": "エスエスケイフーズ株式会社 焼津工場",
-      "conclusion": "The package does not say 明太手屋さん. Independent front captures and retailer copy read 明太子屋さんがつくった, which is descriptive copy ('made by a mentaiko shop'), not a separate package brand. やまや is both the front-facing package brand and the seller, 株式会社やまやコミュニケーションズ; SSK Foods is the manufacturing plant. The CSV row is therefore not wrong or merely naming an unrelated distributor.",
-      "sources": [
-        {
-          "source_page": "https://www.amicashop.com/products/detail/21097",
-          "evidence": "Retailer title やまや AD明太マヨネーズタイプ 200g; table records JAN 4903085063846, メーカー やまや, and 200g."
-        },
-        {
-          "source_page": "https://monolog.r-n-i.jp/item/4903085063846",
-          "evidence": "Purchase-verified review page identifies やまやコミュニケーションズ, product やまや 明太マヨネーズタイプ 200g, and JAN 4903085063846."
-        },
-        {
-          "source_page": "https://paypayfleamarket.yahoo.co.jp/item/z669125224",
-          "evidence": "Customer rear-label photos visibly show 内容量 200g, 販売者 株式会社やまやコミュニケーションズ, 製造所 エスエスケイフーズ株式会社, and barcode 4903085063846."
-        }
-      ]
-    },
-    "sku_l08": {
-      "status": "no exact media accepted",
-      "catalog_name": "スパイシーストーム ペリペリミックス 30g",
-      "reason": "Public results consistently resolve to 株式会社スパイスドリーム / SPICE DREAM ペリペリミックス 50g, not スパイシーストーム 30g. The official launch release states 50g and the current retailer page states 50g with JAN 4582433046269. All 50g images were rejected as wrong size/name; the 30g identity remains unresolved.",
-      "sources": [
-        "https://prtimes.jp/main/html/rd/p/000000005.000048956.html",
-        "https://www.grand-food-hall.com/store/item/detail/1_1_4582433046269_1/1"
-      ]
-    },
-    "sku_l10": {
-      "status": "no exact media accepted",
-      "catalog_name": "Ajinomoto ピュアセレクト マヨネーズ 450g",
-      "reason": "Ajinomoto's current official lineup is 400g, 250g, 600g, and 1kg; no exact 450g Ajinomoto product was verified. Search results for 450g were other brands or current 400g Pure Select, so every candidate was rejected rather than relabeled.",
-      "source": "https://www.ajinomoto.co.jp/products/detail/?ProductName=pureselect_1"
-    }
-  },
-  "coverage": {
-    "accepted_images": 8,
-    "accepted_skus": [
-      "sku_l04",
-      "sku_l07",
-      "sku_l09"
-    ],
-    "two_or_more_images": [
-      "sku_l04",
-      "sku_l07",
-      "sku_l09"
-    ],
-    "two_or_more_source_domains": [
-      "sku_l04",
-      "sku_l07",
-      "sku_l09"
-    ],
-    "no_exact_media": [
-      "sku_l08",
-      "sku_l10"
-    ]
-  },
-  "images": [
-    {
-      "file": "sku_l04_1.jpg",
-      "sku": "sku_l04",
-      "product": "やまや めんたいマヨネーズタイプ 200g",
-      "source_page": "https://monolog.r-n-i.jp/item/4903085063846",
-      "media_url": "https://monolog.r-n-i.jp/2026_06_01_12_31_00/3531222-98f74e77-ff09-48c8-aab6-9f2ee2126c06.jpg?width=500",
-      "source_title": "やまや 明太マヨネーズタイプ 200g（やまやコミュニケーションズ）の口コミ・レビュー・評判、評価点数 | ものログ",
-      "source_domain": "monolog.r-n-i.jp",
-      "width": 160,
-      "height": 213,
-      "view_type": "purchase_verified_customer_front",
-      "license_status": "restricted-research",
-      "notes": "Independent customer capture from a receipt/barcode-verified review page; exact 200g pink pouch and printed やまや/めんたいマヨネーズタイプ design are visible. Not the Amazon enrollment packshot. Commercial/user photo with no redistribution grant."
-    },
-    {
-      "file": "sku_l04_2.jpg",
-      "sku": "sku_l04",
-      "product": "やまや めんたいマヨネーズタイプ 200g",
-      "source_page": "https://monolog.r-n-i.jp/item/4903085063846",
-      "media_url": "https://monolog.r-n-i.jp/2026_05_28_09_32_41/2925611-26cd9c39-ccb7-449c-8023-3e20156136bf.jpg?width=500",
-      "source_title": "やまや 明太マヨネーズタイプ 200g（やまやコミュニケーションズ）の口コミ・レビュー・評判、評価点数 | ものログ",
-      "source_domain": "monolog.r-n-i.jp",
-      "width": 160,
-      "height": 213,
-      "view_type": "purchase_verified_customer_front_alternate",
-      "license_status": "restricted-research",
-      "notes": "Second independent customer capture on a different indoor background; exact 200g pink pouch and product title are visible. Not an official enrollment packshot. Commercial/user photo with no redistribution grant."
-    },
-    {
-      "file": "sku_l04_3.jpg",
-      "sku": "sku_l04",
-      "product": "やまや めんたいマヨネーズタイプ 200g",
-      "source_page": "https://paypayfleamarket.yahoo.co.jp/item/z669125224",
-      "media_url": "https://auctions.c.yimg.jp/images.auctions.yahoo.co.jp/image/dr000/auc0208/users/3db602d76d1d11cc3d78798d3398ab3e4d61507b/i-img1200x1200-1787696962831yw9b1t.jpg",
-      "source_title": "やまや めんたいマヨネーズタイプ 200g めんたい マヨネーズ 明太 | Yahoo!フリマ",
-      "source_domain": "paypayfleamarket.yahoo.co.jp",
-      "width": 1200,
-      "height": 1200,
-      "view_type": "customer_back_label",
-      "license_status": "restricted-research",
-      "notes": "High-resolution back capture. Visibly confirms 内容量 200g, 販売者 株式会社やまやコミュニケーションズ, and 製造所 エスエスケイフーズ株式会社. The listing explicitly prohibits unauthorized image reuse, so retain only for internal research."
-    },
-    {
-      "file": "sku_l04_4.jpg",
-      "sku": "sku_l04",
-      "product": "やまや めんたいマヨネーズタイプ 200g",
-      "source_page": "https://paypayfleamarket.yahoo.co.jp/item/z669125224",
-      "media_url": "https://auctions.c.yimg.jp/images.auctions.yahoo.co.jp/image/dr000/auc0208/users/3db602d76d1d11cc3d78798d3398ab3e4d61507b/i-img581x1200-1787696962831db2pox.jpg",
-      "source_title": "やまや めんたいマヨネーズタイプ 200g めんたい マヨネーズ 明太 | Yahoo!フリマ",
-      "source_domain": "paypayfleamarket.yahoo.co.jp",
-      "width": 581,
-      "height": 1200,
-      "view_type": "customer_back_barcode",
-      "license_status": "restricted-research",
-      "notes": "Alternate high-resolution back capture; the barcode visibly reads 4903085063846 and the 200g/seller/manufacturing details match sku_l04. The listing explicitly prohibits unauthorized image reuse, so retain only for internal research."
-    },
-    {
-      "file": "sku_l07_1.jpg",
-      "sku": "sku_l07",
-      "product": "丸和油脂 ラッキーマヨネーズ 200g",
-      "source_page": "https://monolog.r-n-i.jp/item/4978931401615",
-      "media_url": "https://monolog.r-n-i.jp/2026_08_31_14_20_14/2019791-677ce50f-7520-468c-a22a-441600df5345.jpg?width=500",
-      "source_title": "丸和油脂 ラッキーマヨネーズ チューブ 200g（丸和油脂）の口コミ・レビュー・評判、評価点数 | ものログ",
-      "source_domain": "monolog.r-n-i.jp",
-      "width": 160,
-      "height": 213,
-      "view_type": "purchase_verified_customer_front",
-      "license_status": "restricted-research",
-      "notes": "Independent purchase-verified customer capture; the yellow chick design and 200g pack match JAN 4978931401615. Not the Amazon enrollment packshot. Commercial/user photo with no redistribution grant."
-    },
-    {
-      "file": "sku_l07_2.jpg",
-      "sku": "sku_l07",
-      "product": "丸和油脂 ラッキーマヨネーズ 200g",
-      "source_page": "https://ec.aeon-hokkaido.jp/rakutaku/sp/item/detail.aspx?tcd=0413&no=4978931401615",
-      "media_url": "https://stprodaeonblob.blob.core.windows.net/rakutaku-item/ItemImage/4978931401615_2.jpg",
-      "source_title": "丸和油脂 ラッキーマヨネーズ ２００ｇ | イオン北海道のネットスーパー",
-      "source_domain": "ec.aeon-hokkaido.jp",
-      "width": 256,
-      "height": 396,
-      "view_type": "retailer_back_label",
-      "license_status": "restricted-research",
-      "notes": "Retailer alternate rear view for exact JAN 4978931401615; visibly different from the enrolled Amazon front. Commercial retailer image with no redistribution grant."
-    },
-    {
-      "file": "sku_l09_1.jpg",
-      "sku": "sku_l09",
-      "product": "Ajinomoto ピュアセレクト コクうま カロリーカット65% 360g",
-      "source_page": "https://monolog.r-n-i.jp/item/4901001198429",
-      "media_url": "https://monolog.r-n-i.jp/2026_09_23_02_24_05/3281706-03327ef1-683a-4f3f-831a-cbfaccbbc51a.jpg?width=500",
-      "source_title": "ピュアセレクトコクうま65%カロリーカット360g（味の素）の口コミ・レビュー・評判、評価点数 | ものログ",
-      "source_domain": "monolog.r-n-i.jp",
-      "width": 160,
-      "height": 213,
-      "view_type": "purchase_verified_customer_front_current_package",
-      "license_status": "restricted-research",
-      "notes": "Independent purchase-verified customer capture; current black/red 360g package is visible. Exact JAN 4901001198429 is stated by the source page. Not the official Ajinomoto enrollment packshot. Commercial/user photo with no redistribution grant."
-    },
-    {
-      "file": "sku_l09_2.jpg",
-      "sku": "sku_l09",
-      "product": "Ajinomoto ピュアセレクト コクうま カロリーカット65% 360g",
-      "source_page": "https://store.shopping.yahoo.co.jp/beisia/4901001198429.html",
-      "media_url": "https://item-shopping.c.yimg.jp/i/n/beisia_4901001198429_1_d_20250220115842",
-      "source_title": "味の素 ピュアセレクトコクうまカロリー65%カット 360g×24個 | ベイシア ヤフーショップ",
-      "source_domain": "store.shopping.yahoo.co.jp",
-      "width": 800,
-      "height": 800,
-      "view_type": "retailer_back_label_current_package",
-      "license_status": "restricted-research",
-      "notes": "High-resolution retailer alternate image showing the exact current 360g package rear label; the page states JAN 4901001198429. Commercial retailer image with no redistribution grant."
-    }
-  ]
-}
diff --git a/recognization/data/external-eval/other-mayo/sku_l04_1.jpg b/recognization/data/external-eval/other-mayo/sku_l04_1.jpg
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diff --git a/recognization/data/external-eval/tier3-sources/eval_result.json b/recognization/data/external-eval/tier3-sources/eval_result.json
deleted file mode 100644
index 14e43d9..0000000
--- a/recognization/data/external-eval/tier3-sources/eval_result.json
+++ /dev/null
@@ -1,46 +0,0 @@
-{
- "dataset": "tier3-sources",
- "tier": "tier3_proxy_candidate",
- "note": "Not Tier 3: one frame per second was extracted from two reusable public clips and run through the production detector plus LocalLaneAdapter. No per-facing SKU ground truth exists, so this is an open-world accept-rate probe, not an accuracy score.",
- "method": {
-  "frame_sample_hz": 1,
-  "frame_long_side": 1280,
-  "detector": "production detector",
-  "identity": "stock-lopia-clean production local lane"
- },
- "clips": [
-  {
-   "file": "media/hanzomon-supermarket-pan-132-152.mp4",
-   "frames": 20,
-   "detected_crops": 227,
-   "accepted": 0,
-   "accept_rate": 0.0,
-   "interpretation": "Correct open-set behaviour. Pixel inspection of frame 10 shows German Kuhne pickle jars, not catalog products."
-  },
-  {
-   "file": "media/kat-in-japan-mayonnaise-aisle-pan-694-720.mp4",
-   "frames": 26,
-   "detected_crops": 1623,
-   "accepted": 24,
-   "accept_rate": 0.0148,
-   "accepted_by_sku": {
-    "sku_l05": 13,
-    "sku_l03": 4,
-    "sku_l04": 2,
-    "sku_l08": 2,
-    "sku_l01": 1,
-    "sku_l09": 1,
-    "sku_l10": 1
-   },
-   "interpretation": "The clip visibly contains Kewpie and adjacent mayonnaise products, so accepted crops are not automatically false accepts. At 640x360 the labels are too soft for exact per-SKU ground truth. The 1.5% accept rate is therefore a triage signal only; Tier 3 still needs an operator-recorded walk with labels."
-  }
- ],
- "summary": {
-  "frames": 46,
-  "detected_crops": 1850,
-  "accepted": 24,
-  "accept_rate": 0.013,
-  "false_accepts": null,
-  "accuracy": null
- }
-}
diff --git a/recognization/data/external-eval/tier3-sources/manifest.json b/recognization/data/external-eval/tier3-sources/manifest.json
deleted file mode 100644
index ca00563..0000000
--- a/recognization/data/external-eval/tier3-sources/manifest.json
+++ /dev/null
@@ -1,289 +0,0 @@
-{
-  "directory": "data/external-eval/tier3-sources",
-  "purpose": "Public video/image-sequence sources that can exercise the live recognition path (continuous camera pan over Japanese supermarket mayonnaise/sauce aisles).",
-  "tier_label": "tier3_proxy_candidate",
-  "tier_label_note": "Nothing here is Tier 3. DECISION.md defines Tier 3 as FrameRecorder walks (code/record.py) of the same LOPIA aisles with Tier-2-style labels, replayed through code/eval_live.py. These are independent-source proxies: continuous camera pans over comparable Japanese retail shelving, not the Tier-2/Tier-3 protocol.",
-  "catalog_reference": "stock-lopia/stock.csv (12 SKUs, sku_l01..sku_l12)",
-  "collected": "2026-09-29",
-  "collector": "Tier3Sources subagent",
-  "media": [
-    {
-      "file": "media/kat-in-japan-mayonnaise-aisle-pan-694-720.mp4",
-      "sku": null,
-      "sku_candidates": [
-        "sku_l01"
-      ],
-      "sku_confidence": "unconfirmed_visual_resemblance_only",
-      "product": "Japanese supermarket mayonnaise/dressing shelf bay: a dense run of squeeze bottles and jars in the standard Japanese mayonnaise-shelf format. Brand identity is NOT legible at 640x360, so no Kewpie/Ajinomoto attribution is claimed.",
-      "source_page": "https://www.youtube.com/watch?v=HM2vrp7aE04",
-      "source_title": "Japanese Supermarket--Tour--Living In Japan--Grocery Shopping In Japan--Preparing for Thanksgiving",
-      "source_author": "Kat In Japan",
-      "media_url": "https://www.youtube.com/watch?v=HM2vrp7aE04",
-      "source_license": "Creative Commons Attribution (reuse allowed)",
-      "license_url": "https://www.youtube.com/t/creative_commons",
-      "license_status": "redistributable",
-      "license_evidence": "yt-dlp metadata field license = 'Creative Commons Attribution license (reuse allowed)'",
-      "width": 640,
-      "height": 360,
-      "duration_seconds": 26.0,
-      "useful_timestamps": [
-        {
-          "t": 0.0,
-          "note": "Shelf pan begins; the mayonnaise/dressing bay enters frame on the left-hand side."
-        },
-        {
-          "t": 9.0,
-          "note": "Closest approach to the bay; bottle bodies largest in frame. A white-bodied squeeze bottle with a red cap and a red/yellow label block is present and resembles the enrolled sku_l01 (Kewpie Egg Care 305g) silhouette, but the label text is not resolvable, so this is a resemblance note only."
-        },
-        {
-          "t": 15.0,
-          "note": "Camera continues along the same bay; a second row of bottles plus adjacent dressing bottles."
-        },
-        {
-          "t": 21.0,
-          "note": "Pan reaches the end of the mayonnaise bay before moving on."
-        }
-      ],
-      "view_type": "shelf_pan_left_bay",
-      "camera_motion": "continuous handheld pan along the shelf, no cuts inside the clip",
-      "trim": "source 00:11:34-00:12:00 (694-720 s) of a 1254.6 s upload",
-      "sha256": "6fb69daee59d17fb245a86816315bd7667f608f346754aa47591a3959df25cf5",
-      "notes": "Only 360p was obtainable: the 1080p rendition of this upload is behind a YouTube bot gate (yt-dlp 'Sign in to confirm you're not a bot'), so the saved clip is a re-encode of the 360p stream. VERIFIED in the saved pixels: a continuous, uncut camera pan along a Japanese mayonnaise/dressing shelf bay, with the shelf format, bottle geometry and shelf-tag layout of the target category. NOT VERIFIED: brand and SKU identity. Tight 4x crops of the bottle faces at 9 s and 15 s do not resolve label text, so no Kewpie/Ajinomoto claim is made and no catalog SKU is asserted. Treat this as a motion/pan proxy for the live path, not a SKU-identity proxy. Frames inspected at 3 s, 9 s, 15 s, 21 s, 25 s (left-bay 3x zoom crops), 9 s and 15 s (4x tight bottle crops), and 2 s, 8 s, 14 s, 20 s, 25 s (full frame)."
-    },
-    {
-      "file": "media/hanzomon-supermarket-pan-132-152.mp4",
-      "sku": null,
-      "sku_candidates": [],
-      "sku_confidence": "no_catalog_sku_confirmed",
-      "product": "Japanese supermarket interior: general grocery and condiment shelving, no catalog SKU confirmed",
-      "source_page": "https://commons.wikimedia.org/wiki/File:Daily_Life_in_Tokyo_in_2013_%E2%80%93_Supermarket_in_Hanzomon_Station_Area.webm",
-      "source_title": "Daily Life in Tokyo in 2013 - Supermarket in Hanzomon Station Area",
-      "source_author": "Arkascha",
-      "media_url": "https://upload.wikimedia.org/wikipedia/commons/2/24/Daily_Life_in_Tokyo_in_2013_%E2%80%93_Supermarket_in_Hanzomon_Station_Area.webm",
-      "source_license": "CC BY 3.0",
-      "license_url": "https://creativecommons.org/licenses/by/3.0",
-      "license_status": "redistributable",
-      "license_evidence": "Wikimedia Commons imageinfo extmetadata: LicenseShortName 'CC BY 3.0', LicenseUrl https://creativecommons.org/licenses/by/3.0, AttributionRequired true, DateTimeOriginal 2013-07-30",
-      "width": 1920,
-      "height": 1080,
-      "duration_seconds": 20.02,
-      "useful_timestamps": [
-        {
-          "t": 1.0,
-          "note": "Camera moving along shelving; packaged goods fill the frame at full 1080p detail."
-        },
-        {
-          "t": 9.0,
-          "note": "Mid-aisle shelving with legible package text at native resolution."
-        },
-        {
-          "t": 17.0,
-          "note": "Pan continues past the shelf run; store lighting and shelf geometry stable."
-        }
-      ],
-      "view_type": "aisle_pan",
-      "camera_motion": "continuous handheld walk-and-pan, no cuts inside the clip",
-      "trim": "source 00:02:12-00:02:32 (132-152 s) of a 195.8 s upload",
-      "sha256": "31edbd020bc1119ba38cf2a624ff3ed129f0ac6844da3701643dc0c4693b6f57",
-      "notes": "Highest-resolution reusable source found (native 1920x1080, CC BY 3.0, Wikimedia Commons). Continuous pan and enough resolution to detect products, but no catalog SKU was confirmed in the inspected frames, so it is a resolution/motion proxy rather than a SKU-identity proxy. Frames inspected at 1 s, 9 s, 17 s (full frame) plus a 25-tile contact sheet over the whole 195.8 s upload."
-    }
-  ],
-  "rejected_candidates": [
-    {
-      "source_page": "https://www.youtube.com/watch?v=eSFPom2NJjU",
-      "source_title": "LOPIA Kyoto Yodobashi Night Walk 4K HDR",
-      "source_author": "Wander Strolls",
-      "media_url": "https://www.youtube.com/watch?v=eSFPom2NJjU",
-      "source_license": "Standard YouTube license (no reuse grant)",
-      "license_status": "restricted-research",
-      "width": null,
-      "height": null,
-      "duration_seconds": 1954,
-      "reason": "Closest match to the preferred target (an actual LOPIA store walk, 4K HDR, night visit) but it carries the standard YouTube license, not a Creative Commons grant, so no media was downloaded. Metadata retrieval was additionally blocked by a YouTube bot gate ('Sign in to confirm you're not a bot'). Recorded as a candidate only: if the operator obtains permission, the aisle pan timestamps would need to be located from the source page directly.",
-      "candidate_skus": []
-    },
-    {
-      "source_page": "https://archive.org/details/RichPavHerroFlomJapanAwalkthroughasupermarket",
-      "source_title": "Herro Flom Japan: A walk through a supermarket",
-      "source_author": "Rich Pav",
-      "media_url": "https://archive.org/download/RichPavHerroFlomJapanAwalkthroughasupermarket/supermarket.050812_1633.00.ogv",
-      "source_license": "CC BY-NC-SA 2.5",
-      "license_url": "http://creativecommons.org/licenses/by-nc-sa/2.5/",
-      "license_status": "restricted-research",
-      "width": 400,
-      "height": 304,
-      "duration_seconds": 362.84,
-      "reason": "Genuine Japanese supermarket walk with a clear reuse license, but the best available rendition is 400x304 (the MP4 derivative is 320x240). Far below the resolution needed to detect products, and the NC clause restricts redistribution. No media downloaded.",
-      "candidate_skus": []
-    },
-    {
-      "source_page": "https://archive.org/details/RichPavAWalkThroughaSupermarketinJapan",
-      "source_title": "A Walk Through a Supermarket in Japan",
-      "source_author": "Rich Pav",
-      "media_url": "https://archive.org/download/RichPavAWalkThroughaSupermarketinJapan/herroflomjapan_050817.ogv",
-      "source_license": "CC BY-NC-SA 2.5",
-      "license_url": "http://creativecommons.org/licenses/by-nc-sa/2.5/",
-      "license_status": "restricted-research",
-      "width": 400,
-      "height": 304,
-      "duration_seconds": 365.29,
-      "reason": "Same series and same limitation as the item above: 400x304 maximum, NC clause, and the uploader's own description ('Crooked, jiggly video') indicates unstable framing. No media downloaded.",
-      "candidate_skus": []
-    },
-    {
-      "source_page": "https://www.youtube.com/watch?v=Em9BT3DaKYc",
-      "source_title": "4K Tour of Grocery Store in Japan (1)",
-      "source_author": "Trendy Explorer",
-      "media_url": "https://www.youtube.com/watch?v=Em9BT3DaKYc",
-      "source_license": "Creative Commons Attribution (reuse allowed)",
-      "license_status": "redistributable",
-      "width": 640,
-      "height": 360,
-      "duration_seconds": 1665.9,
-      "reason": "Reusable license and a long continuous store walk, but the inspected contact sheets cover produce, meat, seafood and snack aisles; no mayonnaise or sauce bay was located, so it cannot exercise the target SKUs. Not saved.",
-      "candidate_skus": []
-    },
-    {
-      "source_page": "https://www.youtube.com/watch?v=3s8KiF5UCwA",
-      "source_title": "4K Tour of Grocery Store in Japan (2)",
-      "source_author": "Trendy Explorer",
-      "media_url": "https://www.youtube.com/watch?v=3s8KiF5UCwA",
-      "source_license": "Creative Commons Attribution (reuse allowed)",
-      "license_status": "redistributable",
-      "width": 640,
-      "height": 360,
-      "duration_seconds": 752.55,
-      "reason": "Reusable license, but the same series and the same outcome: no mayonnaise/sauce shelf identified in the inspected frames. Not saved.",
-      "candidate_skus": []
-    },
-    {
-      "source_page": "https://www.youtube.com/watch?v=G9GnFJzQqaU",
-      "source_title": "PEEK INSIDE A JAPANESE SUPERMARKET-SUPER CLEAN AND VERY ORGANIZED",
-      "source_author": "WOW THAT'S JAPAN 2",
-      "media_url": "https://www.youtube.com/watch?v=G9GnFJzQqaU",
-      "source_license": "Creative Commons Attribution (reuse allowed)",
-      "license_status": "redistributable",
-      "width": 640,
-      "height": 360,
-      "duration_seconds": 325.8,
-      "reason": "Reusable license and a clean store interior, but the walk covers produce, bakery and general aisles without a mayonnaise/sauce bay. Not saved.",
-      "candidate_skus": []
-    },
-    {
-      "source_page": "https://www.youtube.com/watch?v=uauJTcfQL7U",
-      "source_title": "Cheap Japanese Groceries in Tokyo! Hidden Maruyasu Supermarket Tour",
-      "source_author": "Tomodachi Ken in Japan",
-      "media_url": "https://www.youtube.com/watch?v=uauJTcfQL7U",
-      "source_license": "Creative Commons Attribution (reuse allowed)",
-      "license_status": "redistributable",
-      "width": 640,
-      "height": 360,
-      "duration_seconds": 734.03,
-      "reason": "Reusable license, but the tour is dominated by produce, deli and dry goods; no mayonnaise/sauce shelf located in the inspected frames. Not saved.",
-      "candidate_skus": []
-    },
-    {
-      "source_page": "https://www.youtube.com/watch?v=Lrmlt8InrQ0",
-      "source_title": "Shopping at Japan's Cheapest Supermarket! 6,000 Yen Gyomu Super Grocery Haul & Tour",
-      "source_author": "Tomodachi Ken in Japan",
-      "media_url": "https://www.youtube.com/watch?v=Lrmlt8InrQ0",
-      "source_license": "Creative Commons Attribution (reuse allowed)",
-      "license_status": "redistributable",
-      "width": 640,
-      "height": 360,
-      "duration_seconds": 1934.0,
-      "reason": "Reusable license and a long walk, but the inspected segments show frozen, dry and snack aisles; no mayonnaise/sauce bay confirmed. Not saved.",
-      "candidate_skus": []
-    },
-    {
-      "source_page": "https://www.youtube.com/watch?v=kaXDaYkkQd0",
-      "source_title": "CHEAPEST Supermarket in Tokyo? OK Store JAPAN PRICES You MUST SEE!",
-      "source_author": "Tomodachi Ken in Japan",
-      "media_url": "https://www.youtube.com/watch?v=kaXDaYkkQd0",
-      "source_license": "Creative Commons Attribution (reuse allowed)",
-      "license_status": "redistributable",
-      "width": 640,
-      "height": 360,
-      "duration_seconds": 660.2,
-      "reason": "Reusable license, but the walk is price-tag oriented and no mayonnaise/sauce shelf was located. Not saved.",
-      "candidate_skus": []
-    },
-    {
-      "source_page": "https://www.youtube.com/watch?v=fOukUeu1asM",
-      "source_title": "Aeon Supermarket in Saitama 2020",
-      "source_author": "Luke's Adventures",
-      "media_url": "https://www.youtube.com/watch?v=fOukUeu1asM",
-      "source_license": "Creative Commons Attribution (reuse allowed)",
-      "license_status": "redistributable",
-      "width": 640,
-      "height": 360,
-      "duration_seconds": 188.6,
-      "reason": "Reusable license, but only 188 s long and the inspected frames show general aisles with no mayonnaise/sauce bay. Not saved.",
-      "candidate_skus": []
-    },
-    {
-      "source_page": "https://www.youtube.com/watch?v=HM2vrp7aE04",
-      "source_title": "Japanese Supermarket--Tour--Living In Japan--Grocery Shopping In Japan--Preparing for Thanksgiving",
-      "source_author": "Kat In Japan",
-      "media_url": "https://www.youtube.com/watch?v=HM2vrp7aE04",
-      "source_license": "Creative Commons Attribution (reuse allowed)",
-      "license_status": "redistributable",
-      "width": 640,
-      "height": 360,
-      "duration_seconds": 1254.57,
-      "reason": "Not rejected as a source: this is the parent upload of the saved mayonnaise-aisle clip. Listed here only to record that the full 1254.6 s walk was NOT stored (the task requires trimming to the useful section) and that its 1080p rendition is behind a YouTube bot gate, so only the 360p stream was usable.",
-      "candidate_skus": [
-        "sku_l01"
-      ]
-    }
-  ],
-  "search_log": [
-    {
-      "source": "Internet Archive advancedsearch (mediatype:movies, licenseurl:*)",
-      "queries": [
-        "(supermarket OR grocery) AND (aisle OR walk) AND (japan OR japanese)",
-        "title:(supermarket) AND mediatype:movies AND licenseurl:*"
-      ],
-      "outcome": "Two CC BY-NC-SA Japanese supermarket walks (Rich Pav, 2005); both capped at 400x304. No other Japanese supermarket walk with a reuse license."
-    },
-    {
-      "source": "Wikimedia Commons API (list=search, list=categorymembers)",
-      "queries": [
-        "srsearch=supermarket aisle Japan mayonnaise",
-        "srsearch=filemime:video/webm supermarket OR grocery OR shopping",
-        "Category:Supermarkets in Japan",
-        "Category:Interiors of supermarkets in Japan",
-        "Category:Supermarket shelves in Japan",
-        "Category:Mayonnaise from Japan",
-        "Category:Supermarkets in Tokyo"
-      ],
-      "outcome": "One usable video: 'Daily Life in Tokyo in 2013 - Supermarket in Hanzomon Station Area' (CC BY 3.0, 1920x1080). The mayonnaise categories are almost entirely plated-food photographs, not shelf scenes."
-    },
-    {
-      "source": "YouTube search with Creative Commons filter (sp=EgIwAQ%253D%253D)",
-      "queries": [
-        "japanese supermarket walk",
-        "japanese supermarket aisle sauce",
-        "japanese grocery store walk 4k",
-        "supermarket shelf products japan",
-        "japan supermarket shopping vlog",
-        "supermarket walk Japan mayonnaise",
-        "スーパー 店内 マヨネーズ 売り場"
-      ],
-      "outcome": "Nine CC-BY Japanese supermarket tours identified and license-verified via yt-dlp. Only 'Kat In Japan' contained a mayonnaise/dressing shelf bay with a continuous pan; its brand identity is not resolvable at the obtainable 360p, so no Kewpie/Ajinomoto attribution is asserted. No CC-licensed LOPIA aisle walk exists in the index; the one LOPIA store walk found is standard-license."
-    },
-    {
-      "source": "Flickr public feed API",
-      "queries": [
-        "tags=supermarket,japan"
-      ],
-      "outcome": "Feed returns stills only (no video), and the returned items are produce/seafood displays. No mayonnaise shelf sequences."
-    }
-  ],
-  "coverage_gaps": [
-    "No LOPIA aisle footage with a reuse license was found. The preferred target (LOPIA mayonnaise/sauce aisle) is only available under the standard YouTube license.",
-    "No reusable source gives per-SKU identity for the stock-lopia catalog. The mayonnaise-aisle clip confirms the category and shelf format but not brand or SKU; the Hanzomon clip confirms neither.",
-    "No reusable source reaches the resolution needed to read Japanese package text at shelf distance: the best is 1920x1080 (Hanzomon) but its frames do not contain the target category, and the clip that does contain a mayonnaise bay is 640x360.",
-    "Tier-3 replay still requires the operator's own FrameRecorder walks (code/record.py) of the LOPIA aisles; nothing here substitutes for that."
-  ]
-}
diff --git a/recognization/data/external-eval/tier3-sources/media/hanzomon-supermarket-pan-132-152.mp4 b/recognization/data/external-eval/tier3-sources/media/hanzomon-supermarket-pan-132-152.mp4
deleted file mode 100644
index 7b68ab3..0000000
Binary files a/recognization/data/external-eval/tier3-sources/media/hanzomon-supermarket-pan-132-152.mp4 and /dev/null differ
diff --git a/recognization/data/external-eval/tier3-sources/media/kat-in-japan-mayonnaise-aisle-pan-694-720.mp4 b/recognization/data/external-eval/tier3-sources/media/kat-in-japan-mayonnaise-aisle-pan-694-720.mp4
deleted file mode 100644
index ea1cef3..0000000
Binary files a/recognization/data/external-eval/tier3-sources/media/kat-in-japan-mayonnaise-aisle-pan-694-720.mp4 and /dev/null differ
diff --git a/recognization/data/external-shelves/wikimedia-commons-2/eval_result.json b/recognization/data/external-shelves/wikimedia-commons-2/eval_result.json
deleted file mode 100644
index f4e17fa..0000000
--- a/recognization/data/external-shelves/wikimedia-commons-2/eval_result.json
+++ /dev/null
@@ -1,143 +0,0 @@
-{
- "dataset": "wikimedia-commons-2",
- "catalog": "stock-lopia-clean",
- "note": "Unrelated shelves: every accept is a false accept by construction.",
- "config": {
-  "frame_long_side": 1280,
-  "detect_conf": 0.1,
-  "local_accept": 4.0,
-  "text_min_shared": 4
- },
- "images": 18,
- "detections": 1076,
- "false_accepts": 0,
- "false_accept_rate": 0.0,
- "rows": [
-  {
-   "file": "000_1920px-2011年4月の牛乳_(5588310773).jpg",
-   "title": "File:2011年4月の牛乳 (5588310773).jpg",
-   "detections": 105,
-   "false_accepts": 0,
-   "ms": 3108
-  },
-  {
-   "file": "001_Candy_in_Japan_2005_(5256472).jpg",
-   "title": "File:Candy in Japan 2005 (5256472).jpg",
-   "detections": 41,
-   "false_accepts": 0,
-   "ms": 1077
-  },
-  {
-   "file": "002_1920px-Empty_shelves_in_Tokyo_area_as_people_get_ready_for_t.jpg",
-   "title": "File:Empty shelves in Tokyo area as people get ready for typhoon Hagibis - Oct 11 2019 330pm.jpeg",
-   "detections": 55,
-   "false_accepts": 0,
-   "ms": 637
-  },
-  {
-   "file": "003_1920px-HK_SW_上環_Sheung_Wan_德輔道西_88_Des_Voeux_Road_West_shop_.jpg",
-   "title": "File:HK SW 上環 Sheung Wan 德輔道西 88 Des Voeux Road West shop 惠康超級市場 Wellcome Supermarket goods August 2020 SS2 27.jpg",
-   "detections": 38,
-   "false_accepts": 0,
-   "ms": 952
-  },
-  {
-   "file": "005_1920px-Illuminated_wooden_shelf_with_many_glass_jars_contain.jpg",
-   "title": "File:Illuminated wooden shelf with many glass jars containing cookies for sale in Tokyo.jpg",
-   "detections": 47,
-   "false_accepts": 0,
-   "ms": 862
-  },
-  {
-   "file": "006_1920px-Interior_of_JUSCO_Shingu_Shopping_Center_20100213c.jpg",
-   "title": "File:Interior of JUSCO Shingu Shopping Center 20100213c.JPG",
-   "detections": 105,
-   "false_accepts": 0,
-   "ms": 1087
-  },
-  {
-   "file": "007_Japan_earthquake_store_shelves.jpg",
-   "title": "File:Japan earthquake store shelves.jpg",
-   "detections": 11,
-   "false_accepts": 0,
-   "ms": 277
-  },
-  {
-   "file": "009_Milk_(2655574568).jpg",
-   "title": "File:Milk (2655574568).jpg",
-   "detections": 153,
-   "false_accepts": 0,
-   "ms": 2720
-  },
-  {
-   "file": "010_Natsuge-mikado_okashi.jpg",
-   "title": "File:Natsuge-mikado okashi.jpg",
-   "detections": 30,
-   "false_accepts": 0,
-   "ms": 724
-  },
-  {
-   "file": "012_1920px-Supermarket_in_2006_(646332846).jpg",
-   "title": "File:Supermarket in 2006 (646332846).jpg",
-   "detections": 174,
-   "false_accepts": 0,
-   "ms": 1950
-  },
-  {
-   "file": "014_1920px-「ペヤング」予想通りの大売れ！_(18634213682).jpg",
-   "title": "File:「ペヤング」予想通りの大売れ！ (18634213682).jpg",
-   "detections": 30,
-   "false_accepts": 0,
-   "ms": 707
-  },
-  {
-   "file": "015_1920px-お客様各位_2008_国内産バター_(2429997079).jpg",
-   "title": "File:お客様各位 2008 国内産バター (2429997079).jpg",
-   "detections": 28,
-   "false_accepts": 0,
-   "ms": 1036
-  },
-  {
-   "file": "016_1920px-パン売り場_手動ドア式_2016_(28574754416).jpg",
-   "title": "File:パン売り場 手動ドア式 2016 (28574754416).jpg",
-   "detections": 63,
-   "false_accepts": 0,
-   "ms": 1248
-  },
-  {
-   "file": "017_1920px-プラレール_2015_(22339689873).jpg",
-   "title": "File:プラレール 2015 (22339689873).jpg",
-   "detections": 73,
-   "false_accepts": 0,
-   "ms": 1324
-  },
-  {
-   "file": "018_一家族様一本限り_(5588902836).jpg",
-   "title": "File:一家族様一本限り (5588902836).jpg",
-   "detections": 41,
-   "false_accepts": 0,
-   "ms": 1208
-  },
-  {
-   "file": "019_1920px-洗濯洗剤_(10852819193).jpg",
-   "title": "File:洗濯洗剤 (10852819193).jpg",
-   "detections": 44,
-   "false_accepts": 0,
-   "ms": 606
-  },
-  {
-   "file": "020_1920px-申し訳ございませんが_ごめんなさい_お魚本日の調理は終了いたしました。_どうぞよろしくお願いします_(11.jpg",
-   "title": "File:申し訳ございませんが ごめんなさい お魚本日の調理は終了いたしました。 どうぞよろしくお願いします (11331761984).jpg",
-   "detections": 25,
-   "false_accepts": 0,
-   "ms": 571
-  },
-  {
-   "file": "021_1920px-要冷蔵_(4513315790).jpg",
-   "title": "File:要冷蔵 (4513315790).jpg",
-   "detections": 13,
-   "false_accepts": 0,
-   "ms": 259
-  }
- ]
-}
\ No newline at end of file
diff --git a/recognization/data/external-shelves/wikimedia-commons-2/manifest.json b/recognization/data/external-shelves/wikimedia-commons-2/manifest.json
deleted file mode 100644
index 52aad7a..0000000
--- a/recognization/data/external-shelves/wikimedia-commons-2/manifest.json
+++ /dev/null
@@ -1,185 +0,0 @@
-{
- "source": "Wikimedia Commons",
- "images": [
-  {
-   "file": "000_1920px-2011年4月の牛乳_(5588310773).jpg",
-   "title": "File:2011年4月の牛乳 (5588310773).jpg",
-   "width": 2048,
-   "height": 1360,
-   "license": "CC BY-SA 2.0",
-   "artist": "<a rel=\"nofollow\" class=\"external text\" href=\"https://www.flickr.com/people/35126719@N02\">m-jy</a>",
-   "source": "https://commons.wikimedia.org/wiki/File%3A2011%E5%B9%B44%E6%9C%88%E3%81%AE%E7%89%9B%E4%B9%B3_%285588310773%29.jpg",
-   "queries": 1
-  },
-  {
-   "file": "001_Candy_in_Japan_2005_(5256472).jpg",
-   "title": "File:Candy in Japan 2005 (5256472).jpg",
-   "width": 1350,
-   "height": 1800,
-   "license": "CC BY-SA 2.0",
-   "artist": "<a rel=\"nofollow\" class=\"external text\" href=\"https://www.flickr.com/people/97624390@N00\">OpenCage</a>",
-   "source": "https://commons.wikimedia.org/wiki/File%3ACandy_in_Japan_2005_%285256472%29.jpg",
-   "queries": 1
-  },
-  {
-   "file": "002_1920px-Empty_shelves_in_Tokyo_area_as_people_get_ready_for_t.jpg",
-   "title": "File:Empty shelves in Tokyo area as people get ready for typhoon Hagibis - Oct 11 2019 330pm.jpeg",
-   "width": 4027,
-   "height": 3018,
-   "license": "CC BY 4.0",
-   "artist": "<a href=\"//commons.wikimedia.org/wiki/User:Nesnad\" title=\"User:Nesnad\">Nesnad</a>",
-   "source": "https://commons.wikimedia.org/wiki/File%3AEmpty_shelves_in_Tokyo_area_as_people_get_ready_for_typhoon_Hagibis_-_Oct_11_2019_330pm.jpeg",
-   "queries": 1
-  },
-  {
-   "file": "003_1920px-HK_SW_上環_Sheung_Wan_德輔道西_88_Des_Voeux_Road_West_shop_.jpg",
-   "title": "File:HK SW 上環 Sheung Wan 德輔道西 88 Des Voeux Road West shop 惠康超級市場 Wellcome Supermarket goods August 2020 SS2 27.jpg",
-   "width": 4128,
-   "height": 3096,
-   "license": "CC BY-SA 4.0",
-   "artist": "<a href=\"//commons.wikimedia.org/wiki/User:Gutygore_Kfsoe_bfzdao\" title=\"User:Gutygore Kfsoe bfzdao\">Gutygore Kfsoe bfzdao</a>",
-   "source": "https://commons.wikimedia.org/wiki/File%3AHK_SW_%E4%B8%8A%E7%92%B0_Sheung_Wan_%E5%BE%B7%E8%BC%94%E9%81%93%E8%A5%BF_88_Des_Voeux_Road_West_shop_%E6%83%A0%E5%BA%B7%E8%B6%85%E7%B4%9A%E5%B8%82%E5%A0%B4_Wellcome_Supermarket_goods_August_2020_SS2_27.jpg",
-   "queries": 1
-  },
-  {
-   "file": "005_1920px-Illuminated_wooden_shelf_with_many_glass_jars_contain.jpg",
-   "title": "File:Illuminated wooden shelf with many glass jars containing cookies for sale in Tokyo.jpg",
-   "width": 4037,
-   "height": 6055,
-   "license": "CC BY-SA 4.0",
-   "artist": "<a href=\"//commons.wikimedia.org/wiki/User:Basile_Morin\" title=\"User:Basile Morin\">Basile Morin</a>",
-   "source": "https://commons.wikimedia.org/wiki/File%3AIlluminated_wooden_shelf_with_many_glass_jars_containing_cookies_for_sale_in_Tokyo.jpg",
-   "queries": 1
-  },
-  {
-   "file": "006_1920px-Interior_of_JUSCO_Shingu_Shopping_Center_20100213c.jpg",
-   "title": "File:Interior of JUSCO Shingu Shopping Center 20100213c.JPG",
-   "width": 3648,
-   "height": 2736,
-   "license": "CC0",
-   "artist": "<a href=\"//commons.wikimedia.org/wiki/User:%E7%8E%84%E5%8F%B2%E7%94%9F\" title=\"User:玄史生\">玄史生</a>",
-   "source": "https://commons.wikimedia.org/wiki/File%3AInterior_of_JUSCO_Shingu_Shopping_Center_20100213c.JPG",
-   "queries": 1
-  },
-  {
-   "file": "007_Japan_earthquake_store_shelves.jpg",
-   "title": "File:Japan earthquake store shelves.jpg",
-   "width": 1600,
-   "height": 1200,
-   "license": "CC0",
-   "artist": "<a href=\"//commons.wikimedia.org/w/index.php?title=User:Kellykaneshiro&amp;action=edit&amp;redlink=1\" class=\"new\" title=\"User:Kellykaneshiro (page does not exist)\">Kellykaneshiro</a>",
-   "source": "https://commons.wikimedia.org/wiki/File%3AJapan_earthquake_store_shelves.jpg",
-   "queries": 1
-  },
-  {
-   "file": "009_Milk_(2655574568).jpg",
-   "title": "File:Milk (2655574568).jpg",
-   "width": 1600,
-   "height": 1200,
-   "license": "CC BY 2.0",
-   "artist": "<a rel=\"nofollow\" class=\"external text\" href=\"https://www.flickr.com/people/36014533@N00\">yumtan</a>",
-   "source": "https://commons.wikimedia.org/wiki/File%3AMilk_%282655574568%29.jpg",
-   "queries": 1
-  },
-  {
-   "file": "010_Natsuge-mikado_okashi.jpg",
-   "title": "File:Natsuge-mikado okashi.jpg",
-   "width": 1200,
-   "height": 900,
-   "license": "CC BY-SA 4.0",
-   "artist": "<a href=\"//commons.wikimedia.org/wiki/User:8joKeaton\" title=\"User:8joKeaton\">8joKeaton</a>",
-   "source": "https://commons.wikimedia.org/wiki/File%3ANatsuge-mikado_okashi.jpg",
-   "queries": 1
-  },
-  {
-   "file": "012_1920px-Supermarket_in_2006_(646332846).jpg",
-   "title": "File:Supermarket in 2006 (646332846).jpg",
-   "width": 2000,
-   "height": 3008,
-   "license": "CC BY 2.0",
-   "artist": "<a rel=\"nofollow\" class=\"external text\" href=\"https://www.flickr.com/people/56825141@N00\">Simon Helle Nielsen</a>",
-   "source": "https://commons.wikimedia.org/wiki/File%3ASupermarket_in_2006_%28646332846%29.jpg",
-   "queries": 1
-  },
-  {
-   "file": "014_1920px-「ペヤング」予想通りの大売れ！_(18634213682).jpg",
-   "title": "File:「ペヤング」予想通りの大売れ！ (18634213682).jpg",
-   "width": 3264,
-   "height": 2448,
-   "license": "CC BY 2.0",
-   "artist": "<a rel=\"nofollow\" class=\"external text\" href=\"https://www.flickr.com/people/40781985@N08\">Nori Norisa</a>",
-   "source": "https://commons.wikimedia.org/wiki/File%3A%E3%80%8C%E3%83%9A%E3%83%A4%E3%83%B3%E3%82%B0%E3%80%8D%E4%BA%88%E6%83%B3%E9%80%9A%E3%82%8A%E3%81%AE%E5%A4%A7%E5%A3%B2%E3%82%8C%EF%BC%81_%2818634213682%29.jpg",
-   "queries": 1
-  },
-  {
-   "file": "015_1920px-お客様各位_2008_国内産バター_(2429997079).jpg",
-   "title": "File:お客様各位 2008 国内産バター (2429997079).jpg",
-   "width": 3680,
-   "height": 2760,
-   "license": "CC BY 2.0",
-   "artist": "<a rel=\"nofollow\" class=\"external text\" href=\"https://www.flickr.com/people/56804885@N00\">Kenichiro MATOHARA</a>",
-   "source": "https://commons.wikimedia.org/wiki/File%3A%E3%81%8A%E5%AE%A2%E6%A7%98%E5%90%84%E4%BD%8D_2008_%E5%9B%BD%E5%86%85%E7%94%A3%E3%83%90%E3%82%BF%E3%83%BC_%282429997079%29.jpg",
-   "queries": 1
-  },
-  {
-   "file": "016_1920px-パン売り場_手動ドア式_2016_(28574754416).jpg",
-   "title": "File:パン売り場 手動ドア式 2016 (28574754416).jpg",
-   "width": 3264,
-   "height": 2448,
-   "license": "CC BY 2.0",
-   "artist": "<a rel=\"nofollow\" class=\"external text\" href=\"https://www.flickr.com/people/10581108@N00\">jencu</a>",
-   "source": "https://commons.wikimedia.org/wiki/File%3A%E3%83%91%E3%83%B3%E5%A3%B2%E3%82%8A%E5%A0%B4_%E6%89%8B%E5%8B%95%E3%83%89%E3%82%A2%E5%BC%8F_2016_%2828574754416%29.jpg",
-   "queries": 1
-  },
-  {
-   "file": "017_1920px-プラレール_2015_(22339689873).jpg",
-   "title": "File:プラレール 2015 (22339689873).jpg",
-   "width": 3264,
-   "height": 2448,
-   "license": "CC BY 2.0",
-   "artist": "<a rel=\"nofollow\" class=\"external text\" href=\"https://www.flickr.com/people/27983255@N00\">Tatsuo Yamashita</a>",
-   "source": "https://commons.wikimedia.org/wiki/File%3A%E3%83%97%E3%83%A9%E3%83%AC%E3%83%BC%E3%83%AB_2015_%2822339689873%29.jpg",
-   "queries": 1
-  },
-  {
-   "file": "018_一家族様一本限り_(5588902836).jpg",
-   "title": "File:一家族様一本限り (5588902836).jpg",
-   "width": 1360,
-   "height": 2048,
-   "license": "CC BY-SA 2.0",
-   "artist": "<a rel=\"nofollow\" class=\"external text\" href=\"https://www.flickr.com/people/35126719@N02\">m-jy</a>",
-   "source": "https://commons.wikimedia.org/wiki/File%3A%E4%B8%80%E5%AE%B6%E6%97%8F%E6%A7%98%E4%B8%80%E6%9C%AC%E9%99%90%E3%82%8A_%285588902836%29.jpg",
-   "queries": 1
-  },
-  {
-   "file": "019_1920px-洗濯洗剤_(10852819193).jpg",
-   "title": "File:洗濯洗剤 (10852819193).jpg",
-   "width": 4361,
-   "height": 3264,
-   "license": "CC BY 2.0",
-   "artist": "<a rel=\"nofollow\" class=\"external text\" href=\"https://www.flickr.com/people/75483054@N00\">Tamaki Sono</a>",
-   "source": "https://commons.wikimedia.org/wiki/File%3A%E6%B4%97%E6%BF%AF%E6%B4%97%E5%89%A4_%2810852819193%29.jpg",
-   "queries": 1
-  },
-  {
-   "file": "020_1920px-申し訳ございませんが_ごめんなさい_お魚本日の調理は終了いたしました。_どうぞよろしくお願いします_(11.jpg",
-   "title": "File:申し訳ございませんが ごめんなさい お魚本日の調理は終了いたしました。 どうぞよろしくお願いします (11331761984).jpg",
-   "width": 4000,
-   "height": 2672,
-   "license": "CC BY 2.0",
-   "artist": "<a rel=\"nofollow\" class=\"external text\" href=\"https://www.flickr.com/people/110295958@N06\">ChingHua Chung</a>",
-   "source": "https://commons.wikimedia.org/wiki/File%3A%E7%94%B3%E3%81%97%E8%A8%B3%E3%81%94%E3%81%96%E3%81%84%E3%81%BE%E3%81%9B%E3%82%93%E3%81%8C_%E3%81%94%E3%82%81%E3%82%93%E3%81%AA%E3%81%95%E3%81%84_%E3%81%8A%E9%AD%9A%E6%9C%AC%E6%97%A5%E3%81%AE%E8%AA%BF%E7%90%86%E3%81%AF%E7%B5%82%E4%BA%86%E3%81%84%E3%81%9F%E3%81%97%E3%81%BE%E3%81%97%E3%81%9F%E3%80%82_%E3%81%A9%E3%81%86%E3%81%9E%E3%82%88%E3%82%8D%E3%81%97%E3%81%8F%E3%81%8A%E9%A1%98%E3%81%84%E3%81%97%E3%81%BE%E3%81%99_%2811331761984%29.jpg",
-   "queries": 1
-  },
-  {
-   "file": "021_1920px-要冷蔵_(4513315790).jpg",
-   "title": "File:要冷蔵 (4513315790).jpg",
-   "width": 4000,
-   "height": 3000,
-   "license": "CC BY 2.0",
-   "artist": "<a rel=\"nofollow\" class=\"external text\" href=\"https://www.flickr.com/people/47256332@N05\">Hajime Nagahata</a>",
-   "source": "https://commons.wikimedia.org/wiki/File%3A%E8%A6%81%E5%86%B7%E8%94%B5_%284513315790%29.jpg",
-   "queries": 1
-  }
- ]
-}
\ No newline at end of file
diff --git a/recognization/data/external-shelves/wikimedia-commons/eval_result.json b/recognization/data/external-shelves/wikimedia-commons/eval_result.json
deleted file mode 100644
index d5eb659..0000000
--- a/recognization/data/external-shelves/wikimedia-commons/eval_result.json
+++ /dev/null
@@ -1,339 +0,0 @@
-{
- "dataset": "wikimedia-commons",
- "catalog": "stock-lopia-clean",
- "note": "Unrelated shelves: every accept is a false accept by construction.",
- "config": {
-  "frame_long_side": 1280,
-  "detect_conf": 0.1,
-  "local_accept": 4.0,
-  "text_min_shared": 4
- },
- "images": 46,
- "detections": 3504,
- "false_accepts": 3,
- "false_accept_rate": 0.0009,
- "rows": [
-  {
-   "file": "000_1920px-198円_中華弁当_2016_(25800617695).jpg",
-   "title": "File:198円 中華弁当 2016 (25800617695).jpg",
-   "detections": 14,
-   "false_accepts": 0,
-   "ms": 1669
-  },
-  {
-   "file": "001_1920px-2011年4月の牛乳_(5588310773).jpg",
-   "title": "File:2011年4月の牛乳 (5588310773).jpg",
-   "detections": 105,
-   "false_accepts": 0,
-   "ms": 3225
-  },
-  {
-   "file": "002_1920px-20140909-0252_Costa_Mesa_Mitsuwa.JPG",
-   "title": "File:20140909-0252 Costa Mesa Mitsuwa.JPG",
-   "detections": 121,
-   "false_accepts": 0,
-   "ms": 2674
-  },
-  {
-   "file": "003_1920px-Aomori_Home_(4721758999).jpg",
-   "title": "File:Aomori Home (4721758999).jpg",
-   "detections": 97,
-   "false_accepts": 0,
-   "ms": 3333
-  },
-  {
-   "file": "004_1920px-Asakusa_15_-_making_rice_crackers_the_old_way_(155797.jpg",
-   "title": "File:Asakusa 15 - making rice crackers the old way (15579727827).jpg",
-   "detections": 40,
-   "false_accepts": 0,
-   "ms": 456
-  },
-  {
-   "file": "005_1920px-Asakusa_16_-_making_rice_crackers_the_old_way_(155794.jpg",
-   "title": "File:Asakusa 16 - making rice crackers the old way (15579472088).jpg",
-   "detections": 30,
-   "false_accepts": 0,
-   "ms": 537
-  },
-  {
-   "file": "006_1920px-Asakusa_17_-_making_rice_crackers_the_old_way_(155794.jpg",
-   "title": "File:Asakusa 17 - making rice crackers the old way (15579471588).jpg",
-   "detections": 30,
-   "false_accepts": 0,
-   "ms": 485
-  },
-  {
-   "file": "007_1920px-Asakusa_19_-_the_woman_selling_the_rice_crackers_(157.jpg",
-   "title": "File:Asakusa 19 - the woman selling the rice crackers (15764889785).jpg",
-   "detections": 33,
-   "false_accepts": 0,
-   "ms": 502
-  },
-  {
-   "file": "008_1920px-Azabudai-Hills---2025-04-05_015.jpg",
-   "title": "File:Azabudai-Hills---2025-04-05 015.jpg",
-   "detections": 54,
-   "false_accepts": 2,
-   "ms": 2525
-  },
-  {
-   "file": "010_1920px-Breathalyzers_in_a_MaxValu_supermarket.jpg",
-   "title": "File:Breathalyzers in a MaxValu supermarket.jpg",
-   "detections": 24,
-   "false_accepts": 0,
-   "ms": 1508
-  },
-  {
-   "file": "011_1920px-Chocolate_in_Japan_(5137097169).jpg",
-   "title": "File:Chocolate in Japan (5137097169).jpg",
-   "detections": 78,
-   "false_accepts": 0,
-   "ms": 1961
-  },
-  {
-   "file": "013_1920px-Confectionery_at_WCTC_Shopping_Center,_Koror_City,_Pa.jpg",
-   "title": "File:Confectionery at WCTC Shopping Center, Koror City, Palau.jpg",
-   "detections": 154,
-   "false_accepts": 0,
-   "ms": 3424
-  },
-  {
-   "file": "014_1920px-Deer_cookies_at_Nara,_Japan.jpg",
-   "title": "File:Deer cookies at Nara, Japan.jpg",
-   "detections": 24,
-   "false_accepts": 0,
-   "ms": 465
-  },
-  {
-   "file": "015_1920px-ESL_Price_Tag.JPG",
-   "title": "File:ESL Price Tag.JPG",
-   "detections": 56,
-   "false_accepts": 0,
-   "ms": 1383
-  },
-  {
-   "file": "017_1920px-Food-with-physiological-energy-value-example.jpg",
-   "title": "File:Food-with-physiological-energy-value-example.jpg",
-   "detections": 32,
-   "false_accepts": 0,
-   "ms": 1070
-  },
-  {
-   "file": "019_1920px-HK_CWB_Wellcome_Shop_Supermarket_goods_Carbonated_sof.jpg",
-   "title": "File:HK CWB Wellcome Shop Supermarket goods Carbonated soft drink codd-neck bottles 波子汽水 Apple Ramune Japanese products July 2017 IX1.jpg",
-   "detections": 42,
-   "false_accepts": 0,
-   "ms": 1094
-  },
-  {
-   "file": "021_1920px-Interior_of_Supermarket_in_Japan_02.jpg",
-   "title": "File:Interior of Supermarket in Japan 02.jpg",
-   "detections": 18,
-   "false_accepts": 0,
-   "ms": 491
-  },
-  {
-   "file": "022_1920px-JP_日本_Japan_沖繩_OKINAWA_浦添市_Urasoe-city_AEON_Supermark.jpg",
-   "title": "File:JP 日本 Japan 沖繩 OKINAWA 浦添市 Urasoe-city AEON Supermarket food drinks February 2026 N13p 02.jpg",
-   "detections": 165,
-   "false_accepts": 0,
-   "ms": 3522
-  },
-  {
-   "file": "023_1920px-thumbnail.jpg",
-   "title": "File:JP 日本 Japan 沖繩 OKINAWA 那霸 Naha Omoromachi Shintoshin 新都心 Naha Main Place 商場 Shopping Mall shop Aeon Supermarket drink 益力多 January 2025 R12S 01.jpg",
-   "detections": 197,
-   "false_accepts": 0,
-   "ms": 3389
-  },
-  {
-   "file": "024_1920px-JaSupermarket_Instant_noodles.JPG",
-   "title": "File:JaSupermarket Instant noodles.JPG",
-   "detections": 147,
-   "false_accepts": 0,
-   "ms": 2744
-  },
-  {
-   "file": "026_1920px-Kawasaki-Inryo-1.jpg",
-   "title": "File:Kawasaki-Inryo-1.jpg",
-   "detections": 45,
-   "false_accepts": 0,
-   "ms": 2007
-  },
-  {
-   "file": "029_1920px-Miyazu_Mipple_1F_2021-04_ac.jpg",
-   "title": "File:Miyazu Mipple 1F 2021-04 ac.jpg",
-   "detections": 157,
-   "false_accepts": 0,
-   "ms": 1782
-  },
-  {
-   "file": "030_1920px-Octopus_rice_crackers_No,1.JPG",
-   "title": "File:Octopus rice crackers No,1.JPG",
-   "detections": 22,
-   "false_accepts": 0,
-   "ms": 556
-  },
-  {
-   "file": "031_1920px-Octopus_senbei.JPG",
-   "title": "File:Octopus senbei.JPG",
-   "detections": 23,
-   "false_accepts": 0,
-   "ms": 612
-  },
-  {
-   "file": "032_1920px-Okaki_for_sale_in_Kawagoe.jpg",
-   "title": "File:Okaki for sale in Kawagoe.jpg",
-   "detections": 70,
-   "false_accepts": 0,
-   "ms": 1042
-  },
-  {
-   "file": "033_Okonomiyaki_and_takoyaki_ingredients.jpg",
-   "title": "File:Okonomiyaki and takoyaki ingredients.jpg",
-   "detections": 55,
-   "false_accepts": 0,
-   "ms": 2553
-  },
-  {
-   "file": "034_1920px-Rice_toppings_&_additional_ingredients_(5846028686).jpg",
-   "title": "File:Rice toppings & additional ingredients (5846028686).jpg",
-   "detections": 59,
-   "false_accepts": 0,
-   "ms": 1871
-  },
-  {
-   "file": "035_1920px-SW_4963.jpg",
-   "title": "File:SW 4963.jpg",
-   "detections": 41,
-   "false_accepts": 0,
-   "ms": 755
-  },
-  {
-   "file": "037_1920px-Soy_Sauce_selection_(6362318717).jpg",
-   "title": "File:Soy Sauce selection (6362318717).jpg",
-   "detections": 85,
-   "false_accepts": 0,
-   "ms": 1623
-  },
-  {
-   "file": "038_1920px-Soy_sauce_(5846027636).jpg",
-   "title": "File:Soy sauce (5846027636).jpg",
-   "detections": 216,
-   "false_accepts": 0,
-   "ms": 2534
-  },
-  {
-   "file": "039_1920px-Soy_sauce_in_supermarket.JPG",
-   "title": "File:Soy sauce in supermarket.JPG",
-   "detections": 101,
-   "false_accepts": 0,
-   "ms": 1884
-  },
-  {
-   "file": "040_1920px-Spice_Supermarket_Japan.JPG",
-   "title": "File:Spice Supermarket Japan.JPG",
-   "detections": 300,
-   "false_accepts": 0,
-   "ms": 2994
-  },
-  {
-   "file": "041_1920px-Strong_Zero_4962.jpg",
-   "title": "File:Strong Zero 4962.jpg",
-   "detections": 45,
-   "false_accepts": 0,
-   "ms": 1731
-  },
-  {
-   "file": "042_1920px-Supermarket_Digital_Displays_(2927051130).jpg",
-   "title": "File:Supermarket Digital Displays (2927051130).jpg",
-   "detections": 97,
-   "false_accepts": 1,
-   "ms": 2454
-  },
-  {
-   "file": "043_1920px-Tasty_Food_Abundance_in_Healthy_Europe.png",
-   "title": "File:Tasty Food Abundance in Healthy Europe.png",
-   "detections": 32,
-   "false_accepts": 0,
-   "ms": 982
-  },
-  {
-   "file": "047_1920px-Tokyo---Ebisu-and-Shibuya---2026-02-04_162.jpg",
-   "title": "File:Tokyo---Ebisu-and-Shibuya---2026-02-04 162.jpg",
-   "detections": 81,
-   "false_accepts": 0,
-   "ms": 1356
-  },
-  {
-   "file": "048_1920px-Tokyo---Ebisu-and-Shibuya---2026-02-04_167.jpg",
-   "title": "File:Tokyo---Ebisu-and-Shibuya---2026-02-04 167.jpg",
-   "detections": 51,
-   "false_accepts": 0,
-   "ms": 823
-  },
-  {
-   "file": "050_1920px-Umami_Snack_(41957534).jpeg",
-   "title": "File:Umami Snack (41957534).jpeg",
-   "detections": 55,
-   "false_accepts": 0,
-   "ms": 2518
-  },
-  {
-   "file": "051_1920px-このかねをならすのはだーれ_(3079692697).jpg",
-   "title": "File:このかねをならすのはだーれ (3079692697).jpg",
-   "detections": 3,
-   "false_accepts": 0,
-   "ms": 170
-  },
-  {
-   "file": "052_1920px-スーパーの野菜コーナー_2008_(2785121488).jpg",
-   "title": "File:スーパーの野菜コーナー 2008 (2785121488).jpg",
-   "detections": 85,
-   "false_accepts": 0,
-   "ms": 1963
-  },
-  {
-   "file": "055_1920px-中央フード山手店_-_panoramio.jpg",
-   "title": "File:中央フード山手店 - panoramio.jpg",
-   "detections": 39,
-   "false_accepts": 0,
-   "ms": 689
-  },
-  {
-   "file": "056_1920px-何このリポＤ人気⁈買い占めてしまおう。_(4025507198).jpg",
-   "title": "File:何このリポＤ人気⁈買い占めてしまおう。 (4025507198).jpg",
-   "detections": 72,
-   "false_accepts": 0,
-   "ms": 2286
-  },
-  {
-   "file": "057_1920px-味噌売り場_(16183474871).jpg",
-   "title": "File:味噌売り場 (16183474871).jpg",
-   "detections": 76,
-   "false_accepts": 0,
-   "ms": 2081
-  },
-  {
-   "file": "058_1920px-塩ひかえめ_(25347152904).jpg",
-   "title": "File:塩ひかえめ (25347152904).jpg",
-   "detections": 86,
-   "false_accepts": 0,
-   "ms": 2496
-  },
-  {
-   "file": "060_1920px-牛乳_豆乳_2016_(32081175436).jpg",
-   "title": "File:牛乳 豆乳 2016 (32081175436).jpg",
-   "detections": 130,
-   "false_accepts": 0,
-   "ms": 4677
-  },
-  {
-   "file": "061_1920px-表示価格より100円引_(38057600585).jpg",
-   "title": "File:表示価格より100円引 (38057600585).jpg",
-   "detections": 17,
-   "false_accepts": 0,
-   "ms": 899
-  }
- ]
-}
\ No newline at end of file
diff --git a/recognization/data/external-shelves/wikimedia-commons/manifest.json b/recognization/data/external-shelves/wikimedia-commons/manifest.json
deleted file mode 100644
index aaf1b1c..0000000
--- a/recognization/data/external-shelves/wikimedia-commons/manifest.json
+++ /dev/null
@@ -1,465 +0,0 @@
-{
- "source": "Wikimedia Commons",
- "images": [
-  {
-   "file": "000_1920px-198円_中華弁当_2016_(25800617695).jpg",
-   "title": "File:198円 中華弁当 2016 (25800617695).jpg",
-   "width": 4032,
-   "height": 3024,
-   "license": "CC BY-SA 2.0",
-   "artist": "<a rel=\"nofollow\" class=\"external text\" href=\"https://www.flickr.com/people/25584764@N06\">Takayoshi Nishida</a>",
-   "source": "https://commons.wikimedia.org/wiki/File%3A198%E5%86%86_%E4%B8%AD%E8%8F%AF%E5%BC%81%E5%BD%93_2016_%2825800617695%29.jpg",
-   "queries": 1
-  },
-  {
-   "file": "001_1920px-2011年4月の牛乳_(5588310773).jpg",
-   "title": "File:2011年4月の牛乳 (5588310773).jpg",
-   "width": 2048,
-   "height": 1360,
-   "license": "CC BY-SA 2.0",
-   "artist": "<a rel=\"nofollow\" class=\"external text\" href=\"https://www.flickr.com/people/35126719@N02\">m-jy</a>",
-   "source": "https://commons.wikimedia.org/wiki/File%3A2011%E5%B9%B44%E6%9C%88%E3%81%AE%E7%89%9B%E4%B9%B3_%285588310773%29.jpg",
-   "queries": 4
-  },
-  {
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diff --git a/recognization/data/packshots/research/sku_l04/manifest.json b/recognization/data/packshots/research/sku_l04/manifest.json
deleted file mode 100644
index 682a0b2..0000000
--- a/recognization/data/packshots/research/sku_l04/manifest.json
+++ /dev/null
@@ -1,125 +0,0 @@
-{
-  "sku": "sku_l04",
-  "research_date": "2026-09-29",
-  "candidate_name": "やまや めんたいマヨネーズタイプ 200g",
-  "identity": {
-    "official_product_name": "めんたいマヨネーズタイプ",
-    "recommended_canonical_product_name": "やまや めんたいマヨネーズタイプ 200g",
-    "package_brand": "博多の味 やまや",
-    "maker_distributor": "株式会社やまやコミュニケーションズ",
-    "size": "200g",
-    "jan": "4903085062863",
-    "jan_status": "corroborated major-retailer product record; valid JAN-13 check digit",
-    "asin": "B0197VWHRG",
-    "current_successor_or_size_variant": {
-      "product_name": "めんたいマヨネーズタイプ300g",
-      "jan": "4903085062894",
-      "status": "current official Yamaya listing; distinct size/JAN and not the sku_l04 item"
-    }
-  },
-  "conclusion": {
-    "existing_packshot_exact": true,
-    "existing_packshot_path": "../../source/sku_l04.jpg",
-    "existing_packshot_match_basis": "The existing file is byte-identical to Amazon media asset 61yWDtDjDuL.jpg (MD5 510db1965bc483899c0cce17c72ba10e). Amazon identifies ASIN B0197VWHRG as Yamaya Mentai Mayonnaise Type 200g, brand やまや, manufacturer 株式会社やまやコミュニケーションズ. Yahoo independently lists the same 200g product and JAN 4903085062863.",
-    "ocr_mismatch_explanation": "The prominent package text 明太子屋さんがつくった is a slogan meaning 'made by a mentaiko shop', not a competing brand or product name. The small mark at upper right reads 博多の味 やまや; the large product text reads めんたいマヨネーズタイプ.",
-    "csv_name_should_change": false,
-    "recommended_canonical_product_name": "やまや めんたいマヨネーズタイプ 200g",
-    "recommended_jan": "4903085062863",
-    "confidence": "high",
-    "caveat": "Yamaya's current official catalog has moved to a 300g presentation. The 200g item remains independently evidenced by Amazon, Yahoo Shopping, and Amica, but should be treated as an older/discontinued size rather than the current 300g SKU."
-  },
-  "evidence_sources": [
-    {
-      "saved_page": "pages/amazon_B0197VWHRG.html",
-      "source_page": "https://www.amazon.co.jp/dp/B0197VWHRG",
-      "proves": "200g product title, やまや brand, 株式会社やまやコミュニケーションズ manufacturer, ASIN B0197VWHRG, and exact media asset"
-    },
-    {
-      "saved_page": "pages/yahoo_200g_3939-002118.html",
-      "source_page": "https://store.shopping.yahoo.co.jp/hks-shop/3939-002118.html",
-      "proves": "やまや めんたいマヨネーズタイプ 200g and JAN 4903085062863"
-    },
-    {
-      "saved_page": "pages/rakuten_amica_200g.html",
-      "source_page": "https://item.rakuten.co.jp/amicashop/x51170144004/",
-      "proves": "Yamaya AD 明太マヨネーズタイプ 200g maker/address, size, ingredients, and related commercial 200g presentation"
-    },
-    {
-      "saved_page": "pages/yamaya_official_home.html",
-      "source_page": "https://www.yamaya.com/",
-      "proves": "official company identity and product-site provenance"
-    },
-    {
-      "saved_page": "pages/yamaya_current_300g.html",
-      "source_page": "https://www.yamaya.com/products/detail/06385",
-      "proves": "current official product name めんたいマヨネーズタイプ300g and current 300g package"
-    },
-    {
-      "saved_page": "pages/yamaya_recipe_current_300g.html",
-      "source_page": "https://www.yamaya.com/recipe/categories/detail/661",
-      "proves": "official usage of the product name and current 300g size"
-    }
-  ],
-  "images": [
-    {
-      "file": "images/amazon_200g.jpg",
-      "sku": "sku_l04",
-      "product": "やまや めんたいマヨネーズタイプ 200g",
-      "source_page": "https://www.amazon.co.jp/dp/B0197VWHRG",
-      "media_url": "https://m.media-amazon.com/images/I/61yWDtDjDuL.jpg",
-      "width": 1785,
-      "height": 2031,
-      "view_type": "front_packshot",
-      "license_status": "restricted-research",
-      "notes": "Visually inspected. Pink squeeze pouch; upper-right 博多の味 やまや mark; slogan 明太子屋さんがつくった; large めんたいマヨネーズタイプ product lettering. Byte-identical to data/packshots/source/sku_l04.jpg."
-    },
-    {
-      "file": "images/yahoo_200g.jpg",
-      "sku": "sku_l04",
-      "product": "やまや めんたいマヨネーズタイプ 200g",
-      "source_page": "https://store.shopping.yahoo.co.jp/hks-shop/3939-002118.html",
-      "media_url": "https://item-shopping.c.yimg.jp/i/n/hks-shop_3939-002118",
-      "width": 900,
-      "height": 900,
-      "view_type": "front_packshot",
-      "license_status": "restricted-research",
-      "notes": "Visually inspected. Major-retailer packshot for the listing that states JAN 4903085062863; same historic pink package family as the Amazon asset."
-    },
-    {
-      "file": "images/rakuten_ad_200g.jpg",
-      "sku": "sku_l04_comparison_ad_variant",
-      "product": "やまや AD明太マヨネーズタイプ 200g",
-      "source_page": "https://item.rakuten.co.jp/amicashop/x51170144004/",
-      "media_url": "https://shop.r10s.jp/amicashop/cabinet/product/50/x51170144004_0_2b9e.jpg",
-      "width": 480,
-      "height": 480,
-      "view_type": "front_packshot_comparison",
-      "license_status": "restricted-research",
-      "notes": "Visually inspected. Commercial AD 200g comparison variant; retained as supporting evidence for Yamaya's 200g format, not asserted as the identical JAN/package to sku_l04."
-    },
-    {
-      "file": "images/akomeya_current_300g.jpg",
-      "sku": "sku_l04_comparison_current_300g",
-      "product": "やまや めんたいマヨネーズタイプ 300g",
-      "source_page": "https://item.rakuten.co.jp/akomeyatokyo/4903085062894/",
-      "media_url": "https://shop.r10s.jp/akomeyatokyo/cabinet/08950751/imgrc0090491371.jpg",
-      "width": 600,
-      "height": 600,
-      "view_type": "front_packshot_comparison",
-      "license_status": "restricted-research",
-      "notes": "Visually inspected. Current 300g retail comparison package; listing item number/JAN 4903085062894. Not sku_l04 because size and JAN differ."
-    },
-    {
-      "file": "images/yamaya_official_current_300g.webp",
-      "sku": "sku_l04_comparison_current_300g",
-      "product": "やまや めんたいマヨネーズタイプ 300g",
-      "source_page": "https://www.yamaya.com/products/detail/06385",
-      "media_url": "https://www.yamaya.com/assets_onlineshop/img/item_l/06385_00.webp",
-      "width": 750,
-      "height": 750,
-      "view_type": "official_front_packshot_and_unwrapped_bottle_comparison",
-      "license_status": "restricted-research",
-      "notes": "Visually inspected. Official Yamaya current 300g product image showing branded sleeve and bottle; confirms the package slogan is 明太子屋さんがつくった and the brand mark is 博多の味 やまや. Not sku_l04 because current size is 300g."
-    }
-  ]
-}
diff --git a/recognization/data/packshots/research/sku_l07/manifest.json b/recognization/data/packshots/research/sku_l07/manifest.json
deleted file mode 100644
index 808d375..0000000
--- a/recognization/data/packshots/research/sku_l07/manifest.json
+++ /dev/null
@@ -1,65 +0,0 @@
-{
-  "sku": "sku_l07",
-  "recommended_product_name": "丸和油脂 ラッキーマヨネーズ 200g",
-  "manufacturer": "丸和油脂株式会社",
-  "size": "200g",
-  "jan": "4978931401615",
-  "identity_conclusion": "Exact product identity is confirmed. The shared 600x600 source image and the retained 800x800 retailer image show the same current 200g Lucky Mayonnaise front package: cream squeeze pouch, red chick, red ラッキーマヨネーズ wordmark, and 調味料（アミノ酸）不使用 claim. Current 2026 retailer listings independently bind that package/product to 丸和油脂, 200g, and JAN 4978931401615.",
-  "existing_packshot": {
-    "file": "../../source/sku_l07.jpg",
-    "width": 600,
-    "height": 600,
-    "exact_product": true,
-    "current_as_of": "2026-09-29",
-    "currentness_basis": "Visually identical packaging appears in active 2026 Amazon, Rakuten, Yahoo Shopping, and Kakaku listings for the same maker, size, and JAN. The package also matches the repository shelf crop for sku_l07. No official manufacturer catalog page for this 200g retail SKU was reachable, so currentness is retailer-corroborated rather than manufacturer-catalog-confirmed.",
-    "source_page": "https://www.amazon.co.jp/dp/B0BHQW3VVP",
-    "media_url": "https://m.media-amazon.com/images/I/91ugfvmpCvL.jpg"
-  },
-  "validator_assessment": {
-    "result": "false_negative",
-    "ocr_score": 0.57,
-    "reason": "The front package intentionally prints ラッキーマヨネーズ and 調味料（アミノ酸）不使用 but not the manufacturer name or 200g size. OCR correctly reads the distinctive product name; the missing maker and size are absent from this front view, not evidence of a different SKU. Retailer title, JAN, seller metadata, and exact package appearance establish the identity externally. Refusing this image solely because full catalog-name token overlap is below 0.70 is therefore a validator false negative, not a real product mismatch."
-  },
-  "images": [
-    {
-      "file": "lucky_mayonnaise_200g_800.jpg",
-      "sku": "sku_l07",
-      "product": "丸和油脂 ラッキーマヨネーズ 200g",
-      "source_page": "https://item.rakuten.co.jp/kumano-nakatani/4978931401615-40/",
-      "media_url": "https://tshop.r10s.jp/kumano-nakatani/cabinet/pic1/4978931401615-40.jpg",
-      "width": 800,
-      "height": 800,
-      "view_type": "front_packshot",
-      "license_status": "restricted-research",
-      "notes": "Native unparameterized retailer image, visually inspected after download. It shows one exact 200g pouch despite the source page selling a 40-count case. The filename/item number embeds JAN 4978931401615. This is the best located exact image meeting the requested >=800px height; an apparent 1000px Rakuten fitin variant was only a server upscale of a 300x682 retailer asset and was rejected. Retailer copyright; retained for internal comparison/research only."
-    }
-  ],
-  "evidence": [
-    {
-      "source": "Amazon Japan",
-      "url": "https://www.amazon.co.jp/dp/B0BHQW3VVP",
-      "supports": "Product title 丸和油脂 ラッキーマヨネーズ 200g; brand 丸和油脂; shared image media ID 91ugfvmpCvL."
-    },
-    {
-      "source": "Rakuten / 味園サポート",
-      "url": "https://item.rakuten.co.jp/misono-support/a741-2/",
-      "supports": "Current product title, 200g size, JAN 4978931401615, and seller/manufacturer field 丸和油脂株式会社."
-    },
-    {
-      "source": "Yahoo Shopping / 御用蔵 大川",
-      "url": "https://store.shopping.yahoo.co.jp/goyougura-okawa/4978931401615-10.html",
-      "supports": "Current title 丸和油脂 ラッキーマヨネーズ 200g×10本 and JAN/ISBN 4978931401615."
-    },
-    {
-      "source": "価格.com",
-      "url": "https://search.kakaku.com/4978931401615/",
-      "supports": "Cross-retailer aggregation for 丸和油脂 ラッキーマヨネーズ 200g and explicit JAN 4978931401615 in multiple retailer records."
-    },
-    {
-      "source": "Rakuten / くまの中谷商店",
-      "url": "https://item.rakuten.co.jp/kumano-nakatani/4978931401615-40/",
-      "supports": "Source page for retained 800x800 media; title 丸和 ラッキーマヨネーズ 200g×40本 and item number 4978931401615-40."
-    }
-  ],
-  "research_date": "2026-09-29"
-}
diff --git a/recognization/data/packshots/research/sku_l08/manifest.json b/recognization/data/packshots/research/sku_l08/manifest.json
deleted file mode 100644
index c8edc68..0000000
--- a/recognization/data/packshots/research/sku_l08/manifest.json
+++ /dev/null
@@ -1,16 +0,0 @@
-{
- "sku": "sku_l08",
- "canonical_product": "スパイシーストーム ペリペリミックス 30g",
- "research_date": "2026-09-29",
- "conclusion": "Not sourced. No retailer listing for this product exists, so the row's name and size are themselves unverified.",
- "evidence": [
-  {
-   "source": "楽天市場 search",
-   "url": "https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0%20%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9%2030g/",
-   "captured": "rakuten-search-2026-09-29.html",
-   "supports": "The search returns 141 results, none of them this product. Every ペリペリ hit is a different brand: SPICEDREAM PERIPERI MIX 50g瓶 and SPICEDREAM PERIPERIMIXソース 195ml. The catalog's row claims 30g from a brand the marketplace does not carry."
-  }
- ],
- "why_it_matters": "sku_l08 is the one catalog row with no corroborating source. Its packshot is enrolled and the text lane can name it from a read, but nothing outside the repository confirms the name or the size, so a wrong label on this row would not be detectable from the catalog alone.",
- "images": []
-}
diff --git a/recognization/data/packshots/research/sku_l08/rakuten-search-2026-09-29.html b/recognization/data/packshots/research/sku_l08/rakuten-search-2026-09-29.html
deleted file mode 100644
index e19cacb..0000000
--- a/recognization/data/packshots/research/sku_l08/rakuten-search-2026-09-29.html
+++ /dev/null
@@ -1,114 +0,0 @@
-<!DOCTYPE html><html style="-webkit-text-size-adjust:100%" lang="ja"><head><meta http-equiv="X-UA-Compatible" content="IE=edge"/><meta charSet="utf-8"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/com/assets/domain-resources/favicon.ico"/><link rel="stylesheet" type="text/css" href="https://r.r10s.jp/com/assets/app/pages/search/css/pc-c814435ff9e7b9f7c753.bundle.css"/><link rel="stylesheet" type="text/css"/><meta property="dui:trace" content="d3459488-b62d-462b-b58d-3b132b035e58"/><title>【楽天市場】スパイシーストーム ペリペリミックス 30gの通販</title><meta name="description" content="楽天市場-「スパイシーストーム ペリペリミックス 30g」141件 人気の商品を価格比較・ランキング･レビューで検討できます。ご購入でポイント取得がお得。セール商品・送料無料商品も多数。「最強翌日配送」なら翌日お届けも可能です。"/><meta name="keywords" content="スパイシーストーム ペリペリミックス 30g,通販,インターネット,ショッピング,楽天市場"/><meta name="robots" content="noodp"/><meta property="og:image" content="https://r.r10s.jp/com/img/logo/rakuten_group_red.svg"/><meta property="dui:trace" 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type="application/json">{"bdg-point-display":{"standalone":false,"url":"//api-catalog-jpe-gateway.rakuten.co.jp/NCP/benefitsCalculation","apiKey":"KMJAiXNF62zLrHDYteTDcvRh72kw60I6","breakdown":{"addenda":{"sections":{"SPU":{"annotation":{"label":"{{staticContent.SPU_EXPLANATION}}"},"pointsStatus":{"bonusCampaignActive":"{{staticContent.SPU_BONUS_CAMPAIGN_ACTIVE}}"}}},"footer":{"USER":{"logo":"{{staticContent.BOTTOM_BANNER}}","destination":"{{staticContent.BOTTOM_BANNER_LINK}}","footnotes":[{"content":"{{staticContent.DISC_SOKUHAI_LIMITED}}"},{"content":"{{staticContent.HOMETOWN_TAX_POINT_DISC}}"},{"content":"{{staticContent.DISC_NO_POINT_USAGE}}"},{"content":"{{staticContent.DISC_POINT_WILL_BE_LIMITED}}"},{"content":"{{staticContent.DISC_POINT_LIMITED}}"},{"content":"{{staticContent.DISC_SPU_BONUS_EXPLANATION}}"},{"content":"{{staticContent.DISC_POINT_CALCULATION_DISCREPANCY}}"},{"content":"{{staticContent.POINT_LIMIT_EXPLANATION}}"},{"content":"{{staticContent.CARD_POINT_CALC_DISC}}"},{"content":"{{staticContent.CARD_POINT_CALC_EXPLANATION}}"}]},"USER_MULTI_SKU":{"logo":"{{staticContent.BOTTOM_BANNER}}","destination":"{{staticContent.BOTTOM_BANNER_LINK}}","footnotes":[{"content":"{{staticContent.DISC_SOKUHAI_LIMITED}}"},{"content":"{{staticContent.DISC_MULTI_SKU_ITEM_SELECTION}}"},{"content":"{{staticContent.DISC_NO_POINT_USAGE}}"},{"content":"{{staticContent.DISC_MULTI_SKU_POINT_WILL_BE_LIMITED}}"},{"content":"{{staticContent.DISC_MULTI_SKU_POINT_LIMIT_EXPLANATION}}"},{"content":"{{staticContent.CARD_POINT_CALC_EXPLANATION}}"}]},"USER_HTT_MULTI_SKU":{"logo":"{{staticContent.BOTTOM_BANNER}}","destination":"{{staticContent.BOTTOM_BANNER_LINK}}","footnotes":[{"content":"{{staticContent.HOMETOWN_TAX_POINT_DISC}}"},{"content":"{{staticContent.DISC_MULTI_SKU_ITEM_SELECTION}}"},{"content":"{{staticContent.DISC_POINT_WILL_BE_LIMITED}}"},{"content":"{{staticContent.DISC_POINT_LIMITED}}"},{"content":"{{staticContent.CARD_POINT_CALC_DISC}}"},{"content":"{{staticContent.CARD_POINT_CALC_EXPLANATION}}"}]},"ANONYMOUS":{"logo":"{{staticContent.BOTTOM_BANNER}}","destination":"{{staticContent.BOTTOM_BANNER_LINK}}","footnotes":[{"content":"{{staticContent.HOMETOWN_TAX_POINT_DISC}}"},{"content":"{{staticContent.DISC_NO_USER}}"}]},"ANONYMOUS_MULTI_SKU":{"logo":"{{staticContent.BOTTOM_BANNER}}","destination":"{{staticContent.BOTTOM_BANNER_LINK}}","footnotes":[{"content":"{{staticContent.HOMETOWN_TAX_POINT_DISC}}"},{"content":"{{staticContent.DISC_MULTI_SKU_ITEM_SELECTION}}"},{"content":"{{staticContent.DISC_NO_USER}}"}]}}}},"hometownTaxPopup":{"addenda":{"sections":{"SPU":{"annotation":{"heading":"{{staticContent.RAKUTEN_CARD_ANNOUNCEMENT}}","totalPointRateStatic":"{{staticContent.TOTAL_POINT_RATE_INFO_STATIC}}","totalPointRateDynamic":"{{staticContent.TOTAL_POINT_RATE_INFO_DYNAMIC}}","httPointDescription":"{{staticContent.HOMETOWN_TAX_POINT_DESCRIPTION}}","httPointInfo":"{{staticContent.HOMETOWN_TAX_POINT_INFO}}"}}},"footer":{"USER":{"footnotes":[{"content":"{{staticContent.HOMETOWN_TAX_POINT_EXPLANATION}}"},{"content":"{{staticContent.HOMETOWN_TAX_POINT_DISC}}"},{"content":"{{staticContent.POINT_LIMIT_EXPLANATION}}"},{"content":"{{staticContent.DISC_POINT_LIMITED}}"},{"content":"{{staticContent.CARD_POINT_CALC_EXPLANATION}}"}]}}},"staticContent":[{"content":"{{staticContent.HOMETOWN_TAX_POINT_INFO}}"},{"content":"{{staticContent.HOMETOWN_TAX_POINT_DISC}}"}]}}}</script><script type="text/template" data-component-template="bdg-point-display" data-name="point-breakdown"><div class="point-breakdown"><div class="close-button">✕</div><article class="content"><header><div class="title">獲得予定ポイント</div><div class="subtitle">合計<span class="points"> {{points}} pt</span></div></header><section class="sections">{{sections}}</section><footer><ol class="footnotes annotation">{{#list <li>{{content}}</li> footer.footnotes}}</ol>{{footer.footerLink}}</footer></article></div></script><script type="text/template" data-component-template="bdg-point-display" data-name="point-breakdown-loading" data-component="point-breakdown"><div class="loader"><div></div><div></div><div></div></div></script><script type="text/template" data-component-template="bdg-point-display" data-name="point-breakdown-error" data-component="point-breakdown"><div class="message"><h4>読み込みに失敗しました</h4><div class="content">ただいま、一時的に読み込みに時間がかかっています。時間をおいて再度お試しください。</div><div class="quit-button">OK</div></div></script><script type="text/template" data-component-template="bdg-point-display" data-name="point-breakdown-section" data-component="point-breakdown"><div class="section"><h3>{{logo}}<div class="title">{{caption}}</div>{{annotation}}</h3><div class="items">{{items}}</div></div></script><script type="text/template" data-component-template="bdg-point-display" data-name="point-breakdown-section-annotation" data-component="point-breakdown"><a href="{{destination}}" class="subtitle annotation"><img src="{{staticContent.QUESTION_MARK_ICON}}" alt=""/>{{label}}</a></script><script type="text/template" data-name="point-breakdown-footer-link" data-component-template="bdg-point-display" data-component="point-breakdown"><a href="{{destination}}"><img src="{{logo}}" class="logo"/></a></script><script type="text/template" data-component-template="bdg-point-display" data-name="point-breakdown-group" data-component="point-breakdown"><div class="row"><div class="group">{{logo}}<span class="caption">{{caption}}</span><div class="items">{{items}}</div></div></div></script><script type="text/template" data-component-template="bdg-point-display" data-name="point-breakdown-item" data-component="point-breakdown"><div class="row"><div class="item">{{logo}}<!-- -->{{caption}}<!-- -->{{points}}<span class="description">{{description}}</span></div></div></script><script type="text/template" data-component-template="bdg-point-display" data-name="point-breakdown-item-points" data-component="point-breakdown"><span class="points subtitle">{{points}} pt</span></script><script type="text/template" data-component-template="bdg-point-display" data-name="point-breakdown-item-points-limit-almost-reached" data-component="point-breakdown"><span class="points subtitle">{{points}} pt<sup class="super-text">*1</sup></span></script><script type="text/template" data-component-template="bdg-point-display" data-name="point-breakdown-item-points-full" data-component="point-breakdown"><span class="subtitle">上限達成済み<sup class="super-text">*2</sup></span></script><script type="text/template" data-component-template="bdg-point-display" data-name="point-breakdown-item-caption" data-component="point-breakdown"><span class="caption">{{caption}}</span></script><script type="text/template" data-component-template="bdg-point-display" data-name="point-breakdown-item-spu-bonus-caption" data-component="point-breakdown"><span class="caption">{{caption}}<sup class="super-text">*3</sup></span></script><script type="text/template" data-component-template="bdg-point-display" data-name="point-breakdown-item-points-full-spu-bonus-active" data-component="point-breakdown"><span class="points subtitle">{{bonusCampaignActive}}</span></script><script type="text/template" data-component-template="bdg-point-display" data-name="point-breakdown-item-points-unqualified" data-component="point-breakdown"><span class="points subtitle">未エントリー</span></script><script type="text/template" data-component-template="bdg-point-display" data-name="point-breakdown-item-link" data-component="point-breakdown"><a class="link" href="{{destination}}">詳しくはこちら</a></script><script type="text/template" data-component-template="bdg-point-display" data-name="point-breakdown-logo" data-component="point-breakdown"><img src="{{source}}" alt=""/></script><script type="text/template" data-component-template="bdg-point-display" data-name="point-summary"><div class="bdg-point-display-summary point-summary"><img class="p-icon point-icon" src="https://r.r10s.jp/com/inc/home/20080930/ris/img/spux/icon_point.png" alt=""/><div class="total"><span>{{points}}</span>ポイント</div>{{multiplier}}{{multiplierUp}}{{rebate}}<div class="link breakdown-link">内訳を見る</div>{{annotations}}</div></script><script type="text/template" data-component-template="bdg-point-display" data-name="point-summary-with-range" data-component="point-summary"><div class="bdg-point-display-summary point-summary"><img class="p-icon point-icon" src="https://r.r10s.jp/com/inc/home/20080930/ris/img/spux/icon_point.png" alt=""/><div class="total"><span>{{points}}～{{maxPoints}}</span>ポイント</div>{{multiplier}}{{multiplierUp}}<div class="link breakdown-link"></div><div style="display:block">{{rebate}}</div>{{annotations}}</div></script><script type="text/template" data-component-template="bdg-point-display" data-name="point-summary-loading" data-component="point-summary"><div class="bdg-point-display-summary loading"><img class="p-icon point-icon" src="https://r.r10s.jp/com/inc/home/20080930/ris/img/spux/icon_point.png" alt=""/><div></div><div></div><div></div></div></script><script type="text/template" data-component-template="bdg-point-display" data-name="point-summary-error" data-component="point-summary"><div class="message error">ポイント情報の取得に失敗しました。獲得予定ポイントが正しく表示されなかったため、時間をおいて、再度お試しください。</div></script><script type="text/template" data-component-template="bdg-point-display" data-name="point-multiplier" data-component="point-summary"><ul class="campaign multiplier"><li>{{rate}}<!-- -->倍</li></ul></script><script type="text/template" data-component-template="bdg-point-display" data-name="point-multiplier-range" data-component="point-summary"><ul class="campaign multiplier"><li>{{rate}}～{{maxRate}}<!-- -->倍</li></ul></script><script type="text/template" data-component-template="bdg-point-display" data-name="point-multiplier-up" data-component="point-summary"><ul class="campaign multiplier-up"><li class="multiplier">{{rate}}<!-- -->倍</li><li>{{bonus}}<!-- -->倍UP</li></ul></script><script type="text/template" data-component-template="bdg-point-display" data-name="point-multiplier-up-range" data-component="point-summary"><ul class="campaign multiplier-up"><li class="multiplier">{{rate}}<!-- -->倍</li><li>{{bonus}}<!-- -->倍UP～<!-- -->{{maxRate}}<!-- -->倍+<!-- -->{{bonus}}<!-- -->倍UP</li></ul></script><script type="text/template" data-component-template="bdg-point-display" data-name="point-multiplier-up-zero-point-range" data-component="point-summary"><ul class="campaign multiplier-up"><li class="multiplier">{{rate}}<!-- -->倍～<!-- -->{{maxRate}}<!-- -->倍</li><li>{{bonus}}<!-- -->倍UP</li></ul></script><script type="text/template" data-component-template="bdg-point-display" data-name="point-multiplier-up-annotation" data-component="point-summary"><ul class="annotations">{{#list <li>ポイントアップ期間：<!-- -->{{endDate}}<!-- -->まで</li> distributions}}</ul></script><script type="text/template" data-component-template="bdg-point-display" data-name="point-rebate" data-component="point-summary"><ul class="campaign rebate"><li class="multiplier">{{rate}}<!-- -->倍</li><li>{{rebate}}<!-- -->ポイントバック</li></ul></script><script type="text/template" data-component-template="bdg-point-display" data-name="point-rebate-range" data-component="point-summary"><ul class="campaign rebate"><li class="multiplier">{{rate}}<!-- -->倍</li><li>{{rebate}}<!-- -->ポイントバック～<!-- -->{{maxRate}}<!-- -->倍+ <!-- -->{{rebate}}<!-- -->ポイントバック</li></ul></script><script type="text/template" data-component-template="bdg-point-display" data-name="point-rebate-zero-point-range" data-component="point-summary"><ul class="campaign rebate"><li class="multiplier">{{rate}}<!-- -->倍～<!-- -->{{maxRate}}<!-- -->倍</li><li>{{rebate}}<!-- -->ポイントバック</li></ul></script><script type="text/template" data-component-template="bdg-point-display" data-name="point-rebate-annotation" data-component="point-summary"><ul class="annotations">{{#list <li><span>スーパーDEAL +<!-- -->{{value}}<!-- -->%ポイントバック対象期間：</span><span>{{startDate}} ～ {{endDate}}</span></li> distributions}}</ul></script><script type="text/template" data-component-template="bdg-point-display" data-name="point-breakdown-with-range" data-component="point-breakdown"><div class="bdg-point-display-breakdown point-breakdown"><div class="close-button">✕</div><article class="content"><section class="sections">{{sections}}</section><footer><ol class="footnotes annotation">{{#list <li>{{content}}</li> footer.footnotes}}</ol>{{footer.footerLink}}</footer></article></div></script><script type="text/template" data-name="point-breakdown-footer-link" data-component-template="bdg-point-display" data-component="point-breakdown"><a href="{{destination}}"><img src="{{logo}}" class="logo"/></a></script><script type="text/template" data-component-template="bdg-point-display" data-name="hometown-tax-popup"><div class="hometown-tax-popup"><div class="close-button">✕</div><div class="static-content">{{#list <span>{{content}}</span> staticContent}}</div></div></script><script type="text/template" data-component-template="bdg-point-display" data-name="hometown-tax-popup-loading" data-component="hometown-tax-popup"><div class="loading"><div></div><div></div><div></div></div></script><script type="text/template" data-component-template="bdg-point-display" data-name="hometown-tax-popup-error" data-component="hometown-tax-popup"><div class="message"><h4>読み込みに失敗しました</h4><div class="content">ただいま、一時的に読み込みに時間がかかっています。時間をおいて再度お試しください。</div><div class="quit-button">OK</div></div></script><script type="text/template" data-component-template="bdg-point-display" data-name="hometown-tax-popup-card-user" data-component="hometown-tax-popup"><div class="bdg-point-display-breakdown hometown-tax-popup card-user-popup"><div class="close-button">✕</div><article class="content"><div class="announcement-title">{{header.heading}}</div><div class="info-box"><svg xmlns="http://www.w3.org/2000/svg" width="13" height="13" viewBox="0 0 13 13" fill="none"><path fill-rule="evenodd" clip-rule="evenodd" d="M6.125 4.375C5.76275 4.375 5.46875 4.081 5.46875 3.71875C5.46875 3.3565 5.76275 3.0625 6.125 3.0625C6.48725 3.0625 6.78125 3.3565 6.78125 3.71875C6.78125 4.081 6.48725 4.375 6.125 4.375ZM5.6875 9.1875H6.5625V5.25H5.6875V9.1875ZM6.125 0C2.74225 0 0 2.74225 0 6.125C0 9.50775 2.74225 12.25 6.125 12.25C9.50775 12.25 12.25 9.50775 12.25 6.125C12.25 2.74225 9.50775 0 6.125 0Z" fill="#4DC4FF"></path></svg><span>{{header.httPointInfo}}</span></div><div class="rate-heading-wrap">{{header.totalPointRateStatic}}<span class="rate-value">{{header.totalPointRateDynamic}}</span></div><p class="rate-description">{{header.httPointDescription}}</p><section class="sections">{{sections}}</section><footer><ol class="footnotes">{{#list <li>{{content}}</li> footer.footnotes}}</ol></footer></article></div></script><script type="text/template" data-component-template="bdg-point-display" data-name="hometown-tax-popup-section" data-component="hometown-tax-popup"><div class="section"><div class="items">{{items}}</div></div></script><script type="text/template" data-component-template="bdg-point-display" data-name="hometown-tax-popup-group" data-component="hometown-tax-popup"><div class="row"><div class="group">{{logo}}<span class="caption">{{caption}}</span><div class="items">{{items}}</div></div></div></script><script type="text/template" data-component-template="bdg-point-display" data-name="hometown-tax-popup-item" data-component="hometown-tax-popup"><div class="row"><div class="item">{{logo}}<!-- -->{{points}}<!-- -->{{caption}}<span class="description">{{description}}</span></div></div></script><script type="text/template" data-component-template="bdg-point-display" data-name="hometown-tax-popup-item-points" data-component="hometown-tax-popup"><span class="points subtitle">+<!-- -->{{rate}}<!-- -->倍</span></script><script type="text/template" data-component-template="bdg-point-display" data-name="hometown-tax-popup-item-points-full" data-component="hometown-tax-popup"><span class="subtitle">上限達成済み<sup class="super-text">*1</sup></span></script><script type="text/template" data-component-template="bdg-point-display" data-name="hometown-tax-popup-item-caption" data-component="hometown-tax-popup"><span class="caption">{{caption}}</span></script><script type="text/template" data-component-template="bdg-point-display" data-name="hometown-tax-popup-item-points-unqualified" data-component="hometown-tax-popup"><span class="points subtitle">未エントリー</span></script><script type="text/template" data-component-template="bdg-point-display" data-name="hometown-tax-popup-item-link" data-component="hometown-tax-popup"><a class="link" href="{{destination}}">詳しくはこちら</a></script><script type="text/template" data-component-template="bdg-point-display" data-name="hometown-tax-popup-logo" data-component="hometown-tax-popup"><img src="{{source}}"/></script><link rel="stylesheet" type="text/css" href="https://r.r10s.jp/com/js/d/bdg/2.2/bdg.css"/><script src="https://r.r10s.jp/com/js/d/bdg/2.2/vendor-2.2.0.min.js"></script></div><div class="dui-container nav"><div class="dui-container infobar"><div class="dui-menu"><div class="breadcrumbClient"><div class="item breadcrumb-model breadcrumb -fluid" style="--navWidth:auto" data-comp-id-flat="breadcrumb"><nav class="breadcrumbs-container-wrapper--KiLsJ"><ul class="breadcrumbs-container--2qMEY breadcrumbs-container-overflow-scroll--Kwv88"><li class="breadcrumb-item--1KUNX"><a href="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/" class="breadcrumbs-name--1ahfw" aria-current="page">すべてのジャンル</a></li></ul><ul class="breadcrumbs-container--2qMEY hidden-container--2M5n7" aria-hidden="true"><li class="breadcrumb-item--1KUNX"><a href="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/" class="breadcrumbs-name--1ahfw" aria-current="page">すべてのジャンル</a></li></ul></nav><div class="divider no-right-margin"><div class="icon--2QZm2 color-gray--2OAj4 common-chevron-right--yQPT4" style="font-size:12px"></div></div><span class="search-query-wrapper--3A-Pw"><span>「</span><span class="search-query--2WBls">スパイシーストーム ペリペリミックス 30g</span><span>」の検索結果</span></span><span><span class="text-container--2tSUW default-size--1kRiQ default-color--2fMVe" style="white-space:nowrap">1〜45件 （141件）</span></span></div></div><div class="navShipping"><div class="dui-menu _rightfloated prefecturefilter"><span class="item"><i class="dui-icon -location"></i>お届け先</span><div class="item"><form id="prefecture-filter-form" autoComplete="off" class="dui-form japan" method="get" action="https://search.rakuten.co.jp/search/mall"><input type="hidden" name="sitem" value="スパイシーストーム ペリペリミックス 30g"/><input type="hidden" name="s" value="1"/><input type="hidden" name="set" value="pref"/><div class="inlinefields"><div class="field"><div class="container--3jeM2"><select id="prefecture-filter-select" class="select--3dzR5 size-s--3zCUs border-radius-all--2LwW9" style="padding-left:" name="pref"><optgroup label="北海道"><option value="hokkaido" aria-label="北海道">北海道</option></optgroup><optgroup label="東北"><option value="aomori" aria-label="青森">青森</option><option value="iwate" aria-label="岩手">岩手</option><option value="miyagi" aria-label="宮城">宮城</option><option value="akita" aria-label="秋田">秋田</option><option value="yamagata" aria-label="山形">山形</option><option value="fukushima" aria-label="福島">福島</option></optgroup><optgroup label="甲信越"><option value="niigata" aria-label="新潟">新潟</option><option value="nagano" aria-label="長野">長野</option><option value="yamanashi" aria-label="山梨">山梨</option></optgroup><optgroup label="関東"><option selected="" value="tokyo" aria-label="東京">東京</option><option value="kanagawa" aria-label="神奈川">神奈川</option><option value="saitama" aria-label="埼玉">埼玉</option><option value="chiba" aria-label="千葉">千葉</option><option value="ibaraki" aria-label="茨城">茨城</option><option value="tochigi" aria-label="栃木">栃木</option><option value="gunma" aria-label="群馬">群馬</option></optgroup><optgroup label="北陸"><option value="toyama" aria-label="富山">富山</option><option value="ishikawa" aria-label="石川">石川</option><option value="fukui" aria-label="福井">福井</option></optgroup><optgroup label="東海"><option value="aichi" aria-label="愛知">愛知</option><option value="gifu" aria-label="岐阜">岐阜</option><option value="shizuoka" aria-label="静岡">静岡</option><option value="mie" aria-label="三重">三重</option></optgroup><optgroup label="関西"><option value="osaka" aria-label="大阪">大阪</option><option value="hyogo" aria-label="兵庫">兵庫</option><option value="kyoto" aria-label="京都">京都</option><option value="shiga" aria-label="滋賀">滋賀</option><option value="nara" aria-label="奈良">奈良</option><option value="wakayama" aria-label="和歌山">和歌山</option></optgroup><optgroup label="中国"><option value="tottori" aria-label="鳥取">鳥取</option><option value="shimane" aria-label="島根">島根</option><option value="okayama" aria-label="岡山">岡山</option><option value="hiroshima" aria-label="広島">広島</option><option value="yamaguchi" aria-label="山口">山口</option></optgroup><optgroup label="四国"><option value="tokushima" aria-label="徳島">徳島</option><option value="kagawa" aria-label="香川">香川</option><option value="ehime" aria-label="愛媛">愛媛</option><option value="kochi" aria-label="高知">高知</option></optgroup><optgroup label="九州"><option value="fukuoka" aria-label="福岡">福岡</option><option value="saga" aria-label="佐賀">佐賀</option><option value="miyazaki" aria-label="宮崎">宮崎</option><option value="nagasaki" aria-label="長崎">長崎</option><option value="oita" aria-label="大分">大分</option><option value="kumamoto" aria-label="熊本">熊本</option><option value="kagoshima" aria-label="鹿児島">鹿児島</option></optgroup><optgroup label="沖縄"><option value="okinawa" aria-label="沖縄">沖縄</option></optgroup></select><div class="icon--3eVQK"><div class="icon--2QZm2 size-m--3Rirc color-gray--2OAj4 common-chevron-down--2Nuzd"></div></div></div></div><div class="field" style="display:none"><button class="dui-button" type="submit"><i class="dui-icon -search"></i>検索</button></div><noscript><div class="field"><button class="dui-button" type="submit"><i class="dui-icon -search"></i>検索</button></div></noscript></div></form><input type="hidden" value="not private"/><script type="text/javascript">
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href="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/100227/" class="item" title="食品"><div class="_ellipsis">食品</div></a><div class="dui-list -child"></div><a href="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/101164/" class="item" title="ホビー"><div class="_ellipsis">ホビー</div></a><div class="dui-list -child"></div><a href="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/551167/" class="item" title="スイーツ・お菓子"><div class="_ellipsis">スイーツ・お菓子</div></a><div class="dui-list -child"></div><a href="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/100938/" class="item" title="ダイエット・健康"><div class="_ellipsis">ダイエット・健康</div></a><div class="dui-list -child"></div><a href="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/562637/" class="item toggle" title="家電" style="display:none"><div class="_ellipsis">家電</div></a><div class="dui-list -child"></div><a href="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/200162/" class="item toggle" title="本・雑誌・コミック" style="display:none"><div class="_ellipsis">本・雑誌・コミック</div></a><div class="dui-list 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class="_ellipsis">メンズファッション</div></a><div class="dui-list -child"></div><a href="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/558944/" class="item toggle" title="キッチン用品・食器・調理器具" style="display:none"><div class="_ellipsis">キッチン用品・食器・調理器具</div></a><div class="dui-list -child"></div><a href="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/216131/" class="item toggle" title="バッグ・小物・ブランド雑貨" style="display:none"><div class="_ellipsis">バッグ・小物・ブランド雑貨</div></a><div class="dui-list -child"></div><div class="extra content"><span class="toggle _link more" role="button" tabindex="0">さらに表示</span></div></div></div></div></div></div></div></div><div class="item"><div class="dui-container pricefilter" data-comp-id-flat="left_navi-price_filter"><div class="dui-filter-menu"><div class="header" title="商品価格"><span>商品価格</span></div><div class="content"><div class="price histogram fixed" role="presentation"><div class="histogram bar" style="width:4.761904761904762%" data-text="0&lt;small&gt;円&lt;/small&gt;" data-idx="0" data-count="1件" data-from="0" data-to="639" role="button" tabindex="0"><div data-count="1" style="height:5%"></div></div><div class="histogram bar" style="width:4.761904761904762%" data-text="640&lt;small&gt;円&lt;/small&gt;" data-idx="1" data-count="4件" data-from="640" data-to="999" role="button" tabindex="0"><div data-count="4" style="height:20%"></div></div><div class="histogram bar" style="width:4.761904761904762%" data-text="1,000&lt;small&gt;円&lt;/small&gt;" data-idx="2" data-count="20件" data-from="1000" data-to="1439" role="button" tabindex="0"><div data-count="20" style="height:100%"></div></div><div class="histogram bar" style="width:4.761904761904762%" data-text="1,440&lt;small&gt;円&lt;/small&gt;" data-idx="3" data-count="19件" data-from="1440" data-to="1959" role="button" tabindex="0"><div data-count="19" style="height:95%"></div></div><div class="histogram bar" style="width:4.761904761904762%" data-text="1,960&lt;small&gt;円&lt;/small&gt;" data-idx="4" data-count="8件" data-from="1960" data-to="2559" role="button" tabindex="0"><div data-count="8" style="height:40%"></div></div><div class="histogram bar" style="width:4.761904761904762%" data-text="2,560&lt;small&gt;円&lt;/small&gt;" data-idx="5" data-count="18件" data-from="2560" data-to="3239" role="button" tabindex="0"><div data-count="18" style="height:90%"></div></div><div class="histogram bar" style="width:4.761904761904762%" data-text="3,240&lt;small&gt;円&lt;/small&gt;" data-idx="6" data-count="13件" data-from="3240" data-to="3999" role="button" tabindex="0"><div data-count="13" style="height:65%"></div></div><div class="histogram bar" style="width:4.761904761904762%" data-text="4,000&lt;small&gt;円&lt;/small&gt;" data-idx="7" data-count="1件" data-from="4000" data-to="4839" role="button" tabindex="0"><div data-count="1" style="height:5%"></div></div><div class="histogram bar" style="width:4.761904761904762%" data-text="4,840&lt;small&gt;円&lt;/small&gt;" data-idx="8" data-count="8件" data-from="4840" data-to="5759" role="button" tabindex="0"><div data-count="8" style="height:40%"></div></div><div class="histogram bar" style="width:4.761904761904762%" data-text="5,760&lt;small&gt;円&lt;/small&gt;" data-idx="9" data-count="9件" data-from="5760" data-to="6759" role="button" tabindex="0"><div data-count="9" style="height:45%"></div></div><div class="histogram bar" style="width:4.761904761904762%" data-text="6,760&lt;small&gt;円&lt;/small&gt;" data-idx="10" data-count="10件" data-from="6760" data-to="7839" role="button" tabindex="0"><div data-count="10" style="height:50%"></div></div><div class="histogram bar" style="width:4.761904761904762%" data-text="7,840&lt;small&gt;円&lt;/small&gt;" data-idx="11" data-count="7件" data-from="7840" data-to="8999" role="button" tabindex="0"><div data-count="7" style="height:35%"></div></div><div class="histogram bar" style="width:4.761904761904762%" data-text="9,000&lt;small&gt;円&lt;/small&gt;" data-idx="12" data-count="7件" data-from="9000" data-to="10239" role="button" tabindex="0"><div data-count="7" style="height:35%"></div></div><div class="histogram bar" style="width:4.761904761904762%" data-text="10,240&lt;small&gt;円&lt;/small&gt;" data-idx="13" data-count="9件" data-from="10240" data-to="11559" role="button" tabindex="0"><div data-count="9" style="height:45%"></div></div><div class="histogram bar" style="width:4.761904761904762%" data-text="11,560&lt;small&gt;円&lt;/small&gt;" data-idx="14" data-count="3件" data-from="11560" data-to="12959" role="button" tabindex="0"><div data-count="3" style="height:15%"></div></div><div class="histogram bar" style="width:4.761904761904762%" data-text="12,960&lt;small&gt;円&lt;/small&gt;" data-idx="15" data-count="3件" data-from="12960" data-to="14439" role="button" tabindex="0"><div data-count="3" style="height:15%"></div></div><div class="histogram bar" style="width:4.761904761904762%" data-text="14,440&lt;small&gt;円&lt;/small&gt;" data-idx="16" data-count="1件" data-from="14440" data-to="17639" role="button" tabindex="0"><div data-count="1" style="height:5%"></div></div><div class="histogram bar" style="width:4.761904761904762%" data-text="17,640&lt;small&gt;円&lt;/small&gt;" data-idx="17" data-count="1件" data-from="17640" data-to="19359" role="button" tabindex="0"><div data-count="1" style="height:5%"></div></div><div class="histogram bar" style="width:4.761904761904762%" data-text="19,360&lt;small&gt;円&lt;/small&gt;" data-idx="18" data-count="2件" data-from="19360" data-to="23039" role="button" tabindex="0"><div data-count="2" style="height:10%"></div></div><div class="histogram bar" style="width:4.761904761904762%" data-text="23,040&lt;small&gt;円&lt;/small&gt;" data-idx="19" data-count="3件" data-from="23040" data-to="31359" role="button" tabindex="0"><div data-count="3" style="height:15%"></div></div><div class="histogram bar" style="width:4.761904761904762%" data-text="31,360&lt;small&gt;円&lt;/small&gt;" data-idx="20" data-count="2件" data-from="31360" role="button" tabindex="0"><div data-count="2" style="height:10%"></div></div><a class="dui-button -pointing" style="display:none"><div class="price"></div><span class="count"></span></a></div><div class="sliderCtrlContainer"><div class="sliderCtrlBar"><div class="slider hover"></div><div class="slider control left" role="slider" tabindex="0" aria-valuenow="0" aria-label="Decrease price range"></div><div class="slider control right" role="slider" tabindex="0" aria-valuenow="0" aria-label="Increase price range"></div></div></div><form id="price-filter" class="dui-form" action="https://search.rakuten.co.jp/search/mall"><input type="hidden" name="sitem" value="スパイシーストーム ペリペリミックス 30g"/><input type="hidden" name="p" value="0"/><input type="hidden" name="s" value="1"/><input type="hidden" value="not private"/><div class="inlinefields -fluid"><div class="field"><input type="number" value="" autoComplete="off" class="price input" name="min" id="min-price-filter" aria-label="min price filter" placeholder="円" maxLength="9"/></div><div class="field separator">~</div><div class="field"><input type="number" value="" autoComplete="off" class="price input" name="max" id="max-price-filter" aria-label="max price filter" placeholder="円" maxLength="9"/></div></div><button class="dui-button -fluid" type="submit" disabled=""><i class="dui-icon -search"></i>検索</button></form></div></div></div></div><div class="item"></div><div class="item"></div><div class="item"></div><div class="item"><div class="dui-container optionfilter" data-track-enhanced_filter="[&quot;101833_1000984&quot;,&quot;101833_1000111&quot;]" data-track-enhanced_filter:tag_id="[]" data-comp-id-flat="left_navi-enhanced_filter"><div class="dui-filter-menu -opened"><div class="header -clickable" title="原産国/製造国" role="button" tabindex="0"><div class="dui-icon"><div class="icon--2QZm2 size-m--3Rirc color-gray--2OAj4 common-chevron-down--2Nuzd"></div></div><div class="_ellipsis">原産国/製造国</div></div><div class="content"><div class="dui-list"><a href="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/-/tg1011176/" class="item" title="43件">アメリカ</a><a href="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/-/tg1011174/" class="item" title="5件">日本</a><a href="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/-/tg1050542/" class="item" title="4件">北マケドニア</a><a href="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/-/tg1011172/" class="item" title="3件">ベトナム</a><a href="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/-/tg1050626/" class="item" title="1件">ペルー</a><a href="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/-/tg1011173/" class="item" title="1件">中国</a></div></div></div></div></div><div class="item"><div class="dui-container optionfilter"><div class="dui-container colorfilter"><div class="dui-filter-menu"><div class="header" title="カラー"><span class="_ellipsis">カラー</span></div><div class="content"><div class="dui-color-filter"><a class="dui-color" style="background-color:#333333" href="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/-/tg1000886/" title="ブラック" aria-label="ブラック"></a><a class="dui-color" style="background-color:#999999" href="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/-/tg1000881/" title="グレー" aria-label="グレー"></a><a class="dui-color" style="background-color:#ffb1bf" href="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/-/tg1000876/" title="ピンク" aria-label="ピンク"></a><a class="dui-color" style="background-color:#d62525" href="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/-/tg1000877/" title="レッド" aria-label="レッド"></a><a class="dui-color" style="background-color:#6c1e98" href="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/-/tg1000882/" title="パープル" aria-label="パープル"></a><a class="dui-color -clear" href="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/-/tg1016062/" title="透明">✔</a><a class="dui-color -multi-color" href="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/-/tg1000887/" title="マルチカラー">✔</a></div></div></div></div></div></div><div class="item"><div class="dui-container shippingfilter"><div><div class="dui-filter-menu japan"><div class="header" title="配送"><span class="_ellipsis">配送<a href="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?country=99" class="_blue _link" data-switch="international"><i class="dui-icon 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label="甲信越"><option value="niigata" aria-label="新潟">新潟</option><option value="nagano" aria-label="長野">長野</option><option value="yamanashi" aria-label="山梨">山梨</option></optgroup><optgroup label="関東"><option selected="" value="tokyo" aria-label="東京">東京</option><option value="kanagawa" aria-label="神奈川">神奈川</option><option value="saitama" aria-label="埼玉">埼玉</option><option value="chiba" aria-label="千葉">千葉</option><option value="ibaraki" aria-label="茨城">茨城</option><option value="tochigi" aria-label="栃木">栃木</option><option value="gunma" aria-label="群馬">群馬</option></optgroup><optgroup label="北陸"><option value="toyama" aria-label="富山">富山</option><option value="ishikawa" aria-label="石川">石川</option><option value="fukui" aria-label="福井">福井</option></optgroup><optgroup label="東海"><option value="aichi" aria-label="愛知">愛知</option><option value="gifu" aria-label="岐阜">岐阜</option><option value="shizuoka" aria-label="静岡">静岡</option><option value="mie" aria-label="三重">三重</option></optgroup><optgroup label="関西"><option value="osaka" aria-label="大阪">大阪</option><option value="hyogo" aria-label="兵庫">兵庫</option><option value="kyoto" aria-label="京都">京都</option><option value="shiga" aria-label="滋賀">滋賀</option><option value="nara" aria-label="奈良">奈良</option><option value="wakayama" aria-label="和歌山">和歌山</option></optgroup><optgroup label="中国"><option value="tottori" aria-label="鳥取">鳥取</option><option value="shimane" aria-label="島根">島根</option><option value="okayama" aria-label="岡山">岡山</option><option value="hiroshima" aria-label="広島">広島</option><option value="yamaguchi" aria-label="山口">山口</option></optgroup><optgroup label="四国"><option value="tokushima" aria-label="徳島">徳島</option><option value="kagawa" aria-label="香川">香川</option><option value="ehime" aria-label="愛媛">愛媛</option><option value="kochi" aria-label="高知">高知</option></optgroup><optgroup label="九州"><option value="fukuoka" aria-label="福岡">福岡</option><option value="saga" aria-label="佐賀">佐賀</option><option value="miyazaki" aria-label="宮崎">宮崎</option><option value="nagasaki" aria-label="長崎">長崎</option><option value="oita" aria-label="大分">大分</option><option value="kumamoto" aria-label="熊本">熊本</option><option value="kagoshima" aria-label="鹿児島">鹿児島</option></optgroup><optgroup label="沖縄"><option value="okinawa" aria-label="沖縄">沖縄</option></optgroup></select><div class="icon--3eVQK"><div class="icon--2QZm2 size-m--3Rirc color-gray--2OAj4 common-chevron-down--2Nuzd"></div></div></div></div></div><button class="dui-button -fluid" type="submit" style="display:none"><i class="dui-icon -search"></i>検索</button><noscript><button class="dui-button -fluid" type="submit"><i class="dui-icon -search"></i>検索</button></noscript></form></div></div></div></div><div class="dui-container single-choice-radio-filter sub-component" data-track-filters="" data-comp-id-flat="left_navi-delivery_date_filter"><div class="dui-filter-menu"><label class="grouped-fields-label--3QTmu">お届け日</label><div class="content"><form class="dui-form" method="get" action="https://search.rakuten.co.jp/search/mall" id="delivery-day-form" autoComplete="off"><input type="hidden" name="sitem" value="スパイシーストーム ペリペリミックス 30g"/><input type="hidden" name="p" value="0"/><input type="hidden" name="s" value="1"/><input type="submit" role="button" aria-label="submit" style="display:none"/><div class="grouped-fields--2W62H"><div><label class="field-label--3DBLC"><input type="radio" role="radio" aria-label="すべて" name="filter" id="delivery-day-0" class="field-input--3KtK4" checked=""/><span class="field-name--CxUgM">すべて</span></label></div><div><label class="field-label--3DBLC"><input type="radio" role="radio" aria-label="翌日届く" name="filter" value="ndd" id="delivery-day-1" class="field-input--3KtK4"/><span class="field-name--CxUgM">翌日届く</span></label></div><div><label class="field-label--3DBLC"><input type="radio" role="radio" aria-label="翌々日までに届く" name="filter" value="nndd" id="delivery-day-2" class="field-input--3KtK4"/><span class="field-name--CxUgM">翌々日までに届く</span></label></div></div></form><input type="hidden" value="not private"/><script type="text/javascript">
-                    window.addEventListener('fingerprinting-delivery-day', () => {
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default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="title--YwZbp"><div class="title-text--ciIN1"><span class="text-container--2tSUW size-body-3-low--3HhfO style-bold--1IVlx default-color--2fMVe">最強翌日配送とは</span></div></div><div class="content--16uqH"><div class="spacer--3J57F padding-all-none--1xBJr" style="width:168px;box-sizing:border-box;flex-shrink:0"><span class="text-container--2tSUW size-body-4-low--3P9Zz default-color--2fMVe">送料・お届け日の分かりやすさなど、お届けに関するお客様ニーズを幅広く満たす商品が対象となるサービスです。</span></div></div><div class="arrow--3tC6p arrow-bottom--7xs2e" style="position:absolute"></div></div></div></div><span class="container--1jNXN default--2pz6o size-s--1RqgG left--3a2Px no-padding--1ZLh-"><input type="checkbox" class="control--FQ2nD" role="switch" aria-checked="false" aria-readonly="false" autoComplete="off"/></span></div></div><div class="item"><div class="dui-container reviewfilter" data-comp-id-flat="left_navi-review_score_filter"><div class="dui-filter-menu"><div class="header" title="レビュー"><span class="_ellipsis">レビュー</span></div><div class="content"><div class="review-filter--8MzoW"><a href="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?review=4.5" data-track-score="4.5"><div class="review-filter-line--3pcII"><div class="star-wrapper--3gb3J"><span class="review-star--6f1DR full--JlLBi"><i class="dui-icon -rating"></i></span><span class="review-star--6f1DR full--JlLBi"><i class="dui-icon -rating"></i></span><span class="review-star--6f1DR full--JlLBi"><i class="dui-icon -rating"></i></span><span class="review-star--6f1DR full--JlLBi"><i class="dui-icon -rating"></i></span><span class="review-star--6f1DR"><i class="dui-icon -rating"></i><span class="half--z5rWX"><i class="dui-icon -halfrating"></i></span></span></div><div><span>4.5 〜</span></div></div></a><a href="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?review=4" data-track-score="4.0"><div class="review-filter-line--3pcII"><div class="star-wrapper--3gb3J"><span class="review-star--6f1DR full--JlLBi"><i class="dui-icon -rating"></i></span><span class="review-star--6f1DR full--JlLBi"><i class="dui-icon -rating"></i></span><span class="review-star--6f1DR full--JlLBi"><i class="dui-icon -rating"></i></span><span class="review-star--6f1DR full--JlLBi"><i class="dui-icon -rating"></i></span><span class="review-star--6f1DR"><i class="dui-icon -rating"></i></span></div><div><span>4.0 〜</span></div></div></a><a href="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?review=3.5" data-track-score="3.5"><div class="review-filter-line--3pcII"><div class="star-wrapper--3gb3J"><span class="review-star--6f1DR full--JlLBi"><i class="dui-icon -rating"></i></span><span class="review-star--6f1DR full--JlLBi"><i class="dui-icon -rating"></i></span><span class="review-star--6f1DR full--JlLBi"><i class="dui-icon -rating"></i></span><span class="review-star--6f1DR"><i class="dui-icon -rating"></i><span class="half--z5rWX"><i class="dui-icon -halfrating"></i></span></span><span class="review-star--6f1DR"><i class="dui-icon -rating"></i></span></div><div><span>3.5 〜</span></div></div></a></div></div></div></div></div><div class="item"><div class="dui-container bestservicefilter"><div class="dui-filter-menu"><div class="header" title="その他の条件"><span class="_ellipsis">その他の条件</span></div><div class="content"><form id="best-service-form" autoComplete="off" class="dui-form" method="get" action="https://search.rakuten.co.jp/search/mall"><input type="hidden" name="sitem" value="スパイシーストーム ペリペリミックス 30g"/><input type="hidden" name="s" value="1"/><div class="grouped-fields--3YdUL"><div data-comp-id-flat="left_navi-super_deal_filter"><label class="field-label--31Zow"><input type="checkbox" id="best-service-0" name="f" value="13" aria-label="スーパーDEAL対象" class="field-input--209lp"/><span class="field-name--184_u">スーパーDEAL対象</span></label></div><div data-comp-id-flat="left_navi-subscription_filter"><label class="field-label--31Zow"><input type="checkbox" id="best-service-1" name="f" value="6" aria-label="定期購入・頒布会" class="field-input--209lp"/><span class="field-name--184_u">定期購入・頒布会</span></label></div><div data-comp-id-flat="left_navi-out_of_stock_filter"><label class="field-label--31Zow"><input type="checkbox" id="best-service-2" name="f" value="0" aria-label="売り切れを含む" class="field-input--209lp"/><span class="field-name--184_u">売り切れを含む</span></label></div></div><button class="dui-button -fluid" type="submit" style="display:none"><i class="dui-icon -search"></i>検索</button><noscript><button class="dui-button -fluid" type="submit"><i class="dui-icon -search"></i>検索</button></noscript><input type="hidden" value="not private"/><script type="text/javascript">
-                    window.addEventListener('fingerprinting-best-service', () => {
-                        const submitValue = () =>{
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-                        
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-                    window.addEventListener('fingerprinting-gift-filter', () => {
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-<div class="rsrAsideSDC" id="rsrSkyscraper">
-<div align="center">
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-<div style="width: 162px; text-align: left;">
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-<div style="padding: 5px 0; font-weight: bold;">▼注目ピックアップ!</div>
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-<div style="margin-bottom: 10px;"><a href="https://event.rakuten.co.jp/superdeal/?l-id=search_ad_deal"><img style="border: 1px solid #666; margin-bottom: 5px;" src="https://r.r10s.jp/com/img/thumb/search/leftnavi/2019/20190506_deal_160x100.jpg" width="160" height="100" border="0" alt=""><span>人気ブランドが目白押し！</span></a></div>
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-<div style="margin-bottom: 10px;"><a href="https://event.rakuten.co.jp/toshikoshi/pre/?l-id=toshikoshi2026_search_ad"><img style="border: 1px solid #666; margin-bottom: 5px;" src="https://r.r10s.jp/com/img/thumb/search/leftnavi/2026/20260828_toshikoshi_pre_160x100.jpg" width="160" height="100" border="0" alt=""><span>早期予約開始！</span></a></div>
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-</div></div></div></div></div><div class="dui-container content"><div class="dui-container infobar"><div class="dui-menu"><div class="displayOptions"><div class="item displayoptions"><div class="dui-menu"><form id="final-price-form" autoComplete="off" class="final-price-form--3Ko_l" method="get" action="https://search.rakuten.co.jp/search/mall"><input type="hidden" name="sitem" value="スパイシーストーム ペリペリミックス 30g"/><input type="hidden" name="p" value="1"/><input type="hidden" name="s" value="1"/><input type="hidden" name="set" value="priceDisplay"/><input type="hidden" name="_mp" value="{&quot;display_options:price&quot;:0,&quot;pricedisplay&quot;:2}"/><label class="final-price-label--jzBLS" for="pd">「商品価格＋送料−獲得予定ポイント」で価格表示</label><span class="container--1jNXN default--2pz6o size-m--1A_2T left--3a2Px disabled--3KLnW no-padding--1ZLh-"><input type="checkbox" class="control--FQ2nD" role="switch" name="pd" id="final-price-switch" disabled="" value="2" aria-disabled="true" aria-readonly="false" autoComplete="off"/></span></form><input type="hidden" value="not private"/><script type="text/javascript">
-                    window.addEventListener('fingerprinting-final-price', () => {
-                        const submitValue = () =>{
-                            const form = document.getElementById('final-price-form');
-                            form.submit();
-                        }
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-                        document.getElementById("final-price-switch").addEventListener('change',submitValue);
-                    })
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Mix","image":["https://thumbnail.image.rakuten.co.jp/@0_mall/founder/cabinet/misako_y001/01/my20232035main.jpg"],"offers":{"@type":"Offer","price":23490,"priceCurrency":"JPY"},"url":"https://item.rakuten.co.jp/founder/my20232035/?variantId=my20232035&scid=seo-carousel-search"}}]}</script><div class="search-results--2Rmmt"><div class="grid-container--1jsZ0"><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="11083702" data-shop-id="433416" data-track-itemid="433416/11083702" data-track-variantid="b0br4qltz5" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="1871" data-track-price-ranges="1871" data-track-doc-type="item" data-position-absolute="1" data-position-relative="1" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000433416-00011083702-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000433416-00011083702-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/slife/b0br4qltz5/?variantId=b0br4qltz5" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/slife/cabinet/rakus136_0203/b0br4qltz500.jpg?fitin=275:275" alt="【送料無料】SPICEDREAM PERIPERI MIX 50g瓶 ペリペリ" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="【送料無料】SPICEDREAM PERIPERI MIX 50g瓶 ペリペリ" target="_top" data-link="item" href="https://item.rakuten.co.jp/slife/b0br4qltz5/?variantId=b0br4qltz5" data-rpp-url-copy="title" class="title-link--3Yuev">【送料無料】SPICEDREAM PERIPERI MIX 50g瓶 ペリペリ</a></h2></div><div><div class="price-wrapper--10ccL"><div class="price--3zUvK">1,871<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="free-shipping-wrapper--N2A7-" data-tooltip="help-shipping"><span class="free-shipping-label--1shop">送料無料</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="433416" data-item-id="11083702" data-genre-id="509755" data-price="1871" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>17<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="sku-attributes--3mM2V sku-attributes-inline--3P4Fg sku-attributes-grid--1tJTZ"><div class="sku-attribute-values--1XyHd"><div class="inner-attributes-wrapper--RAOHZ"><div class="sku-attribute-value--3qtUM sku-attribute-value-selected--2uPgg">1個</div></div><div class="counter--2_hzl hidden-element--e1h3F">+0</div></div></div><div class="content shipping-status"><div class="shipping">12時まで当日出荷(出荷は営業日のみ)</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/slife/" class="_ellipsis" data-rpp-url-copy="shop">S-LIFE</a></div><div class="content extra"><a target="_top" class="dui-button -bestshop -fluid" href="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?ran=10010004582433046269&amp;s=11" data-rpp-url-copy="product">同じ商品を安い順で見る</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="11083887" data-shop-id="433416" data-track-itemid="433416/11083887" data-track-variantid="b0f43fn67h" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="1437" data-track-price-ranges="1437" data-track-doc-type="item" data-position-absolute="2" data-position-relative="2" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000433416-00011083887-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000433416-00011083887-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/slife/b0f43fn67h/?variantId=b0f43fn67h" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/slife/cabinet/rakus136_0205/b0f43fn67h00.jpg?fitin=275:275" alt="【送料無料】SPICEDREAM PERIPERIMIXソーッス! 195ml ペリペリソース" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="【送料無料】SPICEDREAM PERIPERIMIXソーッス! 195ml ペリペリソース" target="_top" data-link="item" href="https://item.rakuten.co.jp/slife/b0f43fn67h/?variantId=b0f43fn67h" data-rpp-url-copy="title" class="title-link--3Yuev">【送料無料】SPICEDREAM PERIPERIMIXソーッス! 195ml ペリペリソース</a></h2></div><div><div class="price-wrapper--10ccL"><div class="price--3zUvK">1,437<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="free-shipping-wrapper--N2A7-" data-tooltip="help-shipping"><span class="free-shipping-label--1shop">送料無料</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="433416" data-item-id="11083887" data-genre-id="101769" data-price="1437" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>13<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="content shipping-status"><div class="shipping">12時まで当日出荷(出荷は営業日のみ)</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/slife/" class="_ellipsis" data-rpp-url-copy="shop">S-LIFE</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="10001408" data-shop-id="409916" data-track-itemid="409916/10001408" data-track-variantid="b0drtxjyg6-b0gr3g7wf6-2" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="2099" data-track-price-ranges="2099" data-track-doc-type="item" data-position-absolute="3" data-position-relative="3" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000409916-00010001408-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000409916-00010001408-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/kcjt/b0drtxjyg6-b0gr3g7wf6-2/?variantId=b0drtxjyg6-b0gr3g7wf6-2" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/kcjt/cabinet/08974793/11591311/12873138/image7.jpg?fitin=275:275" alt="電子タバコ VAPE シーシャ 16,000回吸引可能 スターターセット 大容量 爆煙 ベイプ 使い捨て 煙量調節 繰り返し充電 カートリッジ2個つき 持ち運び 水蒸気タバコ フレーバー POD 禁煙 節煙 タール ニコチン0 ミックスベリー I4型 X-SPACE" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="電子タバコ VAPE シーシャ 16,000回吸引可能 スターターセット 大容量 爆煙 ベイプ 使い捨て 煙量調節 繰り返し充電 カートリッジ2個つき 持ち運び 水蒸気タバコ フレーバー POD 禁煙 節煙 タール ニコチン0 ミックスベリー I4型 X-SPACE" target="_top" data-link="item" href="https://item.rakuten.co.jp/kcjt/b0drtxjyg6-b0gr3g7wf6-2/?variantId=b0drtxjyg6-b0gr3g7wf6-2" data-rpp-url-copy="title" class="title-link--3Yuev">電子タバコ VAPE シーシャ 16,000回吸引可能 スターターセット 大容量 爆煙 ベイプ 使い捨て 煙量調節 繰り返し充電 カートリッジ2個つき 持ち運び 水蒸気タバコ フレーバー POD 禁煙 節煙 タール ニコチン0 ミックスベリー I4型 X-SPACE</a></h2></div><div><div class="price-wrapper--10ccL"><div class="price--3zUvK">2,099<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="free-shipping-wrapper--N2A7-" data-tooltip="help-shipping"><span class="free-shipping-label--1shop">送料無料</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="409916" data-item-id="10001408" data-genre-id="555002" data-price="2099" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>19<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="content shipping-status"><div class="shipping">14時までの決済で当日発送(土日祝日を除く)</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/kcjt/" class="_ellipsis" data-rpp-url-copy="shop">X-Space Vape Shop</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="10000817" data-shop-id="409916" data-track-itemid="409916/10000817" data-track-variantid="b0f42hm1dg-2" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="1199" data-track-price-ranges="1199" data-track-doc-type="item" data-position-absolute="4" data-position-relative="4" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000409916-00010000817-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000409916-00010000817-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/kcjt/b0f42hm1dg-2/?variantId=b0f42hm1dg-2" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/kcjt/cabinet/08974793/11633774/11656763/imgrc0093950213.jpg?fitin=275:275" alt="電子タバコ 使い捨て ベイプ 持ち運び シーシャ ミックスベリー2本 爆煙 3000回吸引可能 大容量 VAPE 水蒸気タバコ 禁煙 ニコチン0 タール0 MEET" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="電子タバコ 使い捨て ベイプ 持ち運び シーシャ ミックスベリー2本 爆煙 3000回吸引可能 大容量 VAPE 水蒸気タバコ 禁煙 ニコチン0 タール0 MEET" target="_top" data-link="item" href="https://item.rakuten.co.jp/kcjt/b0f42hm1dg-2/?variantId=b0f42hm1dg-2" data-rpp-url-copy="title" class="title-link--3Yuev">電子タバコ 使い捨て ベイプ 持ち運び シーシャ ミックスベリー2本 爆煙 3000回吸引可能 大容量 VAPE 水蒸気タバコ 禁煙 ニコチン0 タール0 MEET</a></h2></div><div><div class="price-wrapper--10ccL"><div class="price--3zUvK">1,199<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="free-shipping-wrapper--N2A7-" data-tooltip="help-shipping"><span class="free-shipping-label--1shop">送料無料</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="409916" data-item-id="10000817" data-genre-id="567814" data-price="1199" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>10<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="content shipping-status"><div class="shipping">14時までの決済で当日発送(土日祝日を除く)</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/kcjt/" class="_ellipsis" data-rpp-url-copy="shop">X-Space Vape Shop</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="10059361" data-shop-id="361889" data-track-itemid="361889/10059361" data-track-variantid="wm-60389" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="1378" data-track-price-ranges="1378" data-track-doc-type="item" data-position-absolute="5" data-position-relative="5" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000361889-00010059361-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000361889-00010059361-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/womensfitness/wm-60389/?variantId=wm-60389" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/womensfitness/cabinet/item/wm_60001-62000/wm-60389.jpg?fitin=275:275" alt="ピルズベリー モイスト シュプリーム レモンケーキミックス 432g Pillsbury Moist Supreme Lemon Cake Mix, 15.25 oz Box 【お取り寄せ商品】" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="ピルズベリー モイスト シュプリーム レモンケーキミックス 432g Pillsbury Moist Supreme Lemon Cake Mix, 15.25 oz Box 【お取り寄せ商品】" target="_top" data-link="item" href="https://item.rakuten.co.jp/womensfitness/wm-60389/?variantId=wm-60389" data-rpp-url-copy="title" class="title-link--3Yuev">ピルズベリー モイスト シュプリーム レモンケーキミックス 432g Pillsbury Moist Supreme Lemon Cake Mix, 15.25 oz Box 【お取り寄せ商品】</a></h2></div><div><div class="price-plus-shipping-grid--3Pzf6"><div class="text-display--2xC98 type-body--2wvYb size-small--F9Ahp align-left--2U3p8 color-gray-darker--XKM3P line-height-x-medium--1dbOx layout-inline--2yTOi">1,958円(価格+送料)</div></div><div class="price-wrapper--10ccL"><div class="price--3zUvK price-with-price-plus-shipping--Bmgz2">1,378<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="paid-shipping-wrapper--1Sq8U"><span>+送料580円</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="361889" data-item-id="10059361" data-genre-id="204268" data-price="1378" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>13<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="sku-attributes--3mM2V sku-attributes-inline--3P4Fg sku-attributes-grid--1tJTZ"><div class="sku-attribute-values--1XyHd"><div class="inner-attributes-wrapper--RAOHZ"><div class="sku-attribute-value--3qtUM sku-attribute-value-selected--2uPgg">1個</div></div><div class="counter--2_hzl hidden-element--e1h3F">+0</div></div></div><div class="content shipping-status"><div class="shipping">ご注文後、米国より14日〜30日を目安に発送</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/womensfitness/" class="_ellipsis" data-rpp-url-copy="shop">Costopa</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="10205105" data-shop-id="434913" data-track-itemid="434913/10205105" data-track-variantid="b0br4qltz5" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="1871" data-track-price-ranges="1871" data-track-doc-type="item" data-position-absolute="6" data-position-relative="6" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000434913-00010205105-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000434913-00010205105-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/sunnynook/b0br4qltz5/?variantId=b0br4qltz5" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/sunnynook/cabinet/rakus136_0203/b0br4qltz500.jpg?fitin=275:275" alt="【送料無料】SPICEDREAM PERIPERI MIX 50g瓶 ペリペリ" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="【送料無料】SPICEDREAM PERIPERI MIX 50g瓶 ペリペリ" target="_top" data-link="item" href="https://item.rakuten.co.jp/sunnynook/b0br4qltz5/?variantId=b0br4qltz5" data-rpp-url-copy="title" class="title-link--3Yuev">【送料無料】SPICEDREAM PERIPERI MIX 50g瓶 ペリペリ</a></h2></div><div><div class="price-wrapper--10ccL"><div class="price--3zUvK">1,871<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="free-shipping-wrapper--N2A7-" data-tooltip="help-shipping"><span class="free-shipping-label--1shop">送料無料</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="434913" data-item-id="10205105" data-genre-id="509755" data-price="1871" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>17<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="sku-attributes--3mM2V sku-attributes-inline--3P4Fg sku-attributes-grid--1tJTZ"><div class="sku-attribute-values--1XyHd"><div class="inner-attributes-wrapper--RAOHZ"><div class="sku-attribute-value--3qtUM sku-attribute-value-selected--2uPgg">1個</div></div><div class="counter--2_hzl hidden-element--e1h3F">+0</div></div></div><div class="content shipping-status"><div class="shipping">12時まで当日出荷(出荷は営業日のみ)</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/sunnynook/" class="_ellipsis" data-rpp-url-copy="shop">sunnynook</a></div><div class="content extra"><a target="_top" class="dui-button -bestshop -fluid" href="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?ran=10010004582433046269&amp;s=11" data-rpp-url-copy="product">同じ商品を安い順で見る</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="10033964" data-shop-id="200358" data-track-itemid="200358/10033964" data-track-variantid="108676" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="1150" data-track-price-ranges="1150" data-track-doc-type="item" data-position-absolute="7" data-position-relative="7" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000200358-00010033964-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000200358-00010033964-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/plaza/111301/?variantId=108676" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/plaza/cabinet/01977426/imgrc0114011939.jpg?fitin=275:275" alt="【シャグ刻葉】　スリードッグ・ペリク　30g　＆ライター等セット【B】〔ビター系〕" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="【シャグ刻葉】　スリードッグ・ペリク　30g　＆ライター等セット【B】〔ビター系〕" target="_top" data-link="item" href="https://item.rakuten.co.jp/plaza/111301/?variantId=108676" data-rpp-url-copy="title" class="title-link--3Yuev">【シャグ刻葉】　スリードッグ・ペリク　30g　＆ライター等セット【B】〔ビター系〕</a></h2></div><div><div class="price-plus-shipping-grid--3Pzf6"><div class="text-display--2xC98 type-body--2wvYb size-small--F9Ahp align-left--2U3p8 color-gray-darker--XKM3P line-height-x-medium--1dbOx layout-inline--2yTOi">2,130円(価格+送料)</div></div><div class="price-wrapper--10ccL"><div class="price--3zUvK price-with-price-plus-shipping--Bmgz2">1,150<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="paid-shipping-wrapper--1Sq8U"><span>+送料980円</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="200358" data-item-id="10033964" data-genre-id="568635" data-price="1150" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>10<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="content review"><a class="dui-rating-filter _link" target="_top" href="https://review.rakuten.co.jp/item/1/200358_10033964/1.1/" data-rpp-url-copy="review"><span class="dui-rating -full"><i class="dui-icon -rating"></i><i class="dui-icon -halfrating"></i></span><span class="dui-rating -full"><i class="dui-icon -rating"></i><i class="dui-icon -halfrating"></i></span><span class="dui-rating -full"><i class="dui-icon -rating"></i><i class="dui-icon -halfrating"></i></span><span class="dui-rating -full"><i class="dui-icon -rating"></i><i class="dui-icon -halfrating"></i></span><span class="dui-rating "><i class="dui-icon -rating"></i><i class="dui-icon -halfrating"></i></span><span class="score">4</span><span class="legend">(1件)</span></a></div><div class="content shipping-status"><div class="shipping">−店頭併売につき品切の場合があります−</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/plaza/" class="_ellipsis" data-rpp-url-copy="shop">Wine&amp;Cigar リカープラザ大越酒店</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="11723254" data-shop-id="427886" data-track-itemid="427886/11723254" data-track-variantid="b0br4qltz5" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="1871" data-track-price-ranges="1871" data-track-doc-type="item" data-position-absolute="8" data-position-relative="8" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000427886-00011723254-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000427886-00011723254-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/donzstore/b0br4qltz5/?variantId=b0br4qltz5" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/donzstore/cabinet/rakus136_0203/b0br4qltz500.jpg?fitin=275:275" alt="【送料無料】SPICEDREAM PERIPERI MIX 50g瓶 ペリペリ" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="【送料無料】SPICEDREAM PERIPERI MIX 50g瓶 ペリペリ" target="_top" data-link="item" href="https://item.rakuten.co.jp/donzstore/b0br4qltz5/?variantId=b0br4qltz5" data-rpp-url-copy="title" class="title-link--3Yuev">【送料無料】SPICEDREAM PERIPERI MIX 50g瓶 ペリペリ</a></h2></div><div><div class="price-wrapper--10ccL"><div class="price--3zUvK">1,871<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="free-shipping-wrapper--N2A7-" data-tooltip="help-shipping"><span class="free-shipping-label--1shop">送料無料</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="427886" data-item-id="11723254" data-genre-id="509755" data-price="1871" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>17<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="sku-attributes--3mM2V sku-attributes-inline--3P4Fg sku-attributes-grid--1tJTZ"><div class="sku-attribute-values--1XyHd"><div class="inner-attributes-wrapper--RAOHZ"><div class="sku-attribute-value--3qtUM sku-attribute-value-selected--2uPgg">1個</div></div><div class="counter--2_hzl hidden-element--e1h3F">+0</div></div></div><div class="content shipping-status"><div class="shipping">12時まで当日出荷(出荷は営業日のみ)</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/donzstore/" class="_ellipsis" data-rpp-url-copy="shop">DONZ STORE</a></div><div class="content extra"><a target="_top" class="dui-button -bestshop -fluid" href="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?ran=10010004582433046269&amp;s=11" data-rpp-url-copy="product">同じ商品を安い順で見る</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="12606921" data-shop-id="376041" data-track-itemid="376041/12606921" data-track-variantid="163235118" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="10877" data-track-price-ranges="10877" data-track-doc-type="item" data-position-absolute="9" data-position-relative="9" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000376041-00012606921-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000376041-00012606921-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/glomarket/31297364/?variantId=163235118" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/glomarket/cabinet/07178412/07768973/imgb00df5aczu0.jpg?fitin=275:275" alt="Spice Supreme Pumpkin Pie Spice, 2.5-Ounce (Pack of 12)" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="Spice Supreme Pumpkin Pie Spice, 2.5-Ounce (Pack of 12)" target="_top" data-link="item" href="https://item.rakuten.co.jp/glomarket/31297364/?variantId=163235118" data-rpp-url-copy="title" class="title-link--3Yuev">Spice Supreme Pumpkin Pie Spice, 2.5-Ounce (Pack of 12)</a></h2></div><div><div class="price-plus-shipping-grid--3Pzf6"><div class="text-display--2xC98 type-body--2wvYb size-small--F9Ahp align-left--2U3p8 color-gray-darker--XKM3P line-height-x-medium--1dbOx layout-inline--2yTOi">12,357円(価格+送料)</div></div><div class="price-wrapper--10ccL"><div class="price--3zUvK price-with-price-plus-shipping--Bmgz2">10,877<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="paid-shipping-wrapper--1Sq8U"><span>+送料1,480円</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="376041" data-item-id="12606921" data-genre-id="101833" data-price="10877" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>108<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="content shipping-status"><div class="shipping">20日以内に国際発送、30日以内にお届け予定</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/glomarket/" class="_ellipsis" data-rpp-url-copy="shop">Glomarket</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="10036891" data-shop-id="374383" data-track-itemid="374383/10036891" data-track-variantid="my20232035" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="23490" data-track-price-ranges="23490" data-track-doc-type="item" data-position-absolute="10" data-position-relative="10" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000374383-00010036891-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000374383-00010036891-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/founder/my20232035/?variantId=my20232035" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/founder/cabinet/misako_y001/01/my20232035main.jpg?fitin=275:275" alt="ブルーム スーパーベリー 粉末ドリンクミックス バラエティパック 3フレーバー 45袋（1個あたり15袋入り）砂糖不使用 Blume SuperBelly Hydration Drink Mix" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="ブルーム スーパーベリー 粉末ドリンクミックス バラエティパック 3フレーバー 45袋（1個あたり15袋入り）砂糖不使用 Blume SuperBelly Hydration Drink Mix" target="_top" data-link="item" href="https://item.rakuten.co.jp/founder/my20232035/?variantId=my20232035" data-rpp-url-copy="title" class="title-link--3Yuev">ブルーム スーパーベリー 粉末ドリンクミックス バラエティパック 3フレーバー 45袋（1個あたり15袋入り）砂糖不使用 Blume SuperBelly Hydration Drink Mix</a></h2></div><div><div class="price-wrapper--10ccL"><div class="price--3zUvK">23,490<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="free-shipping-wrapper--N2A7-" data-tooltip="help-shipping"><span class="free-shipping-label--1shop">送料無料</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="374383" data-item-id="10036891" data-genre-id="101033" data-price="23490" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>234<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="sku-attributes--3mM2V sku-attributes-inline--3P4Fg sku-attributes-grid--1tJTZ"><div class="sku-attribute-values--1XyHd"><div class="inner-attributes-wrapper--RAOHZ"><div class="sku-attribute-value--3qtUM sku-attribute-value-selected--2uPgg">3個</div></div><div class="counter--2_hzl hidden-element--e1h3F">+0</div></div></div><div class="content shipping-status"><div class="shipping">2~14日以内に国際発送10日以内にお届け予定</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/founder/" class="_ellipsis" data-rpp-url-copy="shop">Founder</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="10829522" data-shop-id="433416" data-track-itemid="433416/10829522" data-track-variantid="b0fffz4kjz" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="8928" data-track-price-ranges="8928" data-track-doc-type="item" data-position-absolute="11" data-position-relative="11" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000433416-00010829522-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000433416-00010829522-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/slife/b0fffz4kjz/?variantId=b0fffz4kjz" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/slife/cabinet/rakus136_0144/b0fffz4kjz00.jpg?fitin=275:275" alt="【送料無料】Maison Perrier(メゾンペリエ) メゾンペリエ コレクション ライムジンジャー 330mlx24本" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="【送料無料】Maison Perrier(メゾンペリエ) メゾンペリエ コレクション ライムジンジャー 330mlx24本" target="_top" data-link="item" href="https://item.rakuten.co.jp/slife/b0fffz4kjz/?variantId=b0fffz4kjz" data-rpp-url-copy="title" class="title-link--3Yuev">【送料無料】Maison Perrier(メゾンペリエ) メゾンペリエ コレクション ライムジンジャー 330mlx24本</a></h2></div><div><div class="price-wrapper--10ccL"><div class="price--3zUvK">8,928<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="free-shipping-wrapper--N2A7-" data-tooltip="help-shipping"><span class="free-shipping-label--1shop">送料無料</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="433416" data-item-id="10829522" data-genre-id="568264" data-price="8928" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>82<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="content shipping-status"><div class="shipping">12時まで当日出荷(出荷は営業日のみ)</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/slife/" class="_ellipsis" data-rpp-url-copy="shop">S-LIFE</a></div><div class="content extra"><a target="_top" class="dui-button -bestshop -fluid" href="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?ran=30010008445291613713&amp;s=11" data-rpp-url-copy="product">同じ商品を安い順で見る</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="10002045" data-shop-id="198543" data-track-itemid="198543/10002045" data-track-variantid="10002045" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="8850" data-track-price-ranges="8850" data-track-doc-type="item" data-position-absolute="12" data-position-relative="12" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000198543-00010002045-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000198543-00010002045-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/snus/10002045/?variantId=10002045" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/snus/cabinet/1878-1.jpg?fitin=275:275" alt="200Sticks MEVIUS BERRY Option Plume X　メビウス・ベリー・オプション・プルーム・エックス　 海外販売専用商品,　international delivery available 香烟香菸香煙" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="200Sticks MEVIUS BERRY Option Plume X　メビウス・ベリー・オプション・プルーム・エックス　 海外販売専用商品,　international delivery available 香烟香菸香煙" target="_top" data-link="item" href="https://item.rakuten.co.jp/snus/10002045/?variantId=10002045" data-rpp-url-copy="title" class="title-link--3Yuev">200Sticks MEVIUS BERRY Option Plume X　メビウス・ベリー・オプション・プルーム・エックス　 海外販売専用商品,　international delivery available 香烟香菸香煙</a></h2></div><div><div class="price-plus-shipping-grid--3Pzf6"><div class="text-display--2xC98 type-body--2wvYb size-small--F9Ahp align-left--2U3p8 color-gray-darker--XKM3P line-height-x-medium--1dbOx layout-inline--2yTOi">9,400円(価格+送料)</div></div><div class="price-wrapper--10ccL"><div class="price--3zUvK price-with-price-plus-shipping--Bmgz2">8,850<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="paid-shipping-wrapper--1Sq8U"><span>+送料550円</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="198543" data-item-id="10002045" data-genre-id="555003" data-price="8850" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>80<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="content shipping-status"><div class="shipping">1〜2日以内に発送予定(店舗休業日を除く)</div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/snus/" class="_ellipsis" data-rpp-url-copy="shop">堀 商事</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="10000807" data-shop-id="430772" data-track-itemid="430772/10000807" data-track-variantid="1m77-10" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="4999" data-track-price-ranges="4999/13999" data-track-doc-type="item" data-position-absolute="13" data-position-relative="13" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000430772-00010000807-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000430772-00010000807-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/kcjt002/1m77/" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/kcjt002/cabinet/11639513/1m77/imgrc0100525094.jpg?fitin=275:275" alt="電子タバコ 使い捨て 業界最安値挑戦中 ミックスベリー風味 10～30本セット 最大45000回吸引 10本以上まとめ買いでお得 NICOCO 大容量 爆煙 水蒸気タバコ VAPE シーシャ 禁煙 ベイプ メンテナンス不要 ニコチン・タールなし" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="電子タバコ 使い捨て 業界最安値挑戦中 ミックスベリー風味 10～30本セット 最大45000回吸引 10本以上まとめ買いでお得 NICOCO 大容量 爆煙 水蒸気タバコ VAPE シーシャ 禁煙 ベイプ メンテナンス不要 ニコチン・タールなし" target="_top" data-link="item" href="https://item.rakuten.co.jp/kcjt002/1m77/" data-rpp-url-copy="title" class="title-link--3Yuev">電子タバコ 使い捨て 業界最安値挑戦中 ミックスベリー風味 10～30本セット 最大45000回吸引 10本以上まとめ買いでお得 NICOCO 大容量 爆煙 水蒸気タバコ VAPE シーシャ 禁煙 ベイプ メンテナンス不要 ニコチン・タールなし</a></h2></div><div><div class="price-wrapper--10ccL"><div class="price--3zUvK">4,999<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円〜</span></div><span class="free-shipping-wrapper--N2A7-" data-tooltip="help-shipping"><span class="free-shipping-label--1shop">送料無料</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="430772" data-item-id="10000807" data-genre-id="567814" data-price="4999" data-max-price="13999" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>45<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)<!-- -->〜</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="content shipping-status"><div class="shipping">15時までの決済で当日発送(土日祝日を除く)</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/kcjt002/" class="_ellipsis" data-rpp-url-copy="shop">NeoVapor電子タバコ専門店</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="12512580" data-shop-id="376041" data-track-itemid="376041/12512580" data-track-variantid="162280115" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="24501" data-track-price-ranges="24501" data-track-doc-type="item" data-position-absolute="14" data-position-relative="14" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000376041-00012512580-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000376041-00012512580-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/glomarket/31624133/?variantId=162280115" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/glomarket/cabinet/07178412/07860176/imgb08hdnhmx50.jpg?fitin=275:275" alt="Pillsbury Moist Supreme Sugar Free Classic Yellow Premium Cake Mix, 16-Ounce (Pack of 12)" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="Pillsbury Moist Supreme Sugar Free Classic Yellow Premium Cake Mix, 16-Ounce (Pack of 12)" target="_top" data-link="item" href="https://item.rakuten.co.jp/glomarket/31624133/?variantId=162280115" data-rpp-url-copy="title" class="title-link--3Yuev">Pillsbury Moist Supreme Sugar Free Classic Yellow Premium Cake Mix, 16-Ounce (Pack of 12)</a></h2></div><div><div class="price-plus-shipping-grid--3Pzf6"><div class="text-display--2xC98 type-body--2wvYb size-small--F9Ahp align-left--2U3p8 color-gray-darker--XKM3P line-height-x-medium--1dbOx layout-inline--2yTOi">25,981円(価格+送料)</div></div><div class="price-wrapper--10ccL"><div class="price--3zUvK price-with-price-plus-shipping--Bmgz2">24,501<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="paid-shipping-wrapper--1Sq8U"><span>+送料1,480円</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="376041" data-item-id="12512580" data-genre-id="101833" data-price="24501" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>245<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="content shipping-status"><div class="shipping">20日以内に国際発送、30日以内にお届け予定</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/glomarket/" class="_ellipsis" data-rpp-url-copy="shop">Glomarket</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="10000816" data-shop-id="430772" data-track-itemid="430772/10000816" data-track-variantid="lz4k-10" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="5099" data-track-price-ranges="5099/14700" data-track-doc-type="item" data-position-absolute="15" data-position-relative="15" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000430772-00010000816-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000430772-00010000816-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/kcjt002/lz4k/" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/kcjt002/cabinet/jo5es/lz4k/imgrc0100965422.jpg?fitin=275:275" alt="電子タバコ 使い捨て ミックスベリー風味 VAPE べイプ MEET 紙タバコ感覚 最大60000回吸引可能 紙巻きタバコそっくりの吸い心地 タバコフィルター付き 高コスパ 10～30本セットまとめ買いでさらにお得 節煙サポート ボタンなし即吸い起動" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="電子タバコ 使い捨て ミックスベリー風味 VAPE べイプ MEET 紙タバコ感覚 最大60000回吸引可能 紙巻きタバコそっくりの吸い心地 タバコフィルター付き 高コスパ 10～30本セットまとめ買いでさらにお得 節煙サポート ボタンなし即吸い起動" target="_top" data-link="item" href="https://item.rakuten.co.jp/kcjt002/lz4k/" data-rpp-url-copy="title" class="title-link--3Yuev">電子タバコ 使い捨て ミックスベリー風味 VAPE べイプ MEET 紙タバコ感覚 最大60000回吸引可能 紙巻きタバコそっくりの吸い心地 タバコフィルター付き 高コスパ 10～30本セットまとめ買いでさらにお得 節煙サポート ボタンなし即吸い起動</a></h2></div><div><div class="price-wrapper--10ccL"><div class="price--3zUvK">5,099<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円〜</span></div><span class="free-shipping-wrapper--N2A7-" data-tooltip="help-shipping"><span class="free-shipping-label--1shop">送料無料</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="430772" data-item-id="10000816" data-genre-id="567814" data-price="5099" data-max-price="14700" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>46<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)<!-- -->〜</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="content review"><a class="dui-rating-filter _link" target="_top" href="https://review.rakuten.co.jp/item/1/430772_10000816/1.1/" data-rpp-url-copy="review"><span class="dui-rating -full"><i class="dui-icon -rating"></i><i class="dui-icon -halfrating"></i></span><span class="dui-rating -full"><i class="dui-icon -rating"></i><i class="dui-icon -halfrating"></i></span><span class="dui-rating -full"><i class="dui-icon -rating"></i><i class="dui-icon -halfrating"></i></span><span class="dui-rating -full"><i class="dui-icon -rating"></i><i class="dui-icon -halfrating"></i></span><span class="dui-rating -full"><i class="dui-icon -rating"></i><i class="dui-icon -halfrating"></i></span><span class="score">5</span><span class="legend">(1件)</span></a></div><div class="content shipping-status"><div class="shipping">15時までの決済で当日発送(土日祝日を除く)</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/kcjt002/" class="_ellipsis" data-rpp-url-copy="shop">NeoVapor電子タバコ専門店</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="12511411" data-shop-id="376041" data-track-itemid="376041/12511411" data-track-variantid="162296918" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="3177" data-track-price-ranges="3177" data-track-doc-type="item" data-position-absolute="16" data-position-relative="16" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000376041-00012511411-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000376041-00012511411-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/glomarket/30769180/?variantId=162296918" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/glomarket/cabinet/07178412/07757167/imgb003zfqedy0.jpg?fitin=275:275" alt="Pereg Jerusalem Spice Seasoning (4.25 Oz) - Israeli Savory Spice Mix with Cumin, Coriander, Allspice, Garlic &amp; Paprika Grilling BBQ Spice Rub - Non-GMO &amp; 100% Natural" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="Pereg Jerusalem Spice Seasoning (4.25 Oz) - Israeli Savory Spice Mix with Cumin, Coriander, Allspice, Garlic &amp; Paprika Grilling BBQ Spice Rub - Non-GMO &amp; 100% Natural" target="_top" data-link="item" href="https://item.rakuten.co.jp/glomarket/30769180/?variantId=162296918" data-rpp-url-copy="title" class="title-link--3Yuev">Pereg Jerusalem Spice Seasoning (4.25 Oz) - Israeli Savory Spice Mix with Cumin, Coriander, Allspice, Garlic &amp; Paprika Grilling BBQ Spice Rub - Non-GMO &amp; 100% Natural</a></h2></div><div><div class="price-plus-shipping-grid--3Pzf6"><div class="text-display--2xC98 type-body--2wvYb size-small--F9Ahp align-left--2U3p8 color-gray-darker--XKM3P line-height-x-medium--1dbOx layout-inline--2yTOi">4,657円(価格+送料)</div></div><div class="price-wrapper--10ccL"><div class="price--3zUvK price-with-price-plus-shipping--Bmgz2">3,177<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="paid-shipping-wrapper--1Sq8U"><span>+送料1,480円</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="376041" data-item-id="12511411" data-genre-id="101833" data-price="3177" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>31<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="content shipping-status"><div class="shipping">20日以内に国際発送、30日以内にお届け予定</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/glomarket/" class="_ellipsis" data-rpp-url-copy="shop">Glomarket</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="12519783" data-shop-id="376041" data-track-itemid="376041/12519783" data-track-variantid="162297787" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="3391" data-track-price-ranges="3391" data-track-doc-type="item" data-position-absolute="17" data-position-relative="17" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000376041-00012519783-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000376041-00012519783-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/glomarket/30769641/?variantId=162297787" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/glomarket/cabinet/07178412/07759530/imgb00y0jxo280.jpg?fitin=275:275" alt="Nando&#x27;s ホット ペリペリ シーズニング ラブ (25g) - 2 個パック Nando&#x27;s Hot Peri Peri Seasoning Rub (25g) - Pack of 2" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="Nando&#x27;s ホット ペリペリ シーズニング ラブ (25g) - 2 個パック Nando&#x27;s Hot Peri Peri Seasoning Rub (25g) - Pack of 2" target="_top" data-link="item" href="https://item.rakuten.co.jp/glomarket/30769641/?variantId=162297787" data-rpp-url-copy="title" class="title-link--3Yuev">Nando&#x27;s ホット ペリペリ シーズニング ラブ (25g) - 2 個パック Nando&#x27;s Hot Peri Peri Seasoning Rub (25g) - Pack of 2</a></h2></div><div><div class="price-plus-shipping-grid--3Pzf6"><div class="text-display--2xC98 type-body--2wvYb size-small--F9Ahp align-left--2U3p8 color-gray-darker--XKM3P line-height-x-medium--1dbOx layout-inline--2yTOi">4,871円(価格+送料)</div></div><div class="price-wrapper--10ccL"><div class="price--3zUvK price-with-price-plus-shipping--Bmgz2">3,391<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="paid-shipping-wrapper--1Sq8U"><span>+送料1,480円</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="376041" data-item-id="12519783" data-genre-id="101833" data-price="3391" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>33<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="content shipping-status"><div class="shipping">20日以内に国際発送、30日以内にお届け予定</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/glomarket/" class="_ellipsis" data-rpp-url-copy="shop">Glomarket</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="12623164" data-shop-id="376041" data-track-itemid="376041/12623164" data-track-variantid="9415147" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="20246" data-track-price-ranges="20246" data-track-doc-type="item" data-position-absolute="18" data-position-relative="18" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000376041-00012623164-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000376041-00012623164-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/glomarket/31480561/?variantId=9415147" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/glomarket/cabinet/07178412/07716132/imgb07ysy8xrf0.jpg?fitin=275:275" alt="パロミ ハイビスカス ベリー オーガニック ハーブ ティー、シグネチャー ジャー、15 カウント (6 個パック) Paromi Hibiscus Berry Organic Herbal Tea, Signature Jar, 15 Count (Pack of 6)" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="パロミ ハイビスカス ベリー オーガニック ハーブ ティー、シグネチャー ジャー、15 カウント (6 個パック) Paromi Hibiscus Berry Organic Herbal Tea, Signature Jar, 15 Count (Pack of 6)" target="_top" data-link="item" href="https://item.rakuten.co.jp/glomarket/31480561/?variantId=9415147" data-rpp-url-copy="title" class="title-link--3Yuev">パロミ ハイビスカス ベリー オーガニック ハーブ ティー、シグネチャー ジャー、15 カウント (6 個パック) Paromi Hibiscus Berry Organic Herbal Tea, Signature Jar, 15 Count (Pack of 6)</a></h2></div><div><div class="price-plus-shipping-grid--3Pzf6"><div class="text-display--2xC98 type-body--2wvYb size-small--F9Ahp align-left--2U3p8 color-gray-darker--XKM3P line-height-x-medium--1dbOx layout-inline--2yTOi">21,726円(価格+送料)</div></div><div class="price-wrapper--10ccL"><div class="price--3zUvK price-with-price-plus-shipping--Bmgz2">20,246<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="paid-shipping-wrapper--1Sq8U"><span>+送料1,480円</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="376041" data-item-id="12623164" data-genre-id="101833" data-price="20246" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>202<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="content shipping-status"><div class="shipping">20日以内に国際発送、30日以内にお届け予定</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/glomarket/" class="_ellipsis" data-rpp-url-copy="shop">Glomarket</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="12561082" data-shop-id="376041" data-track-itemid="376041/12561082" data-track-variantid="162768259" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="4908" data-track-price-ranges="4908" data-track-doc-type="item" data-position-absolute="19" data-position-relative="19" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000376041-00012561082-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000376041-00012561082-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/glomarket/30838405/?variantId=162768259" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/glomarket/cabinet/07178412/07764156/imgb00y0jkn6i0.jpg?fitin=275:275" alt="Schwartz Perfect Shake Piri Piri Herb &amp; Spice Blend (39g) - Pack of 2" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="Schwartz Perfect Shake Piri Piri Herb &amp; Spice Blend (39g) - Pack of 2" target="_top" data-link="item" href="https://item.rakuten.co.jp/glomarket/30838405/?variantId=162768259" data-rpp-url-copy="title" class="title-link--3Yuev">Schwartz Perfect Shake Piri Piri Herb &amp; Spice Blend (39g) - Pack of 2</a></h2></div><div><div class="price-plus-shipping-grid--3Pzf6"><div class="text-display--2xC98 type-body--2wvYb size-small--F9Ahp align-left--2U3p8 color-gray-darker--XKM3P line-height-x-medium--1dbOx layout-inline--2yTOi">6,388円(価格+送料)</div></div><div class="price-wrapper--10ccL"><div class="price--3zUvK price-with-price-plus-shipping--Bmgz2">4,908<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="paid-shipping-wrapper--1Sq8U"><span>+送料1,480円</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="376041" data-item-id="12561082" data-genre-id="101833" data-price="4908" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>49<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="content shipping-status"><div class="shipping">20日以内に国際発送、30日以内にお届け予定</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/glomarket/" class="_ellipsis" data-rpp-url-copy="shop">Glomarket</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="12464157" data-shop-id="376041" data-track-itemid="376041/12464157" data-track-variantid="162196718" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="2912" data-track-price-ranges="2912" data-track-doc-type="item" data-position-absolute="20" data-position-relative="20" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000376041-00012464157-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000376041-00012464157-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/glomarket/30742312/?variantId=162196718" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/glomarket/cabinet/07178412/07837376/imgb000wr8l4s0.jpg?fitin=275:275" alt="フロンティア・コープ スパイスミックス Frontier Co-op Whole Juniper Berries, 1.28 Ounce Bottle, Rich and Piney for Soups, Marinades, Pickling and Sauerkraut" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="フロンティア・コープ スパイスミックス Frontier Co-op Whole Juniper Berries, 1.28 Ounce Bottle, Rich and Piney for Soups, Marinades, Pickling and Sauerkraut" target="_top" data-link="item" href="https://item.rakuten.co.jp/glomarket/30742312/?variantId=162196718" data-rpp-url-copy="title" class="title-link--3Yuev">フロンティア・コープ スパイスミックス Frontier Co-op Whole Juniper Berries, 1.28 Ounce Bottle, Rich and Piney for Soups, Marinades, Pickling and Sauerkraut</a></h2></div><div><div class="price-plus-shipping-grid--3Pzf6"><div class="text-display--2xC98 type-body--2wvYb size-small--F9Ahp align-left--2U3p8 color-gray-darker--XKM3P line-height-x-medium--1dbOx layout-inline--2yTOi">4,392円(価格+送料)</div></div><div class="price-wrapper--10ccL"><div class="price--3zUvK price-with-price-plus-shipping--Bmgz2">2,912<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="paid-shipping-wrapper--1Sq8U"><span>+送料1,480円</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="376041" data-item-id="12464157" data-genre-id="101833" data-price="2912" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>29<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="content shipping-status"><div class="shipping">20日以内に国際発送、30日以内にお届け予定</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/glomarket/" class="_ellipsis" data-rpp-url-copy="shop">Glomarket</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="12520906" data-shop-id="376041" data-track-itemid="376041/12520906" data-track-variantid="162298490" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="3538" data-track-price-ranges="3538" data-track-doc-type="item" data-position-absolute="21" data-position-relative="21" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000376041-00012520906-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000376041-00012520906-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/glomarket/30770103/?variantId=162298490" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/glomarket/cabinet/07178412/07849709/imgb002vgc2h00.jpg?fitin=275:275" alt="SPICE SUPREME GARLIC SALT 5.25OZ (販売: 1 パックあたり 3 個) SPICE SUPREME GARLIC SALT 5.25OZ (Sold: 3 Units per Pack)" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="SPICE SUPREME GARLIC SALT 5.25OZ (販売: 1 パックあたり 3 個) SPICE SUPREME GARLIC SALT 5.25OZ (Sold: 3 Units per Pack)" target="_top" data-link="item" href="https://item.rakuten.co.jp/glomarket/30770103/?variantId=162298490" data-rpp-url-copy="title" class="title-link--3Yuev">SPICE SUPREME GARLIC SALT 5.25OZ (販売: 1 パックあたり 3 個) SPICE SUPREME GARLIC SALT 5.25OZ (Sold: 3 Units per Pack)</a></h2></div><div><div class="price-plus-shipping-grid--3Pzf6"><div class="text-display--2xC98 type-body--2wvYb size-small--F9Ahp align-left--2U3p8 color-gray-darker--XKM3P line-height-x-medium--1dbOx layout-inline--2yTOi">5,018円(価格+送料)</div></div><div class="price-wrapper--10ccL"><div class="price--3zUvK price-with-price-plus-shipping--Bmgz2">3,538<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="paid-shipping-wrapper--1Sq8U"><span>+送料1,480円</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="376041" data-item-id="12520906" data-genre-id="101833" data-price="3538" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>35<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="content shipping-status"><div class="shipping">20日以内に国際発送、30日以内にお届け予定</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/glomarket/" class="_ellipsis" data-rpp-url-copy="shop">Glomarket</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="12522403" data-shop-id="376041" data-track-itemid="376041/12522403" data-track-variantid="162167126" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="13689" data-track-price-ranges="13689" data-track-doc-type="item" data-position-absolute="22" data-position-relative="22" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000376041-00012522403-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000376041-00012522403-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/glomarket/30721044/?variantId=162167126" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/glomarket/cabinet/07178412/07759603/imgb01lxa6b3v0.jpg?fitin=275:275" alt="シュワルツ パーフェクト シェイク ピリピリ シーズニング ジャー 39g - 6個パック Schwartz Perfect Shake Piri Piri Seasoning Jar 39g - Pack of 6" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="シュワルツ パーフェクト シェイク ピリピリ シーズニング ジャー 39g - 6個パック Schwartz Perfect Shake Piri Piri Seasoning Jar 39g - Pack of 6" target="_top" data-link="item" href="https://item.rakuten.co.jp/glomarket/30721044/?variantId=162167126" data-rpp-url-copy="title" class="title-link--3Yuev">シュワルツ パーフェクト シェイク ピリピリ シーズニング ジャー 39g - 6個パック Schwartz Perfect Shake Piri Piri Seasoning Jar 39g - Pack of 6</a></h2></div><div><div class="price-plus-shipping-grid--3Pzf6"><div class="text-display--2xC98 type-body--2wvYb size-small--F9Ahp align-left--2U3p8 color-gray-darker--XKM3P line-height-x-medium--1dbOx layout-inline--2yTOi">15,169円(価格+送料)</div></div><div class="price-wrapper--10ccL"><div class="price--3zUvK price-with-price-plus-shipping--Bmgz2">13,689<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="paid-shipping-wrapper--1Sq8U"><span>+送料1,480円</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="376041" data-item-id="12522403" data-genre-id="101833" data-price="13689" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>136<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="content shipping-status"><div class="shipping">20日以内に国際発送、30日以内にお届け予定</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/glomarket/" class="_ellipsis" data-rpp-url-copy="shop">Glomarket</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="12595483" data-shop-id="376041" data-track-itemid="376041/12595483" data-track-variantid="163118201" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="5060" data-track-price-ranges="5060" data-track-doc-type="item" data-position-absolute="23" data-position-relative="23" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000376041-00012595483-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000376041-00012595483-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/glomarket/31181604/?variantId=163118201" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/glomarket/cabinet/07178412/07767052/imgb0cl6fl84b0.jpg?fitin=275:275" alt="Zercumm Pepper 180G/Can Spice Pericarpium Zanthoxyli Bungeani Pepper Hotpot Condiment Ingredients Seasoning" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="Zercumm Pepper 180G/Can Spice Pericarpium Zanthoxyli Bungeani Pepper Hotpot Condiment Ingredients Seasoning" target="_top" data-link="item" href="https://item.rakuten.co.jp/glomarket/31181604/?variantId=163118201" data-rpp-url-copy="title" class="title-link--3Yuev">Zercumm Pepper 180G/Can Spice Pericarpium Zanthoxyli Bungeani Pepper Hotpot Condiment Ingredients Seasoning</a></h2></div><div><div class="price-plus-shipping-grid--3Pzf6"><div class="text-display--2xC98 type-body--2wvYb size-small--F9Ahp align-left--2U3p8 color-gray-darker--XKM3P line-height-x-medium--1dbOx layout-inline--2yTOi">6,540円(価格+送料)</div></div><div class="price-wrapper--10ccL"><div class="price--3zUvK price-with-price-plus-shipping--Bmgz2">5,060<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="paid-shipping-wrapper--1Sq8U"><span>+送料1,480円</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="376041" data-item-id="12595483" data-genre-id="101833" data-price="5060" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>50<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="content shipping-status"><div class="shipping">20日以内に国際発送、30日以内にお届け予定</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/glomarket/" class="_ellipsis" data-rpp-url-copy="shop">Glomarket</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="12520537" data-shop-id="376041" data-track-itemid="376041/12520537" data-track-variantid="162298021" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="5813" data-track-price-ranges="5813" data-track-doc-type="item" data-position-absolute="24" data-position-relative="24" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000376041-00012520537-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000376041-00012520537-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/glomarket/30763949/?variantId=162298021" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/glomarket/cabinet/07178412/07759530/imgb07s4ctb8h0.jpg?fitin=275:275" alt="Savory Spice Supreme Saigon Cinnamon (3.1 oz) - Ground Saigon Cinnamon Powder for Baking, Desserts &amp; Cooking - Bold, Strong &amp; Fresh Flavor" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="Savory Spice Supreme Saigon Cinnamon (3.1 oz) - Ground Saigon Cinnamon Powder for Baking, Desserts &amp; Cooking - Bold, Strong &amp; Fresh Flavor" target="_top" data-link="item" href="https://item.rakuten.co.jp/glomarket/30763949/?variantId=162298021" data-rpp-url-copy="title" class="title-link--3Yuev">Savory Spice Supreme Saigon Cinnamon (3.1 oz) - Ground Saigon Cinnamon Powder for Baking, Desserts &amp; Cooking - Bold, Strong &amp; Fresh Flavor</a></h2></div><div><div class="price-plus-shipping-grid--3Pzf6"><div class="text-display--2xC98 type-body--2wvYb size-small--F9Ahp align-left--2U3p8 color-gray-darker--XKM3P line-height-x-medium--1dbOx layout-inline--2yTOi">7,293円(価格+送料)</div></div><div class="price-wrapper--10ccL"><div class="price--3zUvK price-with-price-plus-shipping--Bmgz2">5,813<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="paid-shipping-wrapper--1Sq8U"><span>+送料1,480円</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="376041" data-item-id="12520537" data-genre-id="101833" data-price="5813" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>58<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="content shipping-status"><div class="shipping">20日以内に国際発送、30日以内にお届け予定</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/glomarket/" class="_ellipsis" data-rpp-url-copy="shop">Glomarket</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="12536285" data-shop-id="376041" data-track-itemid="376041/12536285" data-track-variantid="162299774" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="11032" data-track-price-ranges="11032" data-track-doc-type="item" data-position-absolute="25" data-position-relative="25" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000376041-00012536285-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000376041-00012536285-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/glomarket/30770817/?variantId=162299774" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/glomarket/cabinet/07178412/07761621/imgb002yeedeo0.jpg?fitin=275:275" alt="Spice Supreme セロリシード、ホール、3.5 オンス (12 個パック) Spice Supreme Celery Seed, Whole, 3.5-Ounce (Pack of 12)" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="Spice Supreme セロリシード、ホール、3.5 オンス (12 個パック) Spice Supreme Celery Seed, Whole, 3.5-Ounce (Pack of 12)" target="_top" data-link="item" href="https://item.rakuten.co.jp/glomarket/30770817/?variantId=162299774" data-rpp-url-copy="title" class="title-link--3Yuev">Spice Supreme セロリシード、ホール、3.5 オンス (12 個パック) Spice Supreme Celery Seed, Whole, 3.5-Ounce (Pack of 12)</a></h2></div><div><div class="price-plus-shipping-grid--3Pzf6"><div class="text-display--2xC98 type-body--2wvYb size-small--F9Ahp align-left--2U3p8 color-gray-darker--XKM3P line-height-x-medium--1dbOx layout-inline--2yTOi">12,512円(価格+送料)</div></div><div class="price-wrapper--10ccL"><div class="price--3zUvK price-with-price-plus-shipping--Bmgz2">11,032<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="paid-shipping-wrapper--1Sq8U"><span>+送料1,480円</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="376041" data-item-id="12536285" data-genre-id="101833" data-price="11032" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>110<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="content shipping-status"><div class="shipping">20日以内に国際発送、30日以内にお届け予定</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/glomarket/" class="_ellipsis" data-rpp-url-copy="shop">Glomarket</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="10059314" data-shop-id="361889" data-track-itemid="361889/10059314" data-track-variantid="wm-38263" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="1378" data-track-price-ranges="1378" data-track-doc-type="item" data-position-absolute="26" data-position-relative="26" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000361889-00010059314-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000361889-00010059314-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/womensfitness/wm-38263/?variantId=wm-38263" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/womensfitness/cabinet/item/wm_38001-40000/wm-38263.jpg?fitin=275:275" alt="ピルズベリー モイスト シュプリーム ストロベリーケーキミックス 432g Pillsbury Moist Supreme Strawberry Cake Mix, 15.25 oz Box 【お取り寄せ商品】" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="ピルズベリー モイスト シュプリーム ストロベリーケーキミックス 432g Pillsbury Moist Supreme Strawberry Cake Mix, 15.25 oz Box 【お取り寄せ商品】" target="_top" data-link="item" href="https://item.rakuten.co.jp/womensfitness/wm-38263/?variantId=wm-38263" data-rpp-url-copy="title" class="title-link--3Yuev">ピルズベリー モイスト シュプリーム ストロベリーケーキミックス 432g Pillsbury Moist Supreme Strawberry Cake Mix, 15.25 oz Box 【お取り寄せ商品】</a></h2></div><div><div class="price-plus-shipping-grid--3Pzf6"><div class="text-display--2xC98 type-body--2wvYb size-small--F9Ahp align-left--2U3p8 color-gray-darker--XKM3P line-height-x-medium--1dbOx layout-inline--2yTOi">1,958円(価格+送料)</div></div><div class="price-wrapper--10ccL"><div class="price--3zUvK price-with-price-plus-shipping--Bmgz2">1,378<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="paid-shipping-wrapper--1Sq8U"><span>+送料580円</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="361889" data-item-id="10059314" data-genre-id="204268" data-price="1378" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>13<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="sku-attributes--3mM2V sku-attributes-inline--3P4Fg sku-attributes-grid--1tJTZ"><div class="sku-attribute-values--1XyHd"><div class="inner-attributes-wrapper--RAOHZ"><div class="sku-attribute-value--3qtUM sku-attribute-value-selected--2uPgg">1個</div></div><div class="counter--2_hzl hidden-element--e1h3F">+0</div></div></div><div class="content shipping-status"><div class="shipping">ご注文後、米国より14日〜30日を目安に発送</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/womensfitness/" class="_ellipsis" data-rpp-url-copy="shop">Costopa</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="12591700" data-shop-id="376041" data-track-itemid="376041/12591700" data-track-variantid="163074363" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="5407" data-track-price-ranges="5407" data-track-doc-type="item" data-position-absolute="27" data-position-relative="27" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000376041-00012591700-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000376041-00012591700-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/glomarket/31138417/?variantId=163074363" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/glomarket/cabinet/07178412/07766763/imgb0d8my26r90.jpg?fitin=275:275" alt="MES Amis Salt &amp; Pepper Symphony Supreme + Cali Garlic Bliss Plus Pepper &amp; Parsley (2-Pack) Gourmet All-Purpose Seasoning &amp; Spice Blend" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="MES Amis Salt &amp; Pepper Symphony Supreme + Cali Garlic Bliss Plus Pepper &amp; Parsley (2-Pack) Gourmet All-Purpose Seasoning &amp; Spice Blend" target="_top" data-link="item" href="https://item.rakuten.co.jp/glomarket/31138417/?variantId=163074363" data-rpp-url-copy="title" class="title-link--3Yuev">MES Amis Salt &amp; Pepper Symphony Supreme + Cali Garlic Bliss Plus Pepper &amp; Parsley (2-Pack) Gourmet All-Purpose Seasoning &amp; Spice Blend</a></h2></div><div><div class="price-plus-shipping-grid--3Pzf6"><div class="text-display--2xC98 type-body--2wvYb size-small--F9Ahp align-left--2U3p8 color-gray-darker--XKM3P line-height-x-medium--1dbOx layout-inline--2yTOi">6,887円(価格+送料)</div></div><div class="price-wrapper--10ccL"><div class="price--3zUvK price-with-price-plus-shipping--Bmgz2">5,407<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="paid-shipping-wrapper--1Sq8U"><span>+送料1,480円</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="376041" data-item-id="12591700" data-genre-id="101833" data-price="5407" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>54<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="content shipping-status"><div class="shipping">20日以内に国際発送、30日以内にお届け予定</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/glomarket/" class="_ellipsis" data-rpp-url-copy="shop">Glomarket</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="12536655" data-shop-id="376041" data-track-itemid="376041/12536655" data-track-variantid="162300113" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="12287" data-track-price-ranges="12287" data-track-doc-type="item" data-position-absolute="28" data-position-relative="28" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000376041-00012536655-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000376041-00012536655-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/glomarket/30771038/?variantId=162300113" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/glomarket/cabinet/07178412/07761621/imgb002ye5ipm0.jpg?fitin=275:275" alt="Spice Supreme Dill Seeds Case Pack 12" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="Spice Supreme Dill Seeds Case Pack 12" target="_top" data-link="item" href="https://item.rakuten.co.jp/glomarket/30771038/?variantId=162300113" data-rpp-url-copy="title" class="title-link--3Yuev">Spice Supreme Dill Seeds Case Pack 12</a></h2></div><div><div class="price-plus-shipping-grid--3Pzf6"><div class="text-display--2xC98 type-body--2wvYb size-small--F9Ahp align-left--2U3p8 color-gray-darker--XKM3P line-height-x-medium--1dbOx layout-inline--2yTOi">13,767円(価格+送料)</div></div><div class="price-wrapper--10ccL"><div class="price--3zUvK price-with-price-plus-shipping--Bmgz2">12,287<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="paid-shipping-wrapper--1Sq8U"><span>+送料1,480円</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="376041" data-item-id="12536655" data-genre-id="101833" data-price="12287" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>122<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="content shipping-status"><div class="shipping">20日以内に国際発送、30日以内にお届け予定</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/glomarket/" class="_ellipsis" data-rpp-url-copy="shop">Glomarket</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="12474933" data-shop-id="376041" data-track-itemid="376041/12474933" data-track-variantid="162166651" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="7671" data-track-price-ranges="7671" data-track-doc-type="item" data-position-absolute="29" data-position-relative="29" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000376041-00012474933-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000376041-00012474933-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/glomarket/30720871/?variantId=162166651" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/glomarket/cabinet/07178412/07722637/imgb07fmftnqp0.jpg?fitin=275:275" alt="Simple Brilliance Supreme Spice Starter Set #1 with 6 Essential Spices for Cooking Basics - 6 Piece Spice Gift Set Includes Chili Powder, Onion Powder, Garlic Powder, Paprika, Cayenne Pepper and Cinnamon" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="Simple Brilliance Supreme Spice Starter Set #1 with 6 Essential Spices for Cooking Basics - 6 Piece Spice Gift Set Includes Chili Powder, Onion Powder, Garlic Powder, Paprika, Cayenne Pepper and Cinnamon" target="_top" data-link="item" href="https://item.rakuten.co.jp/glomarket/30720871/?variantId=162166651" data-rpp-url-copy="title" class="title-link--3Yuev">Simple Brilliance Supreme Spice Starter Set #1 with 6 Essential Spices for Cooking Basics - 6 Piece Spice Gift Set Includes Chili Powder, Onion Powder, Garlic Powder, Paprika, Cayenne Pepper and Cinnamon</a></h2></div><div><div class="price-plus-shipping-grid--3Pzf6"><div class="text-display--2xC98 type-body--2wvYb size-small--F9Ahp align-left--2U3p8 color-gray-darker--XKM3P line-height-x-medium--1dbOx layout-inline--2yTOi">9,151円(価格+送料)</div></div><div class="price-wrapper--10ccL"><div class="price--3zUvK price-with-price-plus-shipping--Bmgz2">7,671<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="paid-shipping-wrapper--1Sq8U"><span>+送料1,480円</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="376041" data-item-id="12474933" data-genre-id="101833" data-price="7671" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>76<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="content shipping-status"><div class="shipping">20日以内に国際発送、30日以内にお届け予定</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/glomarket/" class="_ellipsis" data-rpp-url-copy="shop">Glomarket</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="10059348" data-shop-id="361889" data-track-itemid="361889/10059348" data-track-variantid="wm-60271" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="1378" data-track-price-ranges="1378" data-track-doc-type="item" data-position-absolute="30" data-position-relative="30" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000361889-00010059348-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000361889-00010059348-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/womensfitness/wm-60271/?variantId=wm-60271" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/womensfitness/cabinet/item/wm_60001-62000/wm-60271.jpg?fitin=275:275" alt="ピルズベリー モイスト シュプリーム ジャーマンチョコレートケーキミックス 432g Pillsbury Moist Supreme German Chocolate Cake Mix, 15.25 oz Box 【お取り寄せ商品】" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="ピルズベリー モイスト シュプリーム ジャーマンチョコレートケーキミックス 432g Pillsbury Moist Supreme German Chocolate Cake Mix, 15.25 oz Box 【お取り寄せ商品】" target="_top" data-link="item" href="https://item.rakuten.co.jp/womensfitness/wm-60271/?variantId=wm-60271" data-rpp-url-copy="title" class="title-link--3Yuev">ピルズベリー モイスト シュプリーム ジャーマンチョコレートケーキミックス 432g Pillsbury Moist Supreme German Chocolate Cake Mix, 15.25 oz Box 【お取り寄せ商品】</a></h2></div><div><div class="price-plus-shipping-grid--3Pzf6"><div class="text-display--2xC98 type-body--2wvYb size-small--F9Ahp align-left--2U3p8 color-gray-darker--XKM3P line-height-x-medium--1dbOx layout-inline--2yTOi">1,958円(価格+送料)</div></div><div class="price-wrapper--10ccL"><div class="price--3zUvK price-with-price-plus-shipping--Bmgz2">1,378<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="paid-shipping-wrapper--1Sq8U"><span>+送料580円</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="361889" data-item-id="10059348" data-genre-id="204268" data-price="1378" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>13<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="sku-attributes--3mM2V sku-attributes-inline--3P4Fg sku-attributes-grid--1tJTZ"><div class="sku-attribute-values--1XyHd"><div class="inner-attributes-wrapper--RAOHZ"><div class="sku-attribute-value--3qtUM sku-attribute-value-selected--2uPgg">1個</div></div><div class="counter--2_hzl hidden-element--e1h3F">+0</div></div></div><div class="content shipping-status"><div class="shipping">ご注文後、米国より14日〜30日を目安に発送</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/womensfitness/" class="_ellipsis" data-rpp-url-copy="shop">Costopa</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="12520293" data-shop-id="376041" data-track-itemid="376041/12520293" data-track-variantid="162297892" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="7669" data-track-price-ranges="7669" data-track-doc-type="item" data-position-absolute="31" data-position-relative="31" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000376041-00012520293-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000376041-00012520293-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/glomarket/30769709/?variantId=162297892" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/glomarket/cabinet/07178412/07759530/imgb00b063ajg0.jpg?fitin=275:275" alt="スパイス シュプリーム ローリエ (12 枚入り) 12 枚入り Gel Spice Spice Supreme Bay Leaves (Pack Of 12) Pack Of 12 Pcs" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="スパイス シュプリーム ローリエ (12 枚入り) 12 枚入り Gel Spice Spice Supreme Bay Leaves (Pack Of 12) Pack Of 12 Pcs" target="_top" data-link="item" href="https://item.rakuten.co.jp/glomarket/30769709/?variantId=162297892" data-rpp-url-copy="title" class="title-link--3Yuev">スパイス シュプリーム ローリエ (12 枚入り) 12 枚入り Gel Spice Spice Supreme Bay Leaves (Pack Of 12) Pack Of 12 Pcs</a></h2></div><div><div class="price-plus-shipping-grid--3Pzf6"><div class="text-display--2xC98 type-body--2wvYb size-small--F9Ahp align-left--2U3p8 color-gray-darker--XKM3P line-height-x-medium--1dbOx layout-inline--2yTOi">9,149円(価格+送料)</div></div><div class="price-wrapper--10ccL"><div class="price--3zUvK price-with-price-plus-shipping--Bmgz2">7,669<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="paid-shipping-wrapper--1Sq8U"><span>+送料1,480円</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="376041" data-item-id="12520293" data-genre-id="101833" data-price="7669" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>76<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="content shipping-status"><div class="shipping">20日以内に国際発送、30日以内にお届け予定</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/glomarket/" class="_ellipsis" data-rpp-url-copy="shop">Glomarket</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="12536789" data-shop-id="376041" data-track-itemid="376041/12536789" data-track-variantid="162300269" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="10004" data-track-price-ranges="10004" data-track-doc-type="item" data-position-absolute="32" data-position-relative="32" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000376041-00012536789-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000376041-00012536789-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/glomarket/30771130/?variantId=162300269" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/glomarket/cabinet/07178412/07761621/imgb002yeedr60.jpg?fitin=275:275" alt="スパイス シュプリーム スパイス シュプリーム ガーリックソルト 12個入り Spice Supreme Spice Supreme Garlic Salt- Case of 12" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="スパイス シュプリーム スパイス シュプリーム ガーリックソルト 12個入り Spice Supreme Spice Supreme Garlic Salt- Case of 12" target="_top" data-link="item" href="https://item.rakuten.co.jp/glomarket/30771130/?variantId=162300269" data-rpp-url-copy="title" class="title-link--3Yuev">スパイス シュプリーム スパイス シュプリーム ガーリックソルト 12個入り Spice Supreme Spice Supreme Garlic Salt- Case of 12</a></h2></div><div><div class="price-plus-shipping-grid--3Pzf6"><div class="text-display--2xC98 type-body--2wvYb size-small--F9Ahp align-left--2U3p8 color-gray-darker--XKM3P line-height-x-medium--1dbOx layout-inline--2yTOi">11,484円(価格+送料)</div></div><div class="price-wrapper--10ccL"><div class="price--3zUvK price-with-price-plus-shipping--Bmgz2">10,004<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="paid-shipping-wrapper--1Sq8U"><span>+送料1,480円</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="376041" data-item-id="12536789" data-genre-id="101833" data-price="10004" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>100<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="content shipping-status"><div class="shipping">20日以内に国際発送、30日以内にお届け予定</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/glomarket/" class="_ellipsis" data-rpp-url-copy="shop">Glomarket</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="12456324" data-shop-id="376041" data-track-itemid="376041/12456324" data-track-variantid="162278160" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="5815" data-track-price-ranges="5815" data-track-doc-type="item" data-position-absolute="33" data-position-relative="33" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000376041-00012456324-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000376041-00012456324-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/glomarket/31478200/?variantId=162278160" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/glomarket/cabinet/07178412/07846741/imgb0078tp8620.jpg?fitin=275:275" alt="Pillsbury Moist Supreme Sugar Free Classic Yellow Premium Cake Mix (Pack of 2)" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="Pillsbury Moist Supreme Sugar Free Classic Yellow Premium Cake Mix (Pack of 2)" target="_top" data-link="item" href="https://item.rakuten.co.jp/glomarket/31478200/?variantId=162278160" data-rpp-url-copy="title" class="title-link--3Yuev">Pillsbury Moist Supreme Sugar Free Classic Yellow Premium Cake Mix (Pack of 2)</a></h2></div><div><div class="price-plus-shipping-grid--3Pzf6"><div class="text-display--2xC98 type-body--2wvYb size-small--F9Ahp align-left--2U3p8 color-gray-darker--XKM3P line-height-x-medium--1dbOx layout-inline--2yTOi">7,295円(価格+送料)</div></div><div class="price-wrapper--10ccL"><div class="price--3zUvK price-with-price-plus-shipping--Bmgz2">5,815<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="paid-shipping-wrapper--1Sq8U"><span>+送料1,480円</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="376041" data-item-id="12456324" data-genre-id="101833" data-price="5815" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>58<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="content shipping-status"><div class="shipping">20日以内に国際発送、30日以内にお届け予定</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/glomarket/" class="_ellipsis" data-rpp-url-copy="shop">Glomarket</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="12535814" data-shop-id="376041" data-track-itemid="376041/12535814" data-track-variantid="162315827" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="11323" data-track-price-ranges="11323" data-track-doc-type="item" data-position-absolute="34" data-position-relative="34" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000376041-00012535814-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000376041-00012535814-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/glomarket/30783029/?variantId=162315827" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/glomarket/cabinet/07178412/07761621/imgb002xuvers0.jpg?fitin=275:275" alt="スパイス シュプリーム チキン シーズニング ケース パック 12 Spice Supreme Poultry Seasoning Case Pack 12" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="スパイス シュプリーム チキン シーズニング ケース パック 12 Spice Supreme Poultry Seasoning Case Pack 12" target="_top" data-link="item" href="https://item.rakuten.co.jp/glomarket/30783029/?variantId=162315827" data-rpp-url-copy="title" class="title-link--3Yuev">スパイス シュプリーム チキン シーズニング ケース パック 12 Spice Supreme Poultry Seasoning Case Pack 12</a></h2></div><div><div class="price-plus-shipping-grid--3Pzf6"><div class="text-display--2xC98 type-body--2wvYb size-small--F9Ahp align-left--2U3p8 color-gray-darker--XKM3P line-height-x-medium--1dbOx layout-inline--2yTOi">12,803円(価格+送料)</div></div><div class="price-wrapper--10ccL"><div class="price--3zUvK price-with-price-plus-shipping--Bmgz2">11,323<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="paid-shipping-wrapper--1Sq8U"><span>+送料1,480円</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="376041" data-item-id="12535814" data-genre-id="101833" data-price="11323" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>113<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="content shipping-status"><div class="shipping">20日以内に国際発送、30日以内にお届け予定</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/glomarket/" class="_ellipsis" data-rpp-url-copy="shop">Glomarket</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="12537272" data-shop-id="376041" data-track-itemid="376041/12537272" data-track-variantid="162316390" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="6932" data-track-price-ranges="6932" data-track-doc-type="item" data-position-absolute="35" data-position-relative="35" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000376041-00012537272-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000376041-00012537272-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/glomarket/30783425/?variantId=162316390" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/glomarket/cabinet/07178412/07761625/imgb00eccq3hg0.jpg?fitin=275:275" alt="Spice Supreme パプリカ、2.5 オンス、プラスチック シェーカー (6 個パック) Spice Supreme paprika, 2.5-oz., plastic shaker (Pack of 6)" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="Spice Supreme パプリカ、2.5 オンス、プラスチック シェーカー (6 個パック) Spice Supreme paprika, 2.5-oz., plastic shaker (Pack of 6)" target="_top" data-link="item" href="https://item.rakuten.co.jp/glomarket/30783425/?variantId=162316390" data-rpp-url-copy="title" class="title-link--3Yuev">Spice Supreme パプリカ、2.5 オンス、プラスチック シェーカー (6 個パック) Spice Supreme paprika, 2.5-oz., plastic shaker (Pack of 6)</a></h2></div><div><div class="price-plus-shipping-grid--3Pzf6"><div class="text-display--2xC98 type-body--2wvYb size-small--F9Ahp align-left--2U3p8 color-gray-darker--XKM3P line-height-x-medium--1dbOx layout-inline--2yTOi">8,412円(価格+送料)</div></div><div class="price-wrapper--10ccL"><div class="price--3zUvK price-with-price-plus-shipping--Bmgz2">6,932<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="paid-shipping-wrapper--1Sq8U"><span>+送料1,480円</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="376041" data-item-id="12537272" data-genre-id="101833" data-price="6932" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>69<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="content shipping-status"><div class="shipping">20日以内に国際発送、30日以内にお届け予定</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/glomarket/" class="_ellipsis" data-rpp-url-copy="shop">Glomarket</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="12509171" data-shop-id="376041" data-track-itemid="376041/12509171" data-track-variantid="162296653" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="3436" data-track-price-ranges="3436" data-track-doc-type="item" data-position-absolute="36" data-position-relative="36" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000376041-00012509171-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000376041-00012509171-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/glomarket/30769058/?variantId=162296653" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/glomarket/cabinet/07178412/07753324/imgb01jsiskce0.jpg?fitin=275:275" alt="Spice Supreme Bay Leaves" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="Spice Supreme Bay Leaves" target="_top" data-link="item" href="https://item.rakuten.co.jp/glomarket/30769058/?variantId=162296653" data-rpp-url-copy="title" class="title-link--3Yuev">Spice Supreme Bay Leaves</a></h2></div><div><div class="price-plus-shipping-grid--3Pzf6"><div class="text-display--2xC98 type-body--2wvYb size-small--F9Ahp align-left--2U3p8 color-gray-darker--XKM3P line-height-x-medium--1dbOx layout-inline--2yTOi">4,916円(価格+送料)</div></div><div class="price-wrapper--10ccL"><div class="price--3zUvK price-with-price-plus-shipping--Bmgz2">3,436<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="paid-shipping-wrapper--1Sq8U"><span>+送料1,480円</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="376041" data-item-id="12509171" data-genre-id="101833" data-price="3436" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>34<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="content shipping-status"><div class="shipping">20日以内に国際発送、30日以内にお届け予定</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/glomarket/" class="_ellipsis" data-rpp-url-copy="shop">Glomarket</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="12561126" data-shop-id="376041" data-track-itemid="376041/12561126" data-track-variantid="162768828" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="6703" data-track-price-ranges="6703" data-track-doc-type="item" data-position-absolute="37" data-position-relative="37" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000376041-00012561126-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000376041-00012561126-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/glomarket/30838982/?variantId=162768828" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/glomarket/cabinet/07178412/07764156/imgb0dzdmhcdz0.jpg?fitin=275:275" alt="Generic 2 pack of Seasoning Herbs and Spices You Choose Your Flavor by Spice Supreme (3.5oz, Greens Seasoning)" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="Generic 2 pack of Seasoning Herbs and Spices You Choose Your Flavor by Spice Supreme (3.5oz, Greens Seasoning)" target="_top" data-link="item" href="https://item.rakuten.co.jp/glomarket/30838982/?variantId=162768828" data-rpp-url-copy="title" class="title-link--3Yuev">Generic 2 pack of Seasoning Herbs and Spices You Choose Your Flavor by Spice Supreme (3.5oz, Greens Seasoning)</a></h2></div><div><div class="price-plus-shipping-grid--3Pzf6"><div class="text-display--2xC98 type-body--2wvYb size-small--F9Ahp align-left--2U3p8 color-gray-darker--XKM3P line-height-x-medium--1dbOx layout-inline--2yTOi">8,183円(価格+送料)</div></div><div class="price-wrapper--10ccL"><div class="price--3zUvK price-with-price-plus-shipping--Bmgz2">6,703<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="paid-shipping-wrapper--1Sq8U"><span>+送料1,480円</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="376041" data-item-id="12561126" data-genre-id="101833" data-price="6703" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>67<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="content shipping-status"><div class="shipping">20日以内に国際発送、30日以内にお届け予定</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/glomarket/" class="_ellipsis" data-rpp-url-copy="shop">Glomarket</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="12451534" data-shop-id="376041" data-track-itemid="376041/12451534" data-track-variantid="162144239" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="2318" data-track-price-ranges="2318" data-track-doc-type="item" data-position-absolute="38" data-position-relative="38" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000376041-00012451534-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000376041-00012451534-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/glomarket/30701519/?variantId=162144239" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/glomarket/cabinet/07178412/07829384/imgb009ercfgm0.jpg?fitin=275:275" alt="Spice Supreme MSG グルタミン酸ナトリウム、プラスチック シェーカー、4.25 オンス Spice Supreme M.S.G. Monosodium Glutamate, plastic shaker, 4.25-oz." data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="Spice Supreme MSG グルタミン酸ナトリウム、プラスチック シェーカー、4.25 オンス Spice Supreme M.S.G. Monosodium Glutamate, plastic shaker, 4.25-oz." target="_top" data-link="item" href="https://item.rakuten.co.jp/glomarket/30701519/?variantId=162144239" data-rpp-url-copy="title" class="title-link--3Yuev">Spice Supreme MSG グルタミン酸ナトリウム、プラスチック シェーカー、4.25 オンス Spice Supreme M.S.G. Monosodium Glutamate, plastic shaker, 4.25-oz.</a></h2></div><div><div class="price-plus-shipping-grid--3Pzf6"><div class="text-display--2xC98 type-body--2wvYb size-small--F9Ahp align-left--2U3p8 color-gray-darker--XKM3P line-height-x-medium--1dbOx layout-inline--2yTOi">3,798円(価格+送料)</div></div><div class="price-wrapper--10ccL"><div class="price--3zUvK price-with-price-plus-shipping--Bmgz2">2,318<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="paid-shipping-wrapper--1Sq8U"><span>+送料1,480円</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="376041" data-item-id="12451534" data-genre-id="101833" data-price="2318" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>23<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="content shipping-status"><div class="shipping">20日以内に国際発送、30日以内にお届け予定</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/glomarket/" class="_ellipsis" data-rpp-url-copy="shop">Glomarket</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="10000259" data-shop-id="411262" data-track-itemid="411262/10000259" data-track-variantid="436" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="1200" data-track-price-ranges="1200" data-track-doc-type="item" data-position-absolute="39" data-position-relative="39" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000411262-00010000259-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000411262-00010000259-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/spice-market/436/?variantId=436" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/spice-market/cabinet/spice/juniper-berries/436_1000_1500px.jpg?fitin=275:275" alt="ジュニパーベリー 100g 乾燥 スパイス ホール 粒 ドライ ハーブ 香辛料 スパイスミックス 料理 アロマ ポプリ クリスマス" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="ジュニパーベリー 100g 乾燥 スパイス ホール 粒 ドライ ハーブ 香辛料 スパイスミックス 料理 アロマ ポプリ クリスマス" target="_top" data-link="item" href="https://item.rakuten.co.jp/spice-market/436/?variantId=436" data-rpp-url-copy="title" class="title-link--3Yuev">ジュニパーベリー 100g 乾燥 スパイス ホール 粒 ドライ ハーブ 香辛料 スパイスミックス 料理 アロマ ポプリ クリスマス</a></h2></div><div><div class="price-wrapper--10ccL"><div class="price--3zUvK">1,200<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="free-shipping-wrapper--N2A7-" data-tooltip="help-shipping"><span class="free-shipping-label--1shop">送料無料</span></span></div><span class="unit-price--3WqCF">12.0円/g (100g)</span><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="411262" data-item-id="10000259" data-genre-id="100309" data-price="1200" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>11<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="sku-attributes--3mM2V sku-attributes-inline--3P4Fg sku-attributes-grid--1tJTZ"><div class="sku-attribute-values--1XyHd"><div class="inner-attributes-wrapper--RAOHZ"><div class="sku-attribute-value--3qtUM sku-attribute-value-selected--2uPgg">1個</div></div><div class="counter--2_hzl hidden-element--e1h3F">+0</div></div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/spice-market/" class="_ellipsis" data-rpp-url-copy="shop">スパイスマーケット</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="12448475" data-shop-id="376041" data-track-itemid="376041/12448475" data-track-variantid="7048422" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="4992" data-track-price-ranges="4992" data-track-doc-type="item" data-position-absolute="40" data-position-relative="40" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000376041-00012448475-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000376041-00012448475-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/glomarket/15215089/?variantId=7048422" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/glomarket/cabinet/07178412/07716394/imgb008vt10d80.jpg?fitin=275:275" alt="ピルズベリー モイスト シュプリーム ケーキ ミックス クラシック イエロー Pillsbury Moist Supreme Cake Mix Classic Yellow" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="ピルズベリー モイスト シュプリーム ケーキ ミックス クラシック イエロー Pillsbury Moist Supreme Cake Mix Classic Yellow" target="_top" data-link="item" href="https://item.rakuten.co.jp/glomarket/15215089/?variantId=7048422" data-rpp-url-copy="title" class="title-link--3Yuev">ピルズベリー モイスト シュプリーム ケーキ ミックス クラシック イエロー Pillsbury Moist Supreme Cake Mix Classic Yellow</a></h2></div><div><div class="price-plus-shipping-grid--3Pzf6"><div class="text-display--2xC98 type-body--2wvYb size-small--F9Ahp align-left--2U3p8 color-gray-darker--XKM3P line-height-x-medium--1dbOx layout-inline--2yTOi">6,472円(価格+送料)</div></div><div class="price-wrapper--10ccL"><div class="price--3zUvK price-with-price-plus-shipping--Bmgz2">4,992<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="paid-shipping-wrapper--1Sq8U"><span>+送料1,480円</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="376041" data-item-id="12448475" data-genre-id="101833" data-price="4992" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>49<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="content shipping-status"><div class="shipping">20日以内に国際発送、30日以内にお届け予定</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/glomarket/" class="_ellipsis" data-rpp-url-copy="shop">Glomarket</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="10022647" data-shop-id="241630" data-track-itemid="241630/10022647" data-track-variantid="16042" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="313" data-track-price-ranges="313" data-track-doc-type="item" data-position-absolute="41" data-position-relative="41" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000241630-00010022647-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000241630-00010022647-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/kyodai/16042/?variantId=16042" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/kyodai/cabinet/kyodai01/16042-1000-1.jpg?fitin=275:275" alt="ターメリック Palillo 30g ペルーシェフ 【ハーブ】【スパイス】【カレー粉】" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="ターメリック Palillo 30g ペルーシェフ 【ハーブ】【スパイス】【カレー粉】" target="_top" data-link="item" href="https://item.rakuten.co.jp/kyodai/16042/?variantId=16042" data-rpp-url-copy="title" class="title-link--3Yuev">ターメリック Palillo 30g ペルーシェフ 【ハーブ】【スパイス】【カレー粉】</a></h2></div><div><div class="price-plus-shipping-grid--3Pzf6"><div class="text-display--2xC98 type-body--2wvYb size-small--F9Ahp align-left--2U3p8 color-gray-darker--XKM3P line-height-x-medium--1dbOx layout-inline--2yTOi">963円(価格+送料)</div></div><div class="price-wrapper--10ccL"><div class="price--3zUvK price-with-price-plus-shipping--Bmgz2">313<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="paid-shipping-wrapper--1Sq8U"><span>+送料650円</span></span></div><span class="unit-price--3WqCF">10.5円/g (30g)</span><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="241630" data-item-id="10022647" data-genre-id="100309" data-price="313" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>2<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="sku-attributes--3mM2V sku-attributes-inline--3P4Fg sku-attributes-grid--1tJTZ"><div class="sku-attribute-values--1XyHd"><div class="inner-attributes-wrapper--RAOHZ"><div class="sku-attribute-value--3qtUM sku-attribute-value-selected--2uPgg">1個</div></div><div class="counter--2_hzl hidden-element--e1h3F">+0</div></div></div><div class="content shipping-status"><div class="shipping">13:00までの注文で<b>最短9/30(翌日)</b>お届け</div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/kyodai/" class="_ellipsis" data-rpp-url-copy="shop">キョウダイ マーケット</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="12491905" data-shop-id="376041" data-track-itemid="376041/12491905" data-track-variantid="162311133" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="3602" data-track-price-ranges="3602" data-track-doc-type="item" data-position-absolute="42" data-position-relative="42" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000376041-00012491905-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000376041-00012491905-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/glomarket/30778356/?variantId=162311133" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/glomarket/cabinet/07178412/07735860/imgb00cxvpt3w0.jpg?fitin=275:275" alt="Spice Supreme Garlic Powder" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="Spice Supreme Garlic Powder" target="_top" data-link="item" href="https://item.rakuten.co.jp/glomarket/30778356/?variantId=162311133" data-rpp-url-copy="title" class="title-link--3Yuev">Spice Supreme Garlic Powder</a></h2></div><div><div class="price-plus-shipping-grid--3Pzf6"><div class="text-display--2xC98 type-body--2wvYb size-small--F9Ahp align-left--2U3p8 color-gray-darker--XKM3P line-height-x-medium--1dbOx layout-inline--2yTOi">5,082円(価格+送料)</div></div><div class="price-wrapper--10ccL"><div class="price--3zUvK price-with-price-plus-shipping--Bmgz2">3,602<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="paid-shipping-wrapper--1Sq8U"><span>+送料1,480円</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="376041" data-item-id="12491905" data-genre-id="101833" data-price="3602" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>36<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="content shipping-status"><div class="shipping">20日以内に国際発送、30日以内にお届け予定</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/glomarket/" class="_ellipsis" data-rpp-url-copy="shop">Glomarket</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="12581674" data-shop-id="376041" data-track-itemid="376041/12581674" data-track-variantid="163065971" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="11532" data-track-price-ranges="11532" data-track-doc-type="item" data-position-absolute="43" data-position-relative="43" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000376041-00012581674-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000376041-00012581674-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/glomarket/31130070/?variantId=163065971" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/glomarket/cabinet/07178412/07765175/imgb002yeci960.jpg?fitin=275:275" alt="Spice Supreme Bay Leaves, .5-Ounce (Pack of 12)" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="Spice Supreme Bay Leaves, .5-Ounce (Pack of 12)" target="_top" data-link="item" href="https://item.rakuten.co.jp/glomarket/31130070/?variantId=163065971" data-rpp-url-copy="title" class="title-link--3Yuev">Spice Supreme Bay Leaves, .5-Ounce (Pack of 12)</a></h2></div><div><div class="price-plus-shipping-grid--3Pzf6"><div class="text-display--2xC98 type-body--2wvYb size-small--F9Ahp align-left--2U3p8 color-gray-darker--XKM3P line-height-x-medium--1dbOx layout-inline--2yTOi">13,012円(価格+送料)</div></div><div class="price-wrapper--10ccL"><div class="price--3zUvK price-with-price-plus-shipping--Bmgz2">11,532<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="paid-shipping-wrapper--1Sq8U"><span>+送料1,480円</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="376041" data-item-id="12581674" data-genre-id="101833" data-price="11532" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>115<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="content shipping-status"><div class="shipping">20日以内に国際発送、30日以内にお届け予定</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/glomarket/" class="_ellipsis" data-rpp-url-copy="shop">Glomarket</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="12473635" data-shop-id="376041" data-track-itemid="376041/12473635" data-track-variantid="59822758" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="3724" data-track-price-ranges="3724/5154" data-track-doc-type="item" data-position-absolute="44" data-position-relative="44" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000376041-00012473635-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000376041-00012473635-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/glomarket/31600978/" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/glomarket/cabinet/07178412/07860364/ovb53e995708.jpg?fitin=275:275" alt="マコーミック カリナリー フリーズドライ ジュニパーベリー 311g スパイス 調味料 料理用 ベリー類 McCormick Culinary Freeze Dried Juniper Berries, 11 oz - One 11 Ounce Container of Dried Juniper Berries for Cooking and Baking, P" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="マコーミック カリナリー フリーズドライ ジュニパーベリー 311g スパイス 調味料 料理用 ベリー類 McCormick Culinary Freeze Dried Juniper Berries, 11 oz - One 11 Ounce Container of Dried Juniper Berries for Cooking and Baking, P" target="_top" data-link="item" href="https://item.rakuten.co.jp/glomarket/31600978/" data-rpp-url-copy="title" class="title-link--3Yuev">マコーミック カリナリー フリーズドライ ジュニパーベリー 311g スパイス 調味料 料理用 ベリー類 McCormick Culinary Freeze Dried Juniper Berries, 11 oz - One 11 Ounce Container of Dried Juniper Berries for Cooking and Baking, P</a></h2></div><div><div class="price-plus-shipping-grid--3Pzf6"><div class="text-display--2xC98 type-body--2wvYb size-small--F9Ahp align-left--2U3p8 color-gray-darker--XKM3P line-height-x-medium--1dbOx layout-inline--2yTOi">5,204円〜 (価格+送料)</div></div><div class="price-wrapper--10ccL"><div class="price--3zUvK price-with-price-plus-shipping--Bmgz2">3,724<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円〜</span></div><span class="paid-shipping-wrapper--1Sq8U"><span>+送料1,480円</span></span></div><span class="unit-price--3WqCF">16.8円～/g (222g)</span><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="376041" data-item-id="12473635" data-genre-id="100309" data-price="3724" data-max-price="5154" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>37<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)<!-- -->〜</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="sku-attributes--3mM2V sku-attributes-inline--3P4Fg sku-attributes-grid--1tJTZ"><div class="sku-attribute-values--1XyHd"><div class="inner-attributes-wrapper--RAOHZ"><div class="sku-attribute-value--3qtUM sku-attribute-value-selected--2uPgg">1個</div></div><div class="counter--2_hzl hidden-element--e1h3F">+0</div></div></div><div class="content shipping-status"><div class="shipping">20日以内に国際発送、30日以内にお届け予定</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/glomarket/" class="_ellipsis" data-rpp-url-copy="shop">Glomarket</a></div></div><div class="dui-card searchresultitem overlay-control-wrapper--3KBO0 title-control-wrapper--1rzvX" data-id="10059418" data-shop-id="361889" data-track-itemid="361889/10059418" data-track-variantid="wm-60675" data-card-type="item" data-track-card="search" data-track-type="item" data-track-price="1378" data-track-price-ranges="1378" data-track-doc-type="item" data-position-absolute="45" data-position-relative="45" data-quickview="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?items=00000361889-00010059418-1&amp;lang=ja&amp;pref=tokyo&amp;res=qv&amp;v=3" data-quickview-id="00000361889-00010059418-1" data-rpp-links-overrides="null"><div class="image-wrapper--3eWn3"><a target="_top" href="https://item.rakuten.co.jp/womensfitness/wm-60675/?variantId=wm-60675" class="image-link-wrapper--3XCNg"><img src="https://tshop.r10s.jp/womensfitness/cabinet/item/wm_60001-62000/wm-60675.jpg?fitin=275:275" alt="ピルズベリー モイスト シュプリーム クリーミー アーモンド ケーキミックス 432g Pillsbury Moist Supreme Creamy Almond Cake Mix, 15.25 oz Box 【お取り寄せ商品】" data-rpp-url-copy="img" class="image--x5mNi"/></a><div class="overlay--1xhCU"><div class="overlay-button-container--2k0ip" qa-feature="quickview"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-left--2nGMK border-radius-bottom-left--37H7x block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-expand--2QOzE"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">もっと見る</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div><div style="flex:1"><div style="display:inline"><span class="reference--4whr_"><div class="overlay-button-container--2k0ip" qa-feature="bookmark"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><div class="overlay-button-inner-wrapper--3n5ZR"><button class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius-top-right--O8mV4 border-radius-bottom-right--7mx2C block--1_kw1 no-padding--287kX no-border--EYmX4 type-basic--1zz3X" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-s--19F4P common-favorite--FhNMV"></div></span></button></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- small-padding--1Ovod default--1WlHj top--21VnW" role="dialog"><div class="content--16uqH"><div class="tooltip-text-wrapper--2ZGrQ">お気に入りに登録</div></div><div class="arrow--3tC6p arrow-top--1pXoX" style="position:absolute"></div></div></div></div></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-m--3xd_q default-padding--G6DNf default--1WlHj bottom--BdH9s" role="dialog"><div class="content--16uqH"><div class="bookmark-error-content-wrapper--rMDdt" role="none"><div class="bookmark-error-title--3LdDU"><span class="text-container--2tSUW size-body-2--2JQY5 style-bold--1IVlx default-color--2fMVe">お気に入りに追加できませんでした</span><button class="button--3YE3u size-m--3cp-2 size-m-padding--3Wsz6 border-radius--11uG4 type-icon-link--3l5hw icon-link-size--1WC0d variant-gray--16m1y" aria-label="button" type="button"><span class="icon-container--2gssu"><div class="icon--2QZm2 size-xxl--3clzM color-gray-dark--2BGIw common-close-button--3N8Ga"></div></span></button></div><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-">新しく追加するには、5,000件未満に整理していただく必要があります。</span><span class="text-container--2tSUW default-size--1kRiQ display-block--317vD default-color--2fMVe paragraph-spacing--2y5N-"><a class="text-container--2tSUW default-size--1kRiQ color-azure--3mEAI style-underscore--2T7Fb link--1A1up" href="https://my.bookmark.rakuten.co.jp/" target="_self">お気に入り一覧へ</a></span></div></div></div></div></div></div></div><div class="title--2KRhr title-grid--18AUw"><h2 class="title-link-wrapper--25--s title-link-wrapper-grid--hGeiw"><a title="ピルズベリー モイスト シュプリーム クリーミー アーモンド ケーキミックス 432g Pillsbury Moist Supreme Creamy Almond Cake Mix, 15.25 oz Box 【お取り寄せ商品】" target="_top" data-link="item" href="https://item.rakuten.co.jp/womensfitness/wm-60675/?variantId=wm-60675" data-rpp-url-copy="title" class="title-link--3Yuev">ピルズベリー モイスト シュプリーム クリーミー アーモンド ケーキミックス 432g Pillsbury Moist Supreme Creamy Almond Cake Mix, 15.25 oz Box 【お取り寄せ商品】</a></h2></div><div><div class="price-plus-shipping-grid--3Pzf6"><div class="text-display--2xC98 type-body--2wvYb size-small--F9Ahp align-left--2U3p8 color-gray-darker--XKM3P line-height-x-medium--1dbOx layout-inline--2yTOi">1,958円(価格+送料)</div></div><div class="price-wrapper--10ccL"><div class="price--3zUvK price-with-price-plus-shipping--Bmgz2">1,378<span class="main-price-unit--1kKO8 main-price-unit-grid--1rP98">円</span></div><span class="paid-shipping-wrapper--1Sq8U"><span>+送料580円</span></span></div><div class="points--DNEud" data-component="bdg-point-display" data-shop-id="361889" data-item-id="10059418" data-genre-id="204268" data-price="1378" data-tax-included="true" data-trigger="breakdown-link" data-item-type="NORMAL" data-purchase-type="NORMAL" data-hometown-tax-eligible="false"><span>13<!-- -->ポイント<wbr/>(<!-- -->1<!-- -->倍)</span> <span class="help-icon--24_vB"><div class="icon--2QZm2 color-gray--2OAj4 common-info-filled--1yUUe"></div></span></div></div><div class="sku-attributes--3mM2V sku-attributes-inline--3P4Fg sku-attributes-grid--1tJTZ"><div class="sku-attribute-values--1XyHd"><div class="inner-attributes-wrapper--RAOHZ"><div class="sku-attribute-value--3qtUM sku-attribute-value-selected--2uPgg">1個</div></div><div class="counter--2_hzl hidden-element--e1h3F">+0</div></div></div><div class="content shipping-status"><div class="shipping">ご注文後、米国より14日〜30日を目安に発送</div></div><div class="grid-tags--OniCN"><div style="display:inline"><span class="reference--4whr_" tabindex="0" role="button"><span class="help-fsl clickable-tag--W1RiS" title="39(サンキュー)ショップ"><div class="container--1qQfH"><img width="64" height="16" src="https://r.r10s.jp/com/assets/resources/39shop-gray166c2d0dc30deccdb5f9d7a67133a7ed.svg" alt="San Kyu Shop"/></div></span></span><div style="position:absolute;left:0;top:0" class="popover-container--2AGxB size-s--11UG- default-padding--G6DNf default--1WlHj bottom-start--fsavj" role="dialog"><div class="content--16uqH"><div class="label-tooltip-content--1vg6_"><div>送料無料ラインを3,980円以下に設定したショップで3,980円以上購入すると、送料無料になります。特定商品・一部地域が対象外になる場合があります。</div><div class="label-tooltip-link--qOqHq"><a class="button--3YE3u size-xs--1qW4M size-xs-padding--1Ud4D border-radius--11uG4 no-padding--287kX type-link--3U8Az" aria-label="もっと詳しく" href="https://event.rakuten.co.jp/guide/freeshippingline" target="_self" aria-disabled="false" aria-pressed="false" tabindex="0"><span class="text--yi9he text-no-margin-left--eBlAc text-no-margin-right--1TaWZ text--16i20 font-family-fixed--3MlDo">もっと詳しく</span></a></div></div></div><div class="arrow--3tC6p arrow-bottom-start--1AHB0" style="position:absolute"></div></div></div></div><div class="content merchant _ellipsis"><i class="dui-icon -simpleshop"></i> <a target="_top" href="https://www.rakuten.co.jp/womensfitness/" class="_ellipsis" data-rpp-url-copy="shop">Costopa</a></div></div></div></div></div></div><div class="searchPagination"><div data-track-rpgn="1" data-comp-id-flat="pager"><div class="dui-container pagination _centered"><div class="dui-pagination"><a class="item -active currentPage">1</a><a href="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?p=2" class="item">2</a><a href="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?p=3" class="item">3</a><a href="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?p=4" class="item -last">4</a><a href="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/?p=2" class="item -next nextPage">次のページ</a></div></div></div></div><div class="adBta"><div></div></div><div class="browsingHistory"><div></div></div><div class="help"><div class="dui-container search-help"><div class="dui-list -horizontal"><div class="item"><div data-comp-id-flat="faq"><h3>お探しの商品が見つかりませんか?<!-- -->　〜 <!-- -->よくあるご質問<!-- --> 〜</h3><div class="dui-list"><span class="item">・ <a href="https://ichiba.faq.rakuten.net/detail/000006462">欲しい商品があるのですが名称がわかりません。どのように探せばよいですか？</a></span><span class="item">・ <a href="https://ichiba.faq.rakuten.net/detail/000006436">どのような商品検索方法があるのですか？</a></span><span class="item">・ <a href="https://ichiba.faq.rakuten.net/detail/000006426">検索しても、商品が一つも表示されません</a></span></div><p><a href="https://ichiba.faq.rakuten.net/category/search-order/search">もっと見る</a></p></div></div><div class="item"><h3>商品の検索でお困りの場合や、探し方について</h3><p><a target="_blank" rel="noopener noreferrer" href="https://chat.ichiba.faq.rakuten.co.jp/Search" class="dui-button -big _red staff" data-comp-id-flat="rakuten_staff_chat">楽天のスタッフに質問する</a></p><p class="_red">対話方式のチャットが開きます。</p></div></div></div></div><div class="realtimeSurvey"><div data-track-survey_version="ichiba_search-20221021" data-track-ref="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/" data-comp-id-flat="pc.main.bottom-realtime_survey"><div class="realtime-survey--3oqTT"><div class="spacer--3J57F full-width--2Ybxj padding-top-3xlarge--2uG1K"><div class="survey-trigger-wrapper--2JEwa survey-trigger-wrapper-border-gray--37uv_ survey-trigger-wrapper-background-white--2D55C"><div class="survey-trigger--3NPJA"><div class="text-display--2xC98 type-body--2wvYb size-x-large--2vpjt align-left--2U3p8 color-gray-darker--XKM3P line-height-x-medium--1dbOx layout-inline--2yTOi">このページの使いやすさはいかがでしたか？</div><div class="trigger-buttons--3DLtb"><div class="trigger-button--JaLSk trigger-button-red--1zGNF">1</div><div class="trigger-button--JaLSk trigger-button-orange--_R5JV">2</div><div class="trigger-button--JaLSk trigger-button-yellow--3JEk4">3</div><div class="trigger-button--JaLSk trigger-button-light-green--2H9wM">4</div><div class="trigger-button--JaLSk trigger-button-green--31fzr">5</div></div><div class="trigger-score-labels--3CO3m"><div class="text-display--2xC98 type-body--2wvYb size-small--F9Ahp align-left--2U3p8 color-gray-darker--XKM3P line-height-x-medium--1dbOx layout-inline--2yTOi">とても使いにくい</div><div class="text-display--2xC98 type-body--2wvYb size-small--F9Ahp align-left--2U3p8 color-gray-darker--XKM3P line-height-x-medium--1dbOx layout-inline--2yTOi">とても使いやすい</div></div></div></div></div></div></div></div><div class="relatedServices"><div class="dui-container relatedservices"><div class="dui-container" data-track-related_categories:ids="[555001,507756,204430]" data-comp-id-flat="related_categories"><h6>関連するジャンル</h6><div class="dui-breadcrumb service"><a class="section" href="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/101164/">ホビー</a><div class="dui-icon -rightarrow divider"><div class="icon--2QZm2 common-chevron-right--yQPT4"></div></div><a class="section" href="https://search.rakuten.co.jp/search/mall/%E3%82%B9%E3%83%91%E3%82%A4%E3%82%B7%E3%83%BC%E3%82%B9%E3%83%88%E3%83%BC%E3%83%A0+%E3%83%9A%E3%83%AA%E3%83%9A%E3%83%AA%E3%83%9F%E3%83%83%E3%82%AF%E3%82%B9+30g/101168/">喫煙具</a><div class="dui-icon -rightarrow divider"><div class="icon--2QZm2 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ペリペリ\",\"image\":[\"https:\u002F\u002Fthumbnail.image.rakuten.co.jp\u002F@0_mall\u002Fsunnynook\u002Fcabinet\u002Frakus136_0203\u002Fb0br4qltz500.jpg\"],\"offers\":{\"@type\":\"Offer\",\"price\":1871,\"priceCurrency\":\"JPY\"},\"url\":\"https:\u002F\u002Fitem.rakuten.co.jp\u002Fsunnynook\u002Fb0br4qltz5\u002F?variantId=b0br4qltz5&scid=seo-carousel-search\"}},{\"@type\":\"ListItem\",\"position\":7,\"item\":{\"@type\":\"Product\",\"name\":\"【シャグ刻葉】　スリードッグ・ペリク　30g　＆ライター等セット【B】〔ビター系〕\",\"image\":[\"https:\u002F\u002Fthumbnail.image.rakuten.co.jp\u002F@0_mall\u002Fplaza\u002Fcabinet\u002F01977426\u002Fimgrc0114011939.jpg\"],\"offers\":{\"@type\":\"Offer\",\"price\":1150,\"priceCurrency\":\"JPY\"},\"aggregateRating\":{\"@type\":\"AggregateRating\",\"ratingValue\":4.0,\"reviewCount\":1},\"url\":\"https:\u002F\u002Fitem.rakuten.co.jp\u002Fplaza\u002F111301\u002F?variantId=108676&scid=seo-carousel-search\"}},{\"@type\":\"ListItem\",\"position\":8,\"item\":{\"@type\":\"Product\",\"name\":\"【送料無料】SPICEDREAM PERIPERI MIX 50g瓶 ペリペリ\",\"image\":[\"https:\u002F\u002Fthumbnail.image.rakuten.co.jp\u002F@0_mall\u002Fdonzstore\u002Fcabinet\u002Frakus136_0203\u002Fb0br4qltz500.jpg\"],\"offers\":{\"@type\":\"Offer\",\"price\":1871,\"priceCurrency\":\"JPY\"},\"url\":\"https:\u002F\u002Fitem.rakuten.co.jp\u002Fdonzstore\u002Fb0br4qltz5\u002F?variantId=b0br4qltz5&scid=seo-carousel-search\"}},{\"@type\":\"ListItem\",\"position\":9,\"item\":{\"@type\":\"Product\",\"name\":\"Spice Supreme Pumpkin Pie Spice, 2.5-Ounce (Pack of 12)\",\"image\":[\"https:\u002F\u002Fthumbnail.image.rakuten.co.jp\u002F@0_mall\u002Fglomarket\u002Fcabinet\u002F07178412\u002F07768973\u002Fimgb00df5aczu0.jpg\"],\"offers\":{\"@type\":\"Offer\",\"price\":10877,\"priceCurrency\":\"JPY\"},\"url\":\"https:\u002F\u002Fitem.rakuten.co.jp\u002Fglomarket\u002F31297364\u002F?variantId=163235118&scid=seo-carousel-search\"}},{\"@type\":\"ListItem\",\"position\":10,\"item\":{\"@type\":\"Product\",\"name\":\"ブルーム スーパーベリー 粉末ドリンクミックス バラエティパック 3フレーバー 45袋（1個あたり15袋入り）砂糖不使用 Blume SuperBelly Hydration Drink Mix\",\"image\":[\"https:\u002F\u002Fthumbnail.image.rakuten.co.jp\u002F@0_mall\u002Ffounder\u002Fcabinet\u002Fmisako_y001\u002F01\u002Fmy20232035main.jpg\"],\"offers\":{\"@type\":\"Offer\",\"price\":23490,\"priceCurrency\":\"JPY\"},\"url\":\"https:\u002F\u002Fitem.rakuten.co.jp\u002Ffounder\u002Fmy20232035\u002F?variantId=my20232035&scid=seo-carousel-search\"}}]}"},"searchType":{"searchType":"lexical+semantic","showPureLexicalSearchTogglingLink":false,"searchTypeAfterToggle":null},"mockingData":null,"genreKaimawari":null,"ichibaFiltersGift":{"filters":[{"id":"gift-social-gift-filter","title":"ソーシャルギフト","options":[{"title":"ソーシャルギフト","param":"gift","value":"social_gift"}],"properties":[{"name":"help_title","value":"ソーシャルギフトとは"},{"name":"help_description","value":"相手の住所を知らなくても、SNSやメールなどでギフトが贈れます。"}]}]}},"featureFlags":{"quickViewActive":true},"env":{"urlPrefix":"https:\u002F\u002Fsearch.rakuten.co.jp"}},"locale":"ja"};
-window.__REWIRED_SCHEMAS__ = ["pc"];
-window.__LOGGER_PROXY_ROUTE__ = "\u002F_proxylog";
-window.__CHECKOUT_TOKEN__ = undefined;
-window.__LOCALE__ = "ja";</script><script id="__LOADABLE_REQUIRED_CHUNKS__" type="application/json">[]</script><script id="__LOADABLE_REQUIRED_CHUNKS___ext" type="application/json">{"namedChunks":[]}</script><script src="https://r.r10s.jp/com/assets/app/pages/search/javascript/pc-5187f30f684950db869e.bundle.js"></script><script src="https://r.r10s.jp/com/assets/app/pages/search/javascript/vendors-5187f30f684950db869e.bundle.js"></script><script src="https://r.r10s.jp/com/assets/app/pages/search/javascript/main-5187f30f684950db869e.bundle.js"></script><script src="https://r.r10s.jp/com/rat/js/ral-1.10.2.js"></script><script src="https://r.r10s.jp/com/rat/js/rat-main-1.12.5.js"></script><script src="https://r.r10s.jp/com/js/d/bdg/2.2/index-2.2.0.min.js"></script><div style="display:none"></div></body></html>
\ No newline at end of file
diff --git a/recognization/data/packshots/research/sku_l10/manifest.json b/recognization/data/packshots/research/sku_l10/manifest.json
deleted file mode 100644
index 2d79924..0000000
--- a/recognization/data/packshots/research/sku_l10/manifest.json
+++ /dev/null
@@ -1,22 +0,0 @@
-{
-  "sku": "sku_l10",
-  "canonical_product": "味の素 ピュアセレクト マヨネーズ 400g",
-  "packshot_path": "data/packshots/source/sku_l10.jpg",
-  "source_url": "https://thumbnail.image.rakuten.co.jp/@0_mall/matsukiyo/cabinet/d0042/4901001114030_1.jpg?_ex=1000x1000",
-  "source_page": "https://item.rakuten.co.jp/matsukiyo/4901001114030/",
-  "source_retailer": "楽天市場 / マツモトキヨシ楽天市場店",
-  "jan": "4901001114030",
-  "width": 1000,
-  "height": 1000,
-  "format": "JPEG",
-  "verification": {
-    "visual": "Front packshot visibly reads AJINOMOTO, ピュアセレクト, マヨネーズ, and 400g.",
-    "exact_product": true
-  },
-  "size_correction": {
-    "rejected_size": "450g",
-    "canonical_size": "400g",
-    "reason": "Ajinomoto's official current product page lists 250g, 400g, 600g, and 1kg, not 450g. Archived official Ajinomoto pages from 2008-2014 show the same lineup. The repository shelf photo add/image6.jpg and its shelf tag show ピュアセレクト マヨネーズ 400g; docs/research/2026-09-28-recognition-architecture/DECISION.md records the same mismatch. Retail searches for 450g returned Kewpie products or mixed listings, while JAN 4901001114030 consistently identifies Ajinomoto ピュアセレクト マヨネーズ 400g.",
-    "official_product_page": "https://www.ajinomoto.co.jp/products/detail/?ProductName=pureselect_1"
-  }
-}
diff --git a/recognization/data/packshots/source/ATTRIBUTION.json b/recognization/data/packshots/source/ATTRIBUTION.json
deleted file mode 100644
index 8335e61..0000000
--- a/recognization/data/packshots/source/ATTRIBUTION.json
+++ /dev/null
@@ -1,31 +0,0 @@
-{
-  "sku_l04": {
-    "url": "https://m.media-amazon.com/images/I/61yWDtDjDuL.jpg",
-    "product": "やまや めんたいマヨネーズタイプ 200g",
-    "source_page": "https://www.amazon.co.jp/dp/B0197VWHRG",
-    "notes": "Exact 200g product; major-retailer pack shot. Manufacturer: 株式会社やまやコミュニケーションズ."
-  },
-  "sku_l07": {
-    "url": "https://m.media-amazon.com/images/I/91ugfvmpCvL.jpg",
-    "product": "丸和油脂 ラッキーマヨネーズ 200g",
-    "source_page": "https://www.amazon.co.jp/dp/B0BHQW3VVP",
-    "notes": "Exact 200g product; major-retailer pack shot. JAN/part number 4978931401615, corroborated by Yahoo Shopping listing."
-  },
-  "sku_l10": {
-    "url": "https://thumbnail.image.rakuten.co.jp/@0_mall/matsukiyo/cabinet/d0042/4901001114030_1.jpg?_ex=1000x1000",
-    "product": "味の素 ピュアセレクト マヨネーズ 400g",
-    "source_page": "https://item.rakuten.co.jp/matsukiyo/4901001114030/",
-    "jan": "4901001114030",
-    "notes": "Exact 400g front packshot. The catalog's 450g was wrong: Ajinomoto's lineup is 250/400/600/1000g and the shelf tag in add/image6.jpg reads 400g. See data/packshots/research/sku_l10/manifest.json."
-  },
-  "sku_l06": {
-    "url": "https://m.media-amazon.com/images/I/81zckoUh+vL._AC_SL1500_.jpg",
-    "product": "キユーピー フィッテ 210g",
-    "source_page": "https://www.amazon.co.jp/dp/B08V2676LK",
-    "notes": "The current package (内臓脂肪を減らす). Replaced the superseded 内臓脂肪ケア view on 2026-09-29; matches the shelf facings in add/image6.jpg exactly. Corroborated by a second domain: https://store.shopping.yahoo.co.jp/ladydrugheartshop/4901577083600.html. See data/external-eval/kewpie/manifest.json."
-  },
-  "_provenance_gap": {
-    "skus": ["sku_l01", "sku_l02", "sku_l03", "sku_l05", "sku_l09", "sku_l11"],
-    "note": "These views were enrolled before this file existed and their source URLs were not recorded. What is verified about them: each is independent of the evaluation photo - template-matched against add/image6.jpg, the highest score across all six is 0.166, far below the 0.95 that identifies a crop of it - and each is 1000 px tall, above the enrollment floor. Their product identity is corroborated by their own OCR (data/packshots/source/ocr-reads.json) against the catalog names. Re-sourcing them with recorded URLs would close this; it is not a correctness gap, it is a traceability one."
-  }
-}
diff --git a/recognization/data/packshots/source/ocr-reads.json b/recognization/data/packshots/source/ocr-reads.json
deleted file mode 100644
index e19ab6b..0000000
--- a/recognization/data/packshots/source/ocr-reads.json
+++ /dev/null
@@ -1,10 +0,0 @@
-{
-"sku_l01.jpg": "卵を使って いない キューピーエッグケア e 乳成分 えび かに くるみ 特定原材料8品目不使用 そば 落花生 305g",
-"sku_l02.jpg": "機能性表示食品届出番号J1501 キューピー 血圧ケア アマニ油 マヨネーズ α-リノレン酸 商品中30% アマニ油配合 200g 食生活は、主食、主菜、副菜を基本に、食事のバランスを。",
-"sku_l03.jpg": "Spicy： 辛さとコクのおいしさ キューピー からしアヨネーズ ピリジと おいしい ® 200g",
-"sku_l05.jpg": "2つの製法で味わうコク コレステロール ゼロ キューピーゼロ ノンコレステロール カロリー 70第 当社マヨネーズ比 310g",
-"sku_l06.jpg": "機能性表示食品届出番号J1507 キューピー 内臓脂肪ケア フィツテ ローズヒップ由来 ティリロサイド 210g カロリー 50カット 兆社マヨネーズ比 食生活は、主食、主菜、副菜を基本に、食事のバランスを。",
-"sku_l09.jpg": "とれて3日以方の 各長算たまごとい。 ピュアセレクト コクうま カロリーカット 05 360g",
-"sku_l10.jpg": "Ay AJINOMOTO. とれて3日以内の 国産新鮮たまごだけき使用 ピュア セレリト マヨネーズ® Pure Select Mayonnaise 400g",
-"sku_l11.jpg": "キューピー New！ 卵黄ならではのコクとうま味 ※写真はイメージです。 カロリー 50% カット 当社マヨネーズ比 ® 400g"
-}
diff --git a/recognization/data/packshots/source/sku_l01.jpg b/recognization/data/packshots/source/sku_l01.jpg
deleted file mode 100644
index e35eb97..0000000
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diff --git a/recognization/data/packshots/source/sku_l04.jpg b/recognization/data/packshots/source/sku_l04.jpg
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diff --git a/recognization/data/packshots/source/sku_l09.jpg b/recognization/data/packshots/source/sku_l09.jpg
deleted file mode 100644
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diff --git a/recognization/data/packshots/source/sku_l10.jpg b/recognization/data/packshots/source/sku_l10.jpg
deleted file mode 100644
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diff --git a/recognization/data/packshots/source/sku_l11.jpg b/recognization/data/packshots/source/sku_l11.jpg
deleted file mode 100644
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diff --git a/recognization/demo-images/ATTRIBUTION.json b/recognization/demo-images/ATTRIBUTION.json
deleted file mode 100644
index 2712674..0000000
--- a/recognization/demo-images/ATTRIBUTION.json
+++ /dev/null
@@ -1,16 +0,0 @@
-[
- {
-  "file": "demo-image-1-mediya-dairy.jpg",
-  "title": "File:Azabudai-Hills---2025-04-05 015.jpg",
-  "license": "CC0",
-  "artist": "<a href=\"//commons.wikimedia.org/wiki/User:RuinDig\" title=\"User:RuinDig\">RuinDig/Yuki Uchida</a>",
-  "source": "https://commons.wikimedia.org/wiki/File%3AAzabudai-Hills---2025-04-05_015.jpg"
- },
- {
-  "file": "demo-image-2-milk-case.jpg",
-  "title": "File:Milk (2655574568).jpg",
-  "license": "CC BY 2.0",
-  "artist": "<a rel=\"nofollow\" class=\"external text\" href=\"https://www.flickr.com/people/36014533@N00\">yumtan</a>",
-  "source": "https://commons.wikimedia.org/wiki/File%3AMilk_%282655574568%29.jpg"
- }
-]
\ No newline at end of file
diff --git a/recognization/demo-images/README.md b/recognization/demo-images/README.md
deleted file mode 100644
index e5a118d..0000000
--- a/recognization/demo-images/README.md
+++ /dev/null
@@ -1,63 +0,0 @@
-# The two demo shelves
-
-Two real store photographs, chosen because they are dense and their products are
-visually distinct, with a store catalog enrolled for each. Both come from Wikimedia
-Commons under licences that allow reuse; `demo-images/ATTRIBUTION.json` records the
-author, licence and source of each.
-
-| | image 1 | image 2 |
-| --- | --- | --- |
-| file | `demo-images/demo-image-1-mediya-dairy.jpg` | `demo-images/demo-image-2-milk-case.jpg` |
-| scene | a MEDIYA dairy case | a supermarket milk case |
-| licence | **CC0** | **CC BY 2.0** |
-| detections | 21 products (13 price cards filtered out) | 56 products |
-| recognised | **16, covering all 14 products** | **49, covering all 13 products** |
-| store | `stock-mediya/` | `stock-milk150/` |
-
-## How to run them
-
-```bash
-cd recognization
-YOLO_OFFLINE=1 .venv/bin/python code/app.py \
-  --port 8090 --identity gallery --live-conf 0.25 \
-  --stock-dir stock-mediya          # or stock-milk150
-```
-
-Then POST the image, or open the web client and use **Photo** mode:
-
-```bash
-curl -s -X POST --data-binary @demo-images/demo-image-1-mediya-dairy.jpg \
-  -H 'content-type: image/jpeg' \
-  'http://127.0.0.1:8090/v1/recognize?classify=true&conf=0.25&tags=true' | python3 -m json.tool | head -30
-```
-
-## What the demo shows
-
-* **Every product on the shelf is boxed and named** - 小岩井 低脂肪牛乳,
-  荻山酪農 カフェ・オ・レ, 丹那 3.6牛乳, 木次 ミルクコーヒー, 木次 パスチャライズ牛乳,
-  キスキ ブラウン・スイス牛乳, 丹那 飲んでやさしいヨーグルト, 農場3.7牛乳【特選】,
-  MINOR FIGURES OAT, 北海道 低脂肪乳, タカナシ 低温殺菌牛乳, RUDE HEALTH Oat Organic,
-  小岩井 無脂肪牛乳, きすき ノンホモ牛乳 on the first; 明治 おいしい牛乳, ゆうパック
-  優良牛乳, 酪農家限定 低温殺菌牛乳, 北海道牛乳, MEGMILK, 毎日牛乳, タカナシ 低温殺菌牛乳,
-  タカナシ 殺菌低脂肪牛乳, 毎日骨太, カルシウム入り低脂肪乳, 無脂肪牛乳, 低脂肪牛乳, ミルク
-  on the second.
-* **Price cards are not boxed as products.** The detector finds them (a card is the
-  shape of a pack); their text gives them away - a card's characters are 30-44 %
-  digits and it prints the tax-excluded price (本体), a product's are 3-19 %. Measured
-  on image 1: 13 cards dropped, 21 products kept.
-* **What it cannot name, it does not invent.** Boxes on the dark shelf strips and on
-  cards come back as "check" (amber), not as a product name.
-
-## The honest caveat, for the demo
-
-Both shelves were **enrolled** - their products were read off the shelf and their
-crops became the store's catalog and its fine-tuned head, which is the product's own
-onboarding flow (a store is enrolled once, then scanned). So the recognition rate
-above is **in-distribution**: it is what the app does on a shelf it has been enrolled
-on, not on a shelf it has never seen. On shelves it has not been enrolled on, the
-measured behaviour is: detection transfers (46 of 54 unseen photos), identity does not
-(off-the-shelf embeddings reach ~25 % top-1 on fine-grained products; see
-`docs/EXTERNAL-EVAL.md`).
-
-The first image is CC0, so it can be used in any recording without attribution; the
-second is CC BY 2.0 and needs the credit in `ATTRIBUTION.json` if it appears on screen.
diff --git a/recognization/demo-images/demo-image-1-mediya-dairy.jpg b/recognization/demo-images/demo-image-1-mediya-dairy.jpg
deleted file mode 100644
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diff --git a/recognization/demo-images/demo-image-2-milk-case.jpg b/recognization/demo-images/demo-image-2-milk-case.jpg
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index 3e5cf1d..0000000
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diff --git a/recognization/detected_empty_slots.jpg b/recognization/detected_empty_slots.jpg
new file mode 100644
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diff --git a/recognization/detected_image.jpg b/recognization/detected_image.jpg
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diff --git a/recognization/docs/API.md b/recognization/docs/API.md
deleted file mode 100644
index 73911ee..0000000
--- a/recognization/docs/API.md
+++ /dev/null
@@ -1,449 +0,0 @@
-# Dorayaki recognition server API
-
-The computer-vision model (YOLO26n product detector + the SURF/SIFT, ORB, BRISK and
-Hybrid feature-matching classifier + the stock catalog) runs as a service. Capture
-clients talk to it over REST and one WebSocket. Any number of clients can connect at
-once; no client shares state with another.
-
-- Web client (served by the server itself): `http://127.0.0.1:8080/`
-- Reference non-browser client: `code/stream_client.py`
-- Interactive API docs: `http://127.0.0.1:8080/docs`
-- Planned extensions (price-tag OCR, reconciliation, compliance, mobile scan):
-  `docs/design-price-tags-and-compliance.md`
-
-## Running it
-
-```bash
-cd recognization
-uv pip install --python .venv/bin/python -r requirements-dev.txt   # first time
-.venv/bin/python code/app.py --port 8080
-```
-
-| Flag | Default | Meaning |
-| --- | --- | --- |
-| `--host` | `127.0.0.1` | Bind address. `0.0.0.0` exposes it on the LAN (set a token). |
-| `--port` | `8080` | Port. |
-| `--device` | `cpu` | `cpu`, `mps`, `cuda`, `cuda:0`. CPU is the measured default here. |
-| `--imgsz` | `640` | Detector input size. |
-| `--weights` | `runs/detect/train/weights/best.pt` | Model file. |
-| `--stock-dir` | `stock` | Catalog directory (images + `stock.csv`). |
-| `--classify-workers` | `5` | Classification thread pool size (measured default). |
-| `--live-method` | `hybrid` | Default method for live sessions. |
-| `--live-conf` | `0.1` | Detection threshold in live mode (the tracker gates). |
-| `--no-tags` | off | Disable price-tag OCR entirely (`DORAYAKI_LIVE_TAGS=0`). |
-| `DORAYAKI_OCR` | `auto` | OCR backend: `auto` (Vision on macOS), `vision`, `fake`, `none`. |
-| `DORAYAKI_TORCH_THREADS` | `4` | torch CPU threads; 0 leaves torch's default. |
-| `--max-sessions` | `4` | Concurrent live sessions. |
-| `--ssl-certfile` / `--ssl-keyfile` | – | HTTPS (needed for a phone camera). |
-| `--log-level` | `info` | Logging level. |
-
-Environment: `DORAYAKI_*` equivalents (`DORAYAKI_API_TOKEN`, `DORAYAKI_DEVICE`,
-`DORAYAKI_MAX_SESSIONS`, `DORAYAKI_CORS_ORIGINS`, `DORAYAKI_LIVE_INFLIGHT`, ...).
-Command-line flags win over environment.
-
-The model never phones home: the server sets `YOLO_OFFLINE=1` and
-`YOLO_AUTOINSTALL=false` before importing ultralytics, and `lap` is pinned in
-`requirements.txt` so the tracker never pip-installs anything mid-request.
-
-## Authentication
-
-When `DORAYAKI_API_TOKEN` is set, every `/v1` route except `GET /v1/health` requires
-`Authorization: Bearer <token>`, and `/v1/stream` takes `?token=<token>` (browsers
-cannot set WebSocket headers). The token is deliberately environment-only so it never
-lands in shell history, and the access log is off because it would print the query
-string. A token on a LAN bind is a speed bump, not security: prefer HTTPS.
-
-## Coordinates
-
-Boxes are `[x1, y1, x2, y2]` integers, origin top-left, in the pixel space of the
-image or frame the client sent. Responses echo
-`"coordinate_convention": "pixel-xyxy-origin-top-left"`.
-
-## Identity states
-
-One vocabulary for REST and WebSocket. **A product name is only ever attached to a
-`recognized` result** - the model's uncertainty is never presented as certainty.
-
-| `state` | Meaning | Has `sku_id`? |
-| --- | --- | --- |
-| `detected` | Product found, not classified yet (photo mode with `classify=false`) | no |
-| `unconfirmed` | Live: tracked, not enough frames to classify yet | no |
-| `identifying` | Live: classification in flight (reserved) | no |
-| `recognized` | Accepted unique catalog match | **yes** |
-| `needs_confirmation` | Ambiguous or weak match; `candidates` lists the top 3 | no |
-| `unrecognized` | Not in the catalog (or no matching evidence) | no |
-
-`identity_level` carries the raw decision: `accepted`, `ambiguous`, `unknown`,
-`rejected`.
-
-## Errors
-
-Every failure uses one shape:
-
-```json
-{"error": {"code": "IMAGE_EXPIRED", "message": "image 'img_x' is no longer available"}}
-```
-
-| Code | HTTP | When |
-| --- | --- | --- |
-| `BAD_REQUEST` | 400 | Malformed request, unknown algorithm, empty body |
-| `UNAUTHORIZED` | 401 | Missing or wrong bearer token |
-| `NOT_FOUND` | 404 | Unknown SKU, sample, or route |
-| `IMAGE_EXPIRED` | 404 | `image_id` left the still-image store (15 min / 16 images) |
-| `PAYLOAD_TOO_LARGE` | 413 | Upload > 20 MB, live frame > 4 MB |
-| `UNSUPPORTED_MEDIA_TYPE` | 415 | Body is not an image content type |
-| `UNDECODABLE_IMAGE` | 422 | Bytes are not a readable image |
-| `VALIDATION_ERROR` | 422 | Body failed validation |
-| `INTERNAL` | 500 | Server fault (details only in the server log) |
-
-## REST
-
-### `GET /v1/health` (no auth)
-
-```json
-{"status": "ok",
- "model": {"version": "best.pt@ultralytics-8.4.153", "device": "cpu", "imgsz": 640, "warm": true},
- "catalog": {"version": "408a658680ed", "sku_count": 21},
- "sessions_active": 0, "uptime_s": 21.1}
-```
-
-### `GET /v1/catalog`
-
-Products with stock and an `image_url` for the template photo. Local file paths are
-never exposed.
-
-### `GET /v1/catalog/{sku_id}/image`
-
-Template image bytes. The path is resolved through the catalog map only, so a crafted
-`sku_id` cannot reach the filesystem.
-
-### `POST /v1/catalog/{sku_id}/stock`
-
-```json
-{"delta": -1}
-```
-
-`-100..100`, non-zero. Stock is in memory and resets on restart, as before.
-
-### `GET /v1/samples` and `GET /v1/samples/{sample_id}`
-
-The demo shelf images (`image.jpg`, `add/*`, the training mosaic) by id, from an
-allowlist built at startup.
-
-### `POST /v1/recognize?classify=true&method=hybrid&conf=0.25&empty_slots=true`
-
-Body: raw image bytes (`Content-Type: image/jpeg|image/png|image/webp`) or multipart
-with a `file` field.
-
-```json
-{"image_id": "img_11d072958c35bc35", "width": 2048, "height": 1365,
- "catalog_version": "408a658680ed", "model_version": "best.pt@ultralytics-8.4.153",
- "coordinate_convention": "pixel-xyxy-origin-top-left",
- "detections": [{"id": 0, "box": [1435, 383, 1493, 520], "confidence": 0.8258,
-                 "identity": {"state": "recognized", "sku_id": "sku_005", "identity_level": "accepted",
-                              "confidence": 0.8948, "score": 47.71,
-                              "candidates": [{"sku_id": "sku_005", "score": 47.71}],
-                              "attempts": 1, "method": "hybrid"}}],
- "empty_slots": [{"id": "empty_1", "box": [812, 404, 909, 549], "width": 97, "height": 145,
-                  "missing_items_est": 2, "center": [860, 476], "row_index": 3}],
- "summary": {"in_view": 170, "recognized": 105, "needs_confirmation": 64, "unrecognized": 1,
-             "detected": 0, "empty_slots": 4, "by_sku": [{"sku_id": "sku_003", "facings": 14}]},
- "timing_ms": {"detect": 24.77, "classify": 1498.88, "total": 1523.65}}
-```
-
-`classify=false` returns `detected` identities immediately so a client can paint boxes
-before identities arrive. The `image_id` stays valid for 15 minutes for
-`classify`/`inspect`.
-
-### Price tags (OCR)
-
-When an OCR backend is available (macOS Vision; see the design doc), a still can
-carry tag evidence. `?tags=true` runs the tag pass synchronously (measured ~0.9 s
-for a six-band shelf) and adds, per detection:
-
-```json
-"price_tag": {"id": "tag_3f2a", "box": [91, 657, 250, 744], "state": "readable",
-              "price": 357, "regular_price": null, "discount": 0.24, "unit_price": 290.52,
-              "name": "ゼロノンコレステロール", "flags": ["sale", "discounted"],
-              "confidence": 0.82, "observations": 2, "settled": false},
-"reconciliation": {"state": "confirmed | mismatch | unverified | no_tag | tag_only",
-                   "signals": [{"kind": "price", "agrees": false, "item": 168, "tag": 269}],
-                   "message": "tag says 269, catalog says 168"}
-```
-
-`price_tag.state` is `readable` (text parsed), `unreadable` (a tag was found but
-its text was not read) or `absent` (no tag under this facing). The summary gains
-`price_tags` counts and `compliance`:
-
-```json
-"price_tags": {"readable": 8, "unreadable": 18, "absent": 85},
-"compliance": {"violations": 7, "by_rule": {"price_mismatch": 7},
-               "rows": [{"row_index": 2, "rule": "price_mismatch", "box": [...], "missing": 1}],
-               "hints": [{"rule": "unreadable_tags", "detail": "tags detected for 5 of 16 facings in this row"}]}
-```
-
-**Compliance fails closed on evidence quality.** A row is only judged when at
-least 70% of its facings carry tag evidence (readable or unreadable); otherwise it
-produces a hint such as `move closer: the price tags in this row could not be
-read`, never a violation. Findings must also persist for 3 of the last 5 frames
-before they publish. This is deliberate: the first implementation reported 106
-missing-tag violations on a shelf where only 8 tags were readable, which was our
-detection being incomplete, not the shelf.
-
-`POST /v1/images/{image_id}/classify` re-reconciles against the cached tag bundle
-and returns `reconciliation` (and `price_tag`) on each identity row, because in
-photo mode the tags are read before the identities exist.
-
-### `POST /v1/images/{image_id}/classify`
-
-```json
-{"method": "hybrid", "detection_ids": [0, 3]}
-```
-
-`detection_ids` optional. Returns identities for the whole image (or the selected
-detections) plus a summary. A method change re-runs it.
-
-### `POST /v1/images/{image_id}/inspect`
-
-```json
-{"box": [1435, 383, 1493, 520], "method": "hybrid"}
-```
-
-Returns the full decision (`identity`, per-method inlier `sub_details`, `product`), the
-cropped product as a data URL, and a feature-match visualization. When the result is
-not accepted, the visualization is drawn against the top candidate and
-`is_candidate` is `true`.
-
-## WebSocket `/v1/stream`
-
-Live frames in, tracked products out. One session per connection.
-
-**On connect**
-
-```json
-{"type": "hello", "protocol": 1, "session_id": "...", "catalog_version": "...",
- "model": {"version": "...", "device": "cpu", "imgsz": 640},
- "config": {"method": "hybrid", "conf": 0.1, "empty_slots": true},
- "limits": {"max_frame_bytes": 4194304, "max_crop_bytes": 1048576}}
-```
-
-**Frames (binary):** `4-byte big-endian frame id` + JPEG bytes. The id is echoed back
-so the client can pair a result with the frame it sent.
-
-**Control (text JSON)**
-
-| Client sends | Server replies |
-| --- | --- |
-| `{"type":"config","method":"orb","conf":0.15,"empty_slots":false}` | `{"type":"config_ack","config":{...}}` |
-| `{"type":"inspect","track_id":12,"method":"hybrid","request_id":"r1"}` | `{"type":"inspect_result","request_id":"r1",...}` or an error |
-| `{"type":"capabilities","crop":true,"max_crop_bytes":131072}` | `{"type":"capabilities_ack","capabilities":{"crop":true,"max_crop_bytes":131072}}` |
-| `{"type":"crop","request_id":"c1","track_id":12,"frame_id":41,"jpeg":"<base64 JPEG>"}` | `{"type":"crop_ack","request_id":"c1","track_id":12,"frame_id":41}` or `crop_error` |
-| `{"type":"reset"}` | `{"type":"reset_ack"}` |
-| `{"type":"ping","t":1}` | `{"type":"pong","t":1}` |
-
-**Per processed frame**
-
-```json
-{"type": "result", "frame_id": 41, "seq": 41, "width": 1280, "height": 720,
- "tracks": [{"track_id": 12, "box": [812, 404, 909, 549], "raw_box": [...], "confidence": 0.83,
-             "first_seen_seq": 30, "relabels": 0, "restarts": 0, "inherited": false,
-             "identity": {"state": "recognized", "sku_id": "sku_005", "confidence": 0.89,
-                          "score": 47.71, "candidates": [...], "attempts": 1, "method": "hybrid"},
-             "product": {"sku_id": "sku_005", "product_name": "...", "price": 179.0}}],
- "empty_slots": [...], "quality": {"sharpness": 812.4, "blur": false, "glare": 0.004,
-                                   "too_far": false, "hint": null},
- "summary": {"in_view": 61, "recognized": 8, "identifying": 0, "needs_confirmation": 1,
-             "unrecognized": 0, "detected": 22, "unconfirmed": 30, "empty_slots": 0,
-             "by_sku": [{"sku_id": "sku_005", "facings": 3}]},
- "timing_ms": {"decode": 3.1, "detect": 21.4, "track": 0.8, "total": 25.3},
- "stats": {"frames_received": 264, "frames_processed": 264, "frames_dropped": 0,
-           "server_fps": 13.2, "classify_pending": 3, "classify_failures": 0,
-           "classify_ms_p50": 88.9}}
-```
-The optional crop lane is additive to protocol `1`. Without `capabilities`, the
-server sends no `crop_request` and existing clients keep their frame/control flow.
-After opting in, a `result` may include `"crop_request":{"request_id":"c1",
-"track_id":12,"frame_id":41,"box":[812,404,850,460]}` on an eligible
-track. The box uses the **sent frame's** pixel coordinates (the raw detection),
-not the camera's native resolution. Keep that exact frame's full-resolution
-capture briefly, map its box into the capture, encode the crop as JPEG, and reply
-in a text JSON `crop` message. Binary frames remain `4-byte id + JPEG`; the crop
-does not take a frame id of its own. `crop_ack` means accepted for asynchronous
-classification, not recognized; subsequent results carry the updated identity and
-`identity.source` (`frame`, `crop`, or `inherited`). `stats.crops` has `requested`,
-`accepted`, and `rejected` counts.
-`stats.classify_failures` counts classifications that raised. A non-zero value
-means labels are missing for an operational reason, not because nothing matched.
-
-The hello limit is 1 MiB of **decoded crop bytes**. The `max_crop_bytes` in
-`capabilities` states the client's limit and must be an integer in 1..1048576; a
-statement that is not one is refused with `BAD_MESSAGE` and the lane stays where
-it was, so both sides keep one limit (128 KiB when the key is `null`). Use the
-acknowledged limit, not only hello's ceiling. At most four requests are outstanding
-per session, and each track object is asked about at most `max_attempts` times
-(the same budget the frame path spends, 2 by default); the budget refills when the
-method changes or when a settled label drifts off the object. A request expires
-after more than six processed frames; clients keep a bounded recent-frame buffer
-and may miss a crop if its capture has already been evicted. A late response
-cannot label a recycled track. Changing method, resetting, or dropping a drifted
-label invalidates that object's outstanding requests.
-
-Crop failures are recoverable text messages with `type: "crop_error"`, `code`,
-and `message`. Pre-decode failures also echo `request_id`; session-level refusals
-do not currently echo it. Codes: `CROP_BAD_MESSAGE` (missing/invalid base64 JPEG
-or missing IDs), `CROP_TOO_LARGE` (empty or oversized decoded crop, excessive
-encoded length, or a JPEG whose header claims more than 4 megapixels),
-`CROP_UNKNOWN_REQUEST` (no outstanding request, including one already expired),
-`CROP_MISMATCH` (track/frame differs), `CROP_STALE` (late or
-invalidated track/session), `CROP_UNDECODABLE` (unreadable image), and
-`CROP_UNAVAILABLE` (classifier shutting down). A request is single-use, including
-when its response is rejected; continue streaming frames rather than retrying it.
-The server may ask again for the same object while its budget lasts.
-
-**Backpressure:** the server keeps one pending frame per session and processes the
-newest, counting the ones it replaced in `frames_dropped`. A client should keep one
-frame in flight (`stream_client.py --pace result` does exactly that); sending faster
-just drops frames and breaks the pan's continuity for the tracker.
-
-**Recoverable errors** (the socket stays open): `BAD_FRAME`, `UNDECODABLE_IMAGE`,
-`PAYLOAD_TOO_LARGE`, `BAD_MESSAGE`, `TRACK_NOT_IN_VIEW`, `INTERNAL`.
-
-**Closes:** `1008` wrong/missing token, `1013` session limit reached, `120 s` idle.
-
-## What the client can rely on
-
-- **A label is only shown for an accepted match.** Weak or ambiguous results are
-  `needs_confirmation` with candidates; unknown products are `unrecognized`.
-- **A label never changes silently.** A settled label is dropped only when the box has
-  drifted off the product for two consecutive frames, or when the track id was
-  restarted. Both increment `relabels`/`restarts`, so a client can audit every change.
-- **One product is classified once.** While the camera pans, a fresh track that covers
-  a recently recognized product inherits that label (`inherited: true`) instead of
-  queueing for classification again.
-- **Detection never waits for classification.** Frames come back at detection speed
-  (~25-45 ms on this machine); labels fill in as the classification pool finishes.
-
-## Design decisions
-
-| Decision | Why | Rejected |
-| --- | --- | --- |
-| Server lives in this repo, entry point `python code/app.py` | It owns the weights, templates, and the team's workflow | A separate service repo (split the model from its data) |
-| WebSocket carrying JPEG frames for live, REST for stills | Browsers and embedded clients both speak it; no media stack | WebRTC (heavy dependency, only wins on lossy WAN), gRPC (browsers), in-browser inference (the matcher is OpenCV Python) |
-| Latest-frame-wins with one pending slot | Lowest latency and self-throttling on any network | Queueing every frame (adds latency, never catches up) |
-| Tracking-by-detection (ultralytics ByteTrack) wrapped for per-session ids | Kalman prediction plus low-score second association is proven; ids must not be shared | No tracking (re-classify everything, far too slow), a hand-rolled tracker (no motion model) |
-| Classify once per track, aggregate up to 3 attempts, lock on accept | Keeps detection real-time and labels stable | Classifying every box every frame (measured as impossible: ~44 ms per crop x 50-170 boxes) |
-| Label inheritance by place | The same product gets several ids while panning; re-classifying it is waste | Re-classify on every id (measured 9/66 recognized vs 17-21/66 with inheritance) |
-| `hybrid` as the live default | Most accurate (21/21 SKUs on `image.jpg`) | `hybrid_fast` (ORB shortlist): 3.4x faster but only 47% agreement with `hybrid` on 553 crops, so it is not the default |
-| CPU as the default device | Measured: 26-29 ms p50 vs 112 ms on MPS, with byte-identical boxes | MPS (slower here), CUDA (no NVIDIA GPU on this machine) |
-| Identity states instead of raw scores | "Never turn model uncertainty into business certainty" (project master) | Showing a best-guess SKU with a confidence number |
-| Token via env var, access log off | Keeps credentials out of shell history and logs | A `--token` flag, default uvicorn access log (prints the query string) |
-
-## Measured performance
-
-Apple M5, 10 cores, 24 GB, CPU device, 2026-09-24 (`code/bench_engine.py`,
-`code/stream_client.py --pace result`):
-
-| Measurement | Value |
-| --- | --- |
-| Model load / first predict | 50 ms / 985 ms (startup warm-up covers this) |
-| Detection, 1280x853 frame | p50 28.7 ms, p95 31.0 ms (169 boxes) |
-| Detection, native 2048x1365 | p50 28.7 ms, p95 87.6 ms (170 boxes) |
-| Detection during a live pan | p50 46-48 ms (classification pool running) |
-| Classification per crop (hybrid) | p50 44 ms, p95 95 ms |
-| Classification throughput | 92 crops/s at 8 threads |
-| Still recognition, whole shelf | 170 detections, 105 recognized, ~2.0 s total |
-| Live results, one client | 10.5-12.2 per second, round-trip p50 58-78 ms |
-| Live results, two clients | 8.5 per second each, detection p50 75 ms |
-| Time to first label on a pan | ~1.0-2.6 s (includes waiting for the product to enter) |
-
-### Live accepted share after the cosine-gate fix (2026-09-28)
-
-The live path used to re-gate the gallery's own scores at `COSINE_ACCEPT` (0.60)
-and `COSINE_AMBIGUOUS` (0.50), lines above the fine-tuned head's own 0.40, so
-matches the gallery had already accepted were refused again. Measured on the
-same machine, store `stock-lopia`, `code/stream_client.py --source add/image6.jpg
---pan 12 --seconds 20 --pace result`:
-
-| Gating | Tracks labelled | Final recognized / in view |
-| --- | --- | --- |
-| Pre-fix 0.60 / 0.50 | 16 of 261 | 2 of 30 |
-| Gallery's own line | 83 of 255 | 6 of 31 |
-
-5.2x the labelled tracks and 3x the settled facings on an identical walk. The
-remaining `unconfirmed` tracks are products the gallery has no view for, not
-refusals: `stock-lopia` holds 12 SKUs while the frame shows a full bay.
-
-The live numbers use the shipped defaults: `--classify-workers 5` and torch capped at
-4 threads (`DORAYAKI_TORCH_THREADS=4`). Uncapped torch and 8 workers measured slower
-(11.1 results/s single-client) because detection and classification fight over the
-CPU. Detection is the part that must stay real-time; classification fills in behind it.
-
-The slide's "約28~30ms per image" matches the measured **detection** time (28.7 ms);
-the full detect-and-classify of a whole 170-product shelf is ~2 s because each product
-is matched against the catalog separately.
-
-## Price-tag OCR: what it can and cannot do (measured)
-
-- **Capture distance decides everything.** At `add/image6.jpg`'s ~1 m (tags ~300x80 px,
-  digits ~22 px) macOS Vision reads a tag's printed fields reliably and the tag pass
-  finds 8 readable tags with names and prices in ~0.9 s. At `image.jpg`'s ~2 m (digits
-  ~14 px) it reads none of them - the pass correctly reports nothing readable.
-- **Tag detection is the weak link, not the OCR.** On a shelf with ~100 facings the
-  pass currently finds tags for a minority of them at this distance; the compliance
-  hints say so instead of inventing violations.
-- **A single read can misread a digit, and a consistent misread survives the vote.**
-  The vote needs independent reads (live mode accumulates them across frames; stills
-  read each band twice at different upscales) and a dead heat stays unread rather than
-  guessing, but when one wrong reading wins most frames it settles as wrong - measured
-  once in a 40-frame live run (`3609` for a `360` tag, 10 observations against 8). The
-  safeguard is downstream: a wrong tag price shows as a `mismatch` with both values and
-  is never applied to the product identity. Cross-checking the sale price against the
-  regular price and discount is the recorded next improvement.
-- **The demo catalog is fictional**, so tag prices legitimately disagree with it. That
-  is shown as `mismatch` with both values; it is not an error.
-
-## Known limitations
-
-- **No session totals.** Facings are counted only for products currently in view. A
-  product that leaves and re-enters gets a new track id, so cumulative counts would
-  double count.
-- **Identity thresholds are uncalibrated** closed-set defaults (`identity.py`), not
-  measured accuracy.
-- **`surf` is SIFT** in this OpenCV build (no nonfree SURF); the label stays for
-  continuity with the paper.
-- **Fast pans classify less.** A very fast pan creates more new tracks than the
-  classification pool can settle; a slower, natural scan labels nearly everything.
-- **Coverage guidance is partial**: glare/blur/too-far hints exist; "which part of the
-  fixture was scanned" does not (that needs mosaicking).
-- **Stock is in memory** and resets when the server restarts.
-- **`hybrid_fast` is experimental** and off by default.
-
-## Mobile client
-
-The protocol is capture-source agnostic. The dorayaki app ships a native client
-against it: `shared/.../data/shelf/LiveScanClient.kt` holds the Ktor WebSocket,
-`platform/CameraFrames` supplies AVFoundation/CameraX frames, and ADR-002 in the
-app repo governs the client side. What the client needs from this server:
-
-- WebSocket client (Ktor `WebSockets` plugin), one frame in flight.
-- Capture JPEG frames from `AVCaptureVideoDataOutput` (iOS) or CameraX
-  `ImageAnalysis` (Android), sent upright; map server boxes into the view with the
-  same object-fit transform the web client uses.
-- iOS ATS: allow local networking (`NSAllowsLocalNetworking`) or use HTTPS; Android
-  needs cleartext permission for plain `http://`.
-- The crop lane is negotiated, not assumed: announce
-  `{"type":"capabilities","crop":true,"max_crop_bytes":N}` after `hello`, wait for
-  `capabilities_ack`, then answer `track.crop_request` with the exact
-  `request_id`/`track_id`/`frame_id` and the crop's JPEG as base64. Silence is a
-  valid answer for a frame the camera no longer holds; the request expires after
-  `crop_ttl_frames`.
-
-## Tests
-
-```bash
-cd recognization
-YOLO_OFFLINE=1 YOLO_AUTOINSTALL=false python -m unittest discover -s code -p 'test_*.py' -v
-```
-
-`test_real_model` is discovered and skipped unless `DORAYAKI_REAL_MODEL=1`.
-CI installs `requirements.lock` and runs the same command.
diff --git a/recognization/docs/DATA.md b/recognization/docs/DATA.md
deleted file mode 100644
index 56c8260..0000000
--- a/recognization/docs/DATA.md
+++ /dev/null
@@ -1,117 +0,0 @@
-# Capturing real data
-
-Everything measured in this project so far comes from one or two photographs. That
-is its largest weakness: the identity benchmark takes its gallery and its queries
-from the same photo, so every accuracy number is an in-distribution number.
-
-This is the procedure for replacing that with data from a real store, recorded on
-the device that will do the scanning.
-
-## 1. Record a walk
-
-From `recognization`, run `./run-demo-server.sh stock-lopia` once to provision
-the ignored `config/local.env` token and the native apps' local LAN settings,
-then stop that server. Rebuild the app after provisioning. The token is a secret:
-keep `.demo-token`, `config/local.env`, and app-local settings out of commits and
-shared logs. For a recorded walk, start the same gallery backend on the
-launcher's port; `code/app.py` reads the token from `config/local.env`:
-
-```bash
-cd product-recognition-demo/recognization  # from the app repository root
-YOLO_OFFLINE=1 .venv/bin/python code/app.py \
-  --port 8099 --host 0.0.0.0 --stock-dir stock-lopia --identity gallery \
-  --live-conf 0.15 --classify-workers 8 \
-  --record-frames ~/dorayaki-data/storeA-visit1 \
-  --record-every 3
-```
-
-This requires the local pinned DINOv2 file described in `../README.md`
-(`weights/dinov2_vits14_lvd142m.safetensors`, or
-`DORAYAKI_DINOV2_WEIGHTS`). A LAN bind without a token is refused. Record the
-second visit to a separate directory with the same catalog and model.
-
-Then run the app and scan. Each scan session becomes its own directory:
-
-```
-~/dorayaki-data/storeA-visit1/<session>/
-    000000.jpg        the frame, exactly as the client sent it
-    000001.jpg
-    index.jsonl       one line per recorded frame:
-                      {"frame": 123, "path": "000001.jpg", "at": 1699...,
-                       "identity_backend": "gallery",
-                       "summary": {...},
-                       "tracks": [{"box": [...], "identity": {...},
-                                   "tag_identity": {...}, "tag": {...}}]}
-```
-
-Frames are sampled, not continuous, and the index keeps each frame's own number so
-the sampling is visible rather than silent. `/v1/health` reports how many frames
-have been written.
-
-## 2. What to capture, and why
-
-Two visits to the same shelf are worth more than one long visit, because the
-benchmark needs a **cross-visit split**: the gallery comes from visit 1 and the
-test set from visit 2. That is the difference between "it works on this photo" and
-"it works on this shelf".
-
-Per shelf, capture:
-
-- a **slow walk along each rail** at working distance (about 1 m) — the live loop's
-  real input;
-- a **close pass** (about 30 cm) over a few tags — the only distance where the
-  barcode is decodable (measured: 0.63 px per module at 1 m needs ~3× more pixels);
-- the same shelf again **on a second visit**, ideally with something changed: a gap,
-  a moved facing, a new product.
-
-## 3. Turn a recording into labels
-
-The tag channel seeds the labels. A facing whose tag named a product, with a price
-the tag's own arithmetic corroborates, is a candidate label rather than a guess:
-
-```bash
-python -c '
-import json, sys
-sys.path.insert(0, "code")
-from record import labelling_candidates
-seeds = labelling_candidates(sys.argv[1])
-print(len(seeds), "candidate labels")
-print(json.dumps(seeds[:3], ensure_ascii=False, indent=1))
-' "$HOME/dorayaki-data/storeA-visit1/REPLACE_WITH_SESSION/index.jsonl"
-```
-
-A human then confirms or corrects them in the app's confirmation screen — the same
-screen that closes the flywheel. Nothing here is a training set on its own; it is
-the raw material for one.
-
-## 4. Replay a cross-visit benchmark
-
-Enroll the gallery from visit 1 only, then freeze that catalog. Confirm labels
-for visit 2 independently; `labels.json` beside its `index.jsonl` (or supplied
-with `--labels`) is a JSON list, or `{"labels": [...]}`. Each entry has `frame`
-(the number in `index.jsonl`), `box` (pixel coordinates `[x1, y1, x2, y2]`),
-and `sku_id` (a catalog SKU). These are confirmed annotations, not the server's
-predictions. For the second visit, run from `recognization` with the same frozen
-`stock-lopia` catalog:
-
-```bash
-SESSION="$HOME/dorayaki-data/storeA-visit2/REPLACE_WITH_SESSION"
-YOLO_OFFLINE=1 .venv/bin/python code/eval_live.py "$SESSION" \
-  --stock-dir stock-lopia --labels "$SESSION/labels.json" \
-  --replay --device cpu
-```
-
-A recording written by the current server names `identity_backend` on each
-index line, and the evaluator uses that. `--identity gallery` (or `matcher`)
-is required when the index omits it, including recordings from before that
-field existed, and must match the recorded value when both are present. It
-does not silently score the matcher.
-
-Replace `REPLACE_WITH_SESSION` with the actual session name. The evaluator replays
-recorded JPEGs through `LiveSession`, writes `eval_live.json` in that directory,
-and reports backend, loaded head, weight/catalog fingerprints, decision counts,
-and held-out labelled top-1 when valid labels exist. It rejects missing or
-unpinned DINOv2 weights rather than substituting matcher scores; labels copied
-from catalog templates are excluded as leakage. Without confirmed held-out
-visit-2 labels, counts are diagnostic only, **not gallery walk accuracy**. No
-held-out cross-visit result is available here yet.
diff --git a/recognization/docs/EXTERNAL-EVAL.md b/recognization/docs/EXTERNAL-EVAL.md
deleted file mode 100644
index 4bb0d9a..0000000
--- a/recognization/docs/EXTERNAL-EVAL.md
+++ /dev/null
@@ -1,285 +0,0 @@
-# Evaluation on real store photos from Wikimedia Commons
-
-Everything else in this repository is measured on two photographs. This evaluates the
-pipeline on **54 real store photos it has never seen**, collected from Wikimedia
-Commons with their licences (CC0 / CC BY / CC BY-SA / public domain) recorded in a
-manifest.
-
-Reproduce:
-
-```bash
-python code/collect_shelf_images.py ~/dorayaki-data/shelf-images   # needs network
-python code/eval_external_images.py ~/dorayaki-data/shelf-images
-```
-
-## 1. Detection transfers, and well
-
-| | |
-| --- | --- |
-| Images with at least one detection | **46 of 54** |
-| Total detections | **2,258** |
-| Book scans, illustrations, a photo of a shop front | 0 detections (correct) |
-
-The detector is the teammate's single-class YOLO trained on one shelf, and it found
-individual cartons, bottles and packets on dairy cases, soy-sauce shelves, spice
-racks, a confectionery aisle, an Okinawan supermarket and a Hong Kong grocery - with
-tight boxes. Detection is not the weak link.
-
-## 2. Identity transfers when the product is genuinely present
-
-The original 21-SKU catalog contains 明治 おいしい牛乳. On
-`File:2011年4月の牛乳 (5588310773).jpg` - a milk case photographed in 2011, entirely
-unseen - the pipeline found **6 facings of 明治 おいしい牛乳 and labelled them
-correctly**, and on `File:Kawasaki-Inryo-1.jpg` it found 野菜生活100 and
-プロビオヨーグルト R-1. So the matcher does transfer to new photographs of products it
-has templates for.
-
-## 3. Precision is the failure, and rejection is missing entirely
-
-The same run labelled these as 明治 おいしい牛乳, in the same photograph:
-
-* 低脂肪殺菌牛乳 (a タカナシ carton, top left)
-* 有機豆乳 (top left corner)
-* 特濃4.5 (three cartons, top middle)
-* 無調整豆乳 (top right)
-* 北海道牛乳 (several, bottom right)
-
-They are all white milk cartons. The matcher has no way to say "unknown", so every
-look-alike becomes a claim.
-
-Measured over 24 foreign shelves whose products are *not* in the catalog: **451
-facings accepted as recognised products**, with the score distribution overlapping
-the real one and its extremes landing on the wrong images:
-
-| | accepted | median score | p10 | p90 |
-| --- | --- | --- | --- | --- |
-| The shelf the catalog describes (`image6`) | 71 | 18.2 | 5.2 | 372.4 |
-| Foreign shelves (24 images) | 451 | 6.0 | — | — |
-| A bento (「198円 中華弁当」) | 1 | 50.5 | | |
-| A supermarket interior | 1 | 80.6 | | |
-| Deer cookies at Nara | 6 | 33.1 | | 189.9 |
-
-**No threshold on this score separates the two**: it would have to sit above 5 (to
-catch the foreign p10) and below 6 (to keep the real median), and the worst offenders
-score higher than any correct match on the demo shelf.
-
-## 4. What this means for the plan
-
-It confirms, on real data, the two conclusions the literature and the review pointed
-at - and it makes them concrete:
-
-1. **The matcher's raw inlier score is not a confidence.** It cannot be used for
-   rejection or for ranking, and it is the reason packaging recognition cannot name
-   products on its own.
-2. **Fine-grained look-alikes are the failure mode**: milk cartons, soy-milk cartons,
-   and the twenty near-identical mayonnaise packs from the earlier benchmark. The
-   GroceryVision study of 190 open VLMs found the same shape (94.5 % Recall@5, a
-   17.5-point drop at Recall@1): global embeddings cluster categories well and rank
-   visually similar SKUs badly.
-
-So step 4 of the plan is not a refinement, it is the fix: a **shortlist followed by
-local re-ranking** (PRISM's shape: SigLIP shortlist, then LightGlue verification) and a
-**calibrated rejection** on an embedding distance where same-product and
-different-product are actually separable (measured earlier: above 0.6 and below 0.5
-respectively, unlike this score).
-
-## Attribution
-
-The images are from Wikimedia Commons under their individual licences (CC0, CC BY,
-CC BY-SA, public domain); `manifest.json` records the author, licence and source URL
-of each one. They are evaluation material and are not redistributed here.
-
-
----
-
-# Part 2: the identity stack, measured on a shelf where the product IS present
-
-Part 1 measured shelves whose products are absent. This part uses
-`File:2011年4月の牛乳 (5588310773).jpg` - a milk case where **14 of 107 facings are
-明治 おいしい牛乳**, a product the original catalog holds - so precision and recall can
-be computed instead of just false accepts. The 107 crops were labelled by eye from a
-contact sheet.
-
-## The shipped matcher
-
-| | claims | correct | precision | recall |
-| --- | --- | --- | --- | --- |
-| matcher | 31 | 12 | **39 %** | **86 %** |
-
-It finds almost every true facing and makes 19 false claims, on cartons of 特濃4.5,
-低脂肪殺菌牛乳, 明治ラブ and 北海道牛乳. Correct where the product is, wrong on the
-look-alikes: the same shape as Part 1.
-
-## The embedding gallery: rejection, but not precision
-
-| threshold | false accepts on foreign shelves | milk shelf: precision | recall |
-| --- | --- | --- | --- |
-| 0.45 | 29 % | 28 % | 79 % |
-| 0.50 | 18 % | 29 % | 64 % |
-| 0.55 | 11 % | 35 % | 50 % |
-| **0.60** | **5 %** | 45 % | 36 % |
-| 0.65 | 2 % | 0 % | 0 % |
-
-The gallery's value is the **controllable rejection**: on shelves where nothing
-matches, false accepts fall from the matcher's 47.8 % to **5 %** at 0.60, and the
-curve is smooth enough to pick an operating point. But on the shelf where the product
-*is* present its precision (28-45 %) is no better than the matcher's 39 % - it buys
-honesty about the unknown, not finer discrimination.
-
-## Enrolling a shelf from itself does not fix it
-
-The flywheel's first step is to enroll a store's own shelf. Measured by taking 1-3
-views per product from the labelled milk shelf and testing on the rest:
-
-| views per SKU | top-1 | wrong | rejected |
-| --- | --- | --- | --- |
-| 1 | **26 %** | 30 | 29 |
-| 2 | 25 % | 27 | 27 |
-| 3 | 23 % | 24 | 26 |
-
-Flat at ~25 %, and far below the 72-76 % measured on the demo shelf with the same
-number of views. The demo shelf's facings are near-identical crops of the same
-photograph; real milk cartons differ by small text and a colour band. **Off-the-shelf
-DINOv2 is not discriminative enough for fine-grained SKUs**, which is what the
-literature says (RP2K and RPC both fine-tune; GroceryVision reports a 17.5-point drop
-from Recall@5 to Recall@1). Fine-tuning is not a refinement of the plan, it is a
-requirement - and its labels are what the tag channel and the confirmation loop
-produce.
-
-## The packaging's own text is a real channel, and resolution-bound
-
-The cartons differ by the text printed on them, so OCR was tested as an identity
-channel - first single-scale, then with the tag reader's trick (two upscales, the red
-channel, pooled):
-
-| product | single scale | multi-scale + red |
-| --- | --- | --- |
-| MEGMILK (large Latin text) | 85 % | **85 %** |
-| 北海道牛乳 (large kanji) | 65 % | **65 %** |
-| 明治ラブ | 0 % | **75 %** |
-| 特濃4.5 | 0 % | **50 %** |
-| 明治 おいしい牛乳 (small vertical text) | 7 % | **28 %** |
-| 低脂肪殺菌牛乳 / 無調整豆乳 / 酪農3.6 | 0 % | 0 % |
-| **overall** | 51 % | **60 %** |
-
-Text works where it is large and fails where it is small - the same resolution wall as
-the price tags. This is a capture-spec question, not a model question.
-
-## The identity architecture this implies
-
-| channel | what it is good for | measured |
-| --- | --- | --- |
-| Detection | finding facings on unseen shelves | 46/54 images, tight boxes |
-| Tag (corroborated) | exact identity and price, where readable | 15/15 fields on the demo shelf; sparse in the field |
-| Packaging text | brand-level discrimination | 60 % recall, higher when text is large |
-| Embedding gallery | **rejection** - saying "unknown" | 5 % false accepts at 0.60 |
-| Fine-tuned embedding | fine-grained SKUs (milk cartons) | not built; off-the-shelf is ~25 % |
-| Confirmation loop | everything the above cannot settle | not built |
-
-So the honest design is: **reject by default**, name the product from the tag when it
-is readable, from the packaging text when it is legible, from a fine-tuned embedding
-otherwise - and hand the remainder to the operator, whose answers become the next
-enrollment.
-
-
----
-
-# Part 3: fine-tuning the embedding, and what it buys
-
-Part 2 ended on the number that matters: enrolling a real shelf from itself leaves
-off-the-shelf DINOv2 at ~25 % top-1, because real milk cartons differ by small text
-and a colour band. The literature's answer is fine-tuning, so this measures it.
-
-`code/finetune_identity.py` trains a small projection head (384 -> 128, cosine
-classifier, angular margin) on frozen DINOv2 features, with augmentation standing in
-for the second visit we do not have. Two enrolment views per product, twelve
-augmented copies each, 72 held-out facings, 8 products. Balanced accuracy is reported
-because the shelf is unbalanced (MEGMILK has 34 facings, 酪農3.6 has two - a system
-that always answered "MEGMILK" would look respectable on the raw rate).
-
-| | top-1 | balanced | answered | of those right |
-| --- | --- | --- | --- | --- |
-| raw DINOv2 cosine | 2/72 (2 %) | 1 % | 72 | 3 % |
-| raw, rejecting below 0.55 | 2/72 (2 %) | 1 % | 44 | 5 % |
-| **fine-tuned + margin** | 28/72 (38 %) | **73 %** | 72 | 39 % |
-| **fine-tuned, rejecting below 0.4** | 17/72 (23 %) | 58 % | 18 | **94 %** |
-
-Three things follow.
-
-1. **Fine-tuning is not a refinement, it is the difference between working and not.**
-   Balanced accuracy goes from **1 % to 73 %** on the same held-out facings. Off-the-
-   shelf features cannot separate these products; a head trained on sixteen crops can.
-2. **With a rejection threshold it becomes precise**: answering only 18 of 72 facings
-   and being right on 94 % of them is exactly the operating point a store wants -
-   name what you are sure of, and hand the rest to the operator. The raw model cannot
-   do this at any threshold (5 % of answered).
-3. **Where it fails is where the labels are coarse.** Per class, with rejection:
-   明治ラブ 2/2, おいしい無調整豆乳 2/2, 低脂肪 1/1, 特濃4.5 1/2, 北海道牛乳 8/21,
-   明治 おいしい牛乳 2/12, and **MEGMILK 1/32**. MEGMILK is the class I labelled most
-   coarsely - the contact sheet shows what are probably several different products
-   under one brand - so the class is internally inconsistent and the model correctly
-   refuses most of it. Better labels, not a better model, is what that needs.
-
-## The honest limit
-
-Enrolment and held-out facings come from **one photograph**. Augmentation stands in
-for a second visit, which is not the same thing: a real second visit changes the
-light, the angle and the stock. So 73 % balanced and 94 % precision are **upper
-bounds**, and the only way to know the real number is to record a second visit
-(`docs/DATA.md`).
-
-What is settled is the mechanism: **a head trained on labels the pipeline can produce
-itself** - tag-named facings, plus operator confirmations - moves fine-grained
-identity from useless to usable, and a rejection threshold turns it into something a
-store can act on.
-
-
----
-
-# Part 4: making it usable, and the trap that came with it
-
-The head is now part of a store: `python code/finetune_identity.py --stock stock-lopia`
-trains on that store's enrolled views and writes `<stock>/identity_head.pt`, which
-`ProductGallery` loads automatically. The first attempt exposed a trap worth writing
-down.
-
-## A trained head destroys rejection
-
-Measured on the same two sets (the home shelf it was enrolled from, and 20 shelves
-whose products are absent):
-
-| | home shelf accepted | foreign shelves accepted |
-| --- | --- | --- |
-| raw features, threshold 0.55 | 78 % | **5 %** |
-| trained head, threshold 0.40 | 58 % | **44 %** |
-
-A discriminative head has no "none of the above": trained with an angular margin to
-separate seven products, it maps *everything* confidently onto its seven prototypes,
-including cartons it has never seen. Rejection, which was the gallery's whole point,
-disappears.
-
-## The fix is two stages, and it needs no retraining
-
-The two mechanisms fail in opposite directions, so each does what it is good at:
-
-* **raw features decide whether to answer** (they generalise: 5 % false accepts on
-  strangers),
-* **the head decides what to answer** (it discriminates: 73 % balanced on
-  fine-grained products against 1 % raw).
-
-| | home shelf accepted | foreign shelves accepted |
-| --- | --- | --- |
-| **raw gate, then head ranking** | 47 % | **5 %** |
-
-The home-shelf acceptance is lower than raw-only because the gate is strict, which is
-the honest trade: fewer claims, and the claims are worth more.
-
-## Still open
-
-* A joint accuracy number on the LOPIA store needs held-out labels there; its
-  enrolment views came from the facings the tags named, so scoring on those would be
-  scoring on the training set.
-* Training the head *with* out-of-distribution negatives (crops from other stores)
-  should let it keep rejecting on its own, which would remove the need for the gate.
-* Everything above is still one photograph per store. The second visit remains the
-  measurement that settles it.
diff --git a/recognization/docs/design-price-tags-and-compliance.md b/recognization/docs/design-price-tags-and-compliance.md
deleted file mode 100644
index 2027192..0000000
--- a/recognization/docs/design-price-tags-and-compliance.md
+++ /dev/null
@@ -1,581 +0,0 @@
-# Design: price-tag OCR, reconciliation, compliance, and the mobile client
-
-Status: proposal, evidence-backed, not implemented.
-Written 2026-09-24 after measuring OCR engines against the real demo images.
-Read `docs/API.md` first for the system this extends.
-
-The owner's brainstorm, restated as requirements:
-
-1. Detect price tags in the image, transcribe them to text.
-2. Run that **in parallel** with product recognition, so both boxes are shown.
-3. **Reconcile** the recognized item with what the tag says; show both results.
-4. Tap a product (web **and** mobile live scan) and see the product card **plus the
-   OCR information from the tag below it**.
-5. **Price-tag compliance**: detect a group of items missing price tags.
-
-Everything below is designed so that none of it slows down detection, and so that no
-compliance claim is ever made from unreadable evidence.
-
----
-
-## 1. What was measured before designing
-
-All numbers from this machine (Apple M5, 10 cores), 2026-09-24.
-
-### 1.1 Tag readability depends almost entirely on capture distance
-
-| Image | Distance | Tag size | Digit height | macOS Vision on tag | RapidOCR on tag |
-| --- | --- | --- | --- | --- | --- |
-| `image.jpg` (2048 px) | ~2 m | ~110x35 px | ~14 px | **nothing** | not attempted (same scale) |
-| `add/image6.jpg` (1500 px) | ~1 m | ~300x80 px | ~22 px | **reads 5/5 fields** | reads printed text, misses the big red digits |
-
-Vision on a single `image6` tag (`300x80` crop, 191 ms) returned:
-
-```
-0.30  キュービーゼロ ノンコレステロール<310g>     product name
-1.00  357                                     regular price
-0.30  2.4割引                                  discount
-0.30  269                                     sale price (large, stylised)
-1.00  290.52                                  unit price
-```
-
-RapidOCR on the same crop (263 ms) returned `357`, `2.4割引`, `290.52`, `<310g>` at
-0.97-1.00 confidence but **missed `269`** (large red outlined digits).
-
-**Conclusion:** a real Japanese price tag carries a JAN barcode, the product name,
-the sale price, the regular price, the discount, and a unit price - and at the
-demo's `image.jpg` distance none of it is readable by any engine. Tag OCR is a
-capture-quality problem before it is a model problem.
-
-### 1.2 Engine comparison
-
-| Engine | Platform | Rail band 1500x120 | Full 2048 px image | Small printed text | Large stylised digits | Verdict |
-| --- | --- | --- | --- | --- | --- | --- |
-| macOS Vision (accurate) | macOS only | 182 ms, 20 regions | 510 ms, 58 regions | good | **reads them** | best here, not portable |
-| RapidOCR (PP-OCR ONNX) | cross-platform | 752-1290 ms, 23 regions | ~3.4 s at 1280 px, 143 regions | good | misses them | cross-platform default |
-| Tesseract `jpn` | cross-platform | ~300 ms | garbage on tags (`と1拓5 MI HMI`) | unusable | unusable | rejected |
-| PaddleOCR (full) | cross-platform | heavier sibling of RapidOCR | - | - | - | candidate if RapidOCR's accuracy is not enough |
-
-### 1.3 The same OCR pass reads packaging text - but confirmation depends on catalog coherence
-
-Measured by OCR-ing an image and checking, for every accepted detection, whether a text
-region inside its box matches the catalog name (normalized, longest-common-substring
-ratio >= 0.4):
-
-| Image | Accepted | Confirmed by packaging text | Note |
-| --- | --- | --- | --- |
-| the 21 catalog **template** photos | 21 | **14 (67%)** | catalog and photo describe the same product, by construction |
-| `image.jpg` (2 m shelf) | 105 | 11 (10%) | only the products our fictional catalog happens to share with the photo (Tropicana, 野菜生活) |
-| `add/image6.jpg` (1 m shelf) | 59 | 2 (3%) | the shelf is a different store's assortment (キューピー); our catalog does not describe it |
-
-The template row is the honest measure of the *mechanism* (67%, with exact matches at
-`野菜生活100オリジナル`, `野菜生活100ベリーサラダ`, `野菜生活100アップルサラダ`,
-`歌舞伎揚`). The shelf rows measure the *data problem*: our demo catalog is fictional
-and the photos are a real store, so most products have nothing to match against.
-
-Failure modes seen in the 7 template misses, all explainable:
-
-- **Romanized reads** - `亀田のまがりせんべい` came back as `Magari Senbei`; character
-  LCS scores 0.08 while the semantics match. Matching must compare kana/kanji *and*
-  a romaji transliteration of the catalog name.
-- **Brand-only reads** - `三幸製菓`, `岩塚製菓` (the maker) without the product name.
-- **Name not on the visible face** - the 明治 milk carton's name sat outside the
-  detected region.
-
-Consequence for the design: packaging text is a **bonus confirmation**, never a gate.
-Its absence must be neutral (never a contradiction), and its evaluation needs a
-coherent catalog-photo pair - which is the same coherence requirement as price
-reconciliation (§6.1).
-
-So OCR has **two** uses:
-
-- **Tag OCR** - price, name, JAN. Needs capture distance; blocked on demo data.
-- **Packaging-text identity** - a second opinion on what the matcher decided; works
-  today on coherent data (67% on templates), informative but sparse on the demo
-  shelves.
-
-### 1.4 The barcode is the exact-identity path, and it is currently too small
-
-`image6`'s tag carries an EAN-13-style barcode; at this distance (~150x25 px, blurred)
-`cv2.barcode.BarcodeDetector` fails even at 4x upscale. An EAN-13 needs roughly 3 px
-per module (~300 px width) to decode. A phone at 40-60 cm decodes it trivially.
-**Design consequence:** barcode decoding is the strongest reconciliation signal
-available, and it is gated by the same capture requirement as the text.
-
-### 1.5 The catalog has no JAN and no price semantics
-
-`stock.csv` has `SKU_ID, product_name, receipt_date, expiration_date, number_in,
-number_out, number_on, price`. There is no JAN, no regular-vs-sale price, and the demo
-prices (¥168 for Tropicana) belong to a fictional store, not the store in the photo.
-Exact reconciliation needs a catalog change, and an honest demo needs the catalog and
-the tags to describe the same store.
-
----
-
-## 2. Design principles this system inherits
-
-1. **The UI never turns uncertainty into certainty** (project master). A tag read is
-   evidence with a confidence, never a fact.
-2. **Fail closed on evidence quality.** "No tag detected" and "tag detected, text
-   unreadable" are different states. Only the first may become a compliance
-   violation; the second is a capture-quality hint.
-3. **Detection stays real-time.** OCR is the slowest stage (0.2-3.4 s); it must never
-   sit in the frame's critical path.
-4. **One product, one classification, one tag read.** Tags are static; the same
-   physical tag seen in 30 frames is one tag with one vote history, not 30 reads.
-5. **Additive protocol changes only.** The live protocol is version 1; new fields are
-   optional, old clients keep working.
-
----
-
-## 3. Architecture: a second, parallel perception stage
-
-```
-                    ┌───────────────────────── frame loop (must stay ~25-45 ms) ─────┐
- client frames ──▶  decode ──▶ detect ──▶ track ──▶ schedule classification ──▶ result
-                       │                       │                                   ▲
-                       │ (same decoded frame, read-only)                           │
-                       ▼                       ▼                                   │
-                 ┌──────────────────────────────────────────┐                       │
-                 │  TagService (own thread, 1-2 workers)    │  cached tag state ────┘
-                 │                                          │  merged into the result
-                 │  rail bands from row geometry            │
-                 │  → crop → OCR (engine backend)           │
-                 │  → parse → vote over passes              │
-                 │  → associate tag ↔ product               │
-                 └──────────────────────────────────────────┘
-                                  │
-                                  ▼
-                     Reconciliation + compliance rules
-                     (pure functions over settled evidence)
-```
-
-Why a separate service and not another stage in `process_frame`:
-
-- **OCR is 10-100x slower than detection.** 0.75-1.3 s per rail band (RapidOCR) means
-  it cannot run per frame; it runs on a cadence and its answers are cached.
-- **Tags are static.** A tag read stays valid for the whole pass over that shelf.
-- **The frame loop already has a budget.** Detection (46 ms under live load) plus
-  tracking plus classification scheduling fills it.
-
-### 3.1 The tag pass (cadence and caching)
-
-A *tag pass* processes one rail band and updates the tag cache.
-
-| Trigger | When |
-| --- | --- |
-| Periodic | every N frames (default 15, ~1-1.5 s at live rates) while tags are unsettled |
-| Movement | the band's screen position moved more than ~2% of frame width since the last pass |
-| Settle | a band whose tags are all `settled` (see below) is skipped entirely |
-| On demand | photo mode, or the client asks (`inspect`) |
-
-Band extraction reuses the geometry we already have: `empty_shelf_detector.cluster_rows()`
-groups product boxes into rows, and a rail band is the strip below a row
-(`row_bottom` to `row_bottom + ~8% of row height`, clipped to the frame). No new
-geometry model is needed, and it is the same code the empty-slot detector trusts.
-
-### 3.2 Voting: the accuracy lever
-
-Every pass produces a `TagObservation` (box, text lines, parsed fields, confidence).
-Observations that overlap the same tag (IoU ≥ 0.5 against the cached tag) accumulate
-into that tag's history, and the *published* value is the vote, not the latest read:
-
-- **Price**: majority vote over parsed integers; ties break to the highest-confidence
-  read. (A misread `26円` vs `269` is resolved by repetition - the single strongest
-  cheap accuracy improvement available, and the reason caching is a feature.)
-- **Name**: longest common substring across observations, or the highest-confidence
-  read when observations disagree entirely.
-- **Barcode**: first successful decode wins permanently (it either checksums or it
-  does not).
-- A tag is `settled` after 3 agreeing observations or 8 passes, whichever comes first;
-  settled tags stop costing OCR time.
-
-### 3.3 Latency budget
-
-| Stage | Cost | Cadence | Contribution to frame loop |
-| --- | --- | --- | --- |
-| Rail band crop | <1 ms | per pass | 0 (outside the loop) |
-| OCR, Vision backend | ~180 ms/band | 1-2 Hz | 0 |
-| OCR, RapidOCR backend | 0.75-1.3 s/band | 0.3-0.5 Hz, band-parallel | 0 |
-| Parse + vote + associate | <1 ms | per frame | ~0.2 ms |
-| Merge into result | <0.5 ms | per frame | ~0.2 ms |
-
-Worst realistic case: 4 bands x RapidOCR at 1.3 s = 5.2 s of work per full shelf
-sweep, spread over 1-2 workers while detection keeps running. On macOS with Vision it
-is ~0.7 s for the whole shelf. The frame loop is unaffected either way.
-
----
-
-## 4. Price-tag detection
-
-Four approaches were considered.
-
-| Approach | Needs training data | Robust to blur | Works on demo images | Verdict |
-| --- | --- | --- | --- | --- |
-| A. Geometry only (bright quadrilateral in the rail band) | no | partly | no (tags too small to segment reliably) | fallback |
-| B. Trained tag detector (YOLO-nano on tags) | **yes, none exists** | yes | after training | target state |
-| C. OCR-native text regions clustered into tags | no | it is the same failure as OCR | at readable distance | **start here** |
-| D. Hybrid: C now, B later, A as fallback | no → yes | yes | at readable distance | **recommended path** |
-
-Recommended sequence: **C → B**.
-
-**C (now).** The OCR engine's own detector returns text-region boxes (RapidOCR found
-23 in a rail band; Vision found 20). Cluster regions inside a rail band into tag
-candidates:
-
-1. Take regions whose box height is 8-40% of the band height and whose center is
-   inside the band.
-2. Cluster by horizontal proximity (gap ≤ 1.5 x median region height) into groups.
-3. A group becomes a tag if its bounding box has aspect 2.5:1 to 8:1 and width
-   ≥ 4% of frame width - the measured tag shape is ~300x80 px (3.75:1) at 1 m.
-4. Reject groups that are pure noise: no parsed price AND no kana/kanji AND fewer
-   than 2 regions.
-
-**B (later).** Bootstrap a tag dataset from C's output (auto-labelled tag boxes,
-human-verified once), train a YOLO-nano (same ultralytics toolchain as the product
-detector, same deployment path), and switch detection to it. A trained detector makes
-tag detection independent of OCR quality - the tag box is found even when its text is
-unreadable, which is exactly what compliance needs.
-
-**A (fallback)** stays for the case where OCR returns nothing: a brightness/saturation
-rectangle test inside the band, used only to answer "is there *something* tag-shaped
-here", never to read text.
-
-### 4.1 Associating a tag with a product
-
-A tag belongs to the product whose facing sits directly above it on the same rail:
-
-1. For each product box, the candidate band is `[row_bottom, row_bottom + 8% height]`.
-2. Candidate tags are those with horizontal overlap ≥ 50% of the *tag* width, or,
-   failing that, nearest tag center within 0.5 x product width.
-3. Each tag belongs to at most one product; each product gets at most one tag
-   (the closest by center distance). Unclaimed tags are `orphan_tag` candidates.
-4. The association is recomputed per frame from cached tags - cheap, and it follows
-   the camera as the track moves.
-
----
-
-## 5. OCR engine abstraction
-
-Same pattern as the app's `platform/` bridges: one interface, several implementations,
-picked by availability and configuration.
-
-```python
-class OcrEngine(Protocol):
-    name: str
-    def read(self, image: np.ndarray, *, hints: OcrHints) -> list[OcrLine]: ...
-    def available(self) -> bool: ...
-
-@dataclass
-class OcrLine:
-    text: str
-    confidence: float
-    box: list[int]        # xyxy in the input image's pixels
-
-@dataclass
-class OcrHints:
-    languages: tuple[str, ...] = ("ja", "en")
-    mode: str = "sparse"          # "sparse" = rail bands/tags; "dense" = full page
-    max_regions: int | None = None
-```
-
-Backends:
-
-| Backend | Class | Selection rule |
-| --- | --- | --- |
-| `vision` | `VisionOcr` (macOS, `pyobjc` `VNRecognizeTextRequest`) | default on macOS when importable; measured 182 ms/band |
-| `rapidocr` | `RapidOcr` (`rapidocr-onnxruntime`) | default elsewhere (Windows/Linux); measured 0.75-1.3 s/band |
-| `fake` | `FakeOcr` | tests, and the deterministic demo |
-| `paddle` | `PaddleOcr` | only if RapidOCR's Japanese accuracy proves insufficient |
-
-Tesseract is rejected on measurement (§1.2). Cloud OCR (Google/Azure) is rejected: the
-demo must run offline, and shipping shelf images to a third party is a privacy decision
-the owner has not made.
-
-Two preprocessing rules, both from the measurements:
-
-- **Never OCR the whole frame in live mode.** Crop the rail band (and later the tag
-  box) - a full 1280 px frame costs 3.4 s in RapidOCR for 143 regions we do not want.
-- **Upscale small tags 2-3x with INTER_CUBIC before OCR** when the tag's digit height
-  is below ~20 px; beyond 3x the blur wins and accuracy falls.
-
-### 5.1 Parsing a Japanese price tag
-
-Field extraction is deterministic parsing over the OCR lines, not another model:
-
-| Field | Rule | Example |
-| --- | --- | --- |
-| `price` | largest integer 2-5 digits inside a red/black large region, or the integer adjacent to `円` | `269` |
-| `regular_price` | integer near `割引` / on the left with smaller size | `357` |
-| `discount` | `(\d+(\.\d+)?)\s*割` | `2.4割引` → 24% |
-| `unit_price` | `\d+\.\d{1,2}` (per 100 g / per 10 mL convention) | `290.52` |
-| `name` | longest kana/kanji run with confidence ≥ 0.3, excluding prices and sale words (`毎日お買得`) | `キュービーゼロ ノンコレステロール<310g>` |
-| `jan` | barcode decode of the tag's barcode region (separate step) | `4902105...` |
-| `flags` | `sale` when a discount or 割引 present, `tax_included` on 税込, `new` on 新発売 | |
-
-Sanity rules that reject a parse outright: price outside 1-99999; discount > 90%;
-more than one candidate price with no size ordering. A rejected parse publishes
-`price: null` with `quality: "unparsed"` - never a guess.
-
----
-
-## 6. Reconciliation: item vs tag
-
-Inputs per facing: the identity decision (accepted / ambiguous / unknown / rejected,
-with candidates), the associated tag (if any), and the catalog entry.
-
-Signals, strongest first:
-
-| Signal | Strength | Requires |
-| --- | --- | --- |
-| JAN decoded from the tag's barcode == catalog JAN | **exact** | catalog JAN column, capture quality |
-| OCR name ≈ catalog name (normalized kana/kanji + romaji form, LCS ratio ≥ 0.6 for tags, ≥ 0.4 for packaging) | strong | readable name; catalog describes the store |
-| OCR price == catalog price (regular or sale) | medium | coherent catalog/tag data |
-| Position (tag directly below, same rail slot) | association only | geometry |
-
-Matching must compare three forms of the catalog name - normalized kana/kanji,
-NFKC-folded, and a romaji transliteration - because the packaging sometimes prints the
-name in Latin script (`Magari Senbei` for 亀田のまがりせんべい, measured).
-
-Reconciliation states (published per facing, alongside - never replacing - the
-identity state):
-
-| State | Condition | UI |
-| --- | --- | --- |
-| `confirmed` | accepted identity AND a strong/medium signal agrees | green chip "tag matches" |
-| `mismatch` | accepted identity AND a signal disagrees (price differs, name unrelated, JAN differs) | amber chip, both values shown |
-| `unverified` | accepted identity, tag present but unreadable or unparsed | grey chip "tag unread" |
-| `no_tag` | accepted identity, no tag found under the facing | red chip (compliance input) |
-| `tag_only` | tag read, no accepted identity above it | tag shown on its own |
-
-Hard rule: `mismatch` never silently changes the product identity. It is a *second
-opinion* displayed next to the first; only a JAN match may promote an `ambiguous`
-identity to `accepted` (that is the one case where the tag is stronger evidence than
-the matcher, because a checksummed barcode is not a guess).
-
-### 6.1 Catalog changes required
-
-```csv
-SKU_ID,product_name,receipt_date,expiration_date,number_in,number_out,number_on,image_link,price,jan,regular_price
-sku_003,Tropicana エッセンシャルズ マルチビタミン,...,168,4902105xxxxxx,198
-```
-
-- `jan` (optional): enables exact reconciliation.
-- `regular_price` (optional): lets the tag's regular/sale pair be checked separately.
-- Both are additive; `InventoryManager` already ignores unknown columns, so nothing
-  breaks until the columns appear.
-- **Demo coherence:** for a believable reconciliation demo, the tags photographed must
-  belong to the same store as the catalog. Either shoot/curate shelf photos whose tags
-  match the demo catalog, or render synthetic tags for the demo catalog and place them
-  on the shelf (a small, honest demo asset - labelled as synthetic). Real-store photos
-  with our fictional catalog will legitimately show `mismatch` everywhere, which
-  demonstrates the compliance path but not the reconciliation path.
-
----
-
-## 7. Price-tag compliance
-
-First rule family of the (future) rules engine. Each rule is a pure function over
-settled evidence, returning findings with boxes and counts - no side effects, no
-network.
-
-| Rule id | Fires when | Evidence | Guard |
-| --- | --- | --- | --- |
-| `missing_price_tag` | an accepted facing has no associated tag | facing box, row, SKU | only on rows where tag detection is `readable` (≥1 tag parsed in that row) |
-| `missing_tag_group` | **≥ 3 consecutive facings in a row** have no tag | row index, span box, count | same guard; this is the owner's "group of items missing price tags" |
-| `orphan_tag` | a tag has no facing above it | tag box, row | tag must be parsed |
-| `price_mismatch` | `mismatch` state from a price signal | facing box, tag price, catalog price | catalog price must exist |
-| `duplicate_tag` | one tag associated with two facings | tag box, both facing boxes | - |
-| `unreadable_tags` | a row where tags were found but none parsed | row box, count | informational, never a violation |
-
-The guard in the first two rows is the whole design: a row whose tags are simply
-unreadable (every row in `image.jpg`) must produce a **capture-quality hint**, not a
-compliance violation. That is principle 2, implemented.
-
-Aggregation: findings roll up per row, then per frame/session, into
-`compliance: {rows: [...], violations: N, by_rule: {...}}`. The UI shows a row-level
-strip ("Row 3: 4 facings without price tag") with the offending boxes highlighted -
-the same red-box language the empty-slot detector already uses.
-
-Evaluation cadence: per frame from settled evidence only (cheap), with a stability
-filter (a finding must hold for 3 consecutive frames) so nothing flickers.
-
----
-
-## 8. Protocol changes (additive, protocol stays 1)
-
-Per track/detection:
-
-```json
-"price_tag": {
-  "box": [812, 404, 909, 441],
-  "state": "readable | unreadable | absent",
-  "price": 269, "regular_price": 357, "discount": 0.24, "unit_price": 290.52,
-  "name": "キュービーゼロ ノンコレステロール<310g>",
-  "jan": null,
-  "confidence": 0.82,
-  "observations": 3,
-  "settled": true
-},
-"reconciliation": {
-  "state": "confirmed | mismatch | unverified | no_tag | tag_only",
-  "signals": [{"kind": "price", "agrees": true, "item": 269, "tag": 269}],
-  "message": null
-}
-```
-
-Frame summary gains:
-
-```json
-"summary": {
-  "price_tags": {"readable": 12, "unreadable": 3, "absent": 4},
-  "compliance": {"violations": 1, "by_rule": {"missing_tag_group": 1},
-                 "rows": [{"row_index": 3, "span": [x1,y1,x2,y2], "missing": 4,
-                           "skus": ["sku_003", "sku_009"]}]}
-}
-```
-
-New WebSocket control message (optional, for tap-to-inspect freshness):
-
-```json
-{"type": "read_tags", "request_id": "t1", "boxes": [[x1,y1,x2,y2]]}
-→ {"type": "read_tags_result", "request_id": "t1", "tags": [...]}
-```
-
-REST `recognize` gains the same two fields per detection, plus
-`?tags=true` to request the tag pass on a still (photo mode default on).
-`inspect` gains `tag` (the tag associated with the tapped box) so the mobile/web card
-can show OCR text without a second round trip.
-
----
-
-## 9. Client changes (web now, mobile after)
-
-Web (`code/web/`):
-
-- Tag boxes drawn in a distinct style (thin teal outline, small price chip when
-  parsed) so they never read as product boxes.
-- Product card gains an **OCR section**: price read vs catalog price, name read,
-  discount, and the reconciliation chip (`confirmed` / `mismatch` / `unreadable`).
-- A **compliance strip** under the stage: rule, row, count; clicking highlights the
-  boxes.
-- Quality hint gains `move_closer_for_tags` when a row's tags are unreadable - the
-  measured failure mode from §1.1.
-
----
-
-## 10. Mobile live scan (the second ask)
-
-Target: the dorayaki app's scan screen does what the web demo does - live boxes,
-tap a product, see the product card, and see the tag/OCR information under it.
-
-The app repo (`/Users/ngotrung/Documents/Projects/dorayaki`) requires an **ADR**
-before a second API base URL is added (its `AGENTS.md`), and its `ScanCaptureView` is
-currently a timer-driven placeholder (`ScanViewModel.tick()`), not a camera client.
-So this lane is: one ADR, then a feature slice copied from the kit's own recipes.
-
-| Piece | Where | What |
-| --- | --- | --- |
-| ADR | `docs/decisions/ADR-002-recognition-server.md` | second base URL, why, flavor mapping (Debug → `http://<mac>:8080`, Staging/Prod later) |
-| Camera bridge | `shared/.../platform/CameraFrames.kt` + `iosMain` (AVFoundation) + `androidMain` (CameraX) | `Flow<FrameBytes>` at a chosen cadence; JPEG encode on the platform side (official APIs only) |
-| Live client | `shared/.../data/shelf/LiveScanClient.kt` (Ktor WebSockets) | connect, send frames, receive results, expose `Flow<LiveScanFrame>` |
-| Domain | `shared/.../domain/shelf/LiveScan.kt` | `LiveTrack`, `LiveIdentity`, `PriceTag`, `ComplianceFinding`, `LiveScanFrame` - mirroring the wire schema, with defaults for every optional field (kit rule) |
-| ViewModel | `shared/.../feature/shelf/LiveScanViewModel.kt` | `@NativeCoroutinesState` flows; tap → `selectTrack(id)`; inspect via the socket |
-| iOS UI | `iosApp/iosApp/ScanCaptureView.swift` | overlay boxes (SwiftUI `Canvas`), tap hit-test, product sheet with the OCR section |
-| Android UI | `androidApp/.../screens/ScanScreen.kt` | same, Compose |
-| Tests | `commonTest` | protocol parsing with a fake socket; identity/price-tag state mapping |
-
-Sequencing inside the lane: iOS first (the owner demos on iPad), Android immediately
-after, because the kit's contract is two native UIs.
-
-Protocol subset the app needs: `hello`, binary frames, `result`, `inspect` /
-`inspect_result`. Everything else is optional. Tap-to-inspect maps to the existing
-`inspect` message, so the card shows crop + feature matching + tag in one reply.
-
-Constraint to respect: phone cameras are only available over HTTPS or localhost, so
-the demo path is either `--ssl-certfile` (mkcert) or a tailnet-only HTTPS edge. The
-server already supports the flags; the ADR should record the chosen option.
-
----
-
-## 11. Data and capture plan
-
-1. **Capture requirement (measured):** tag digit height ≥ 20 px, tag width ≥ 150 px.
-   At a typical Japanese store tag (60 mm wide, digits ~8 mm), that is a phone at
-   40-80 cm from the rail, or a 4K frame at ~1.5 m. The live client should show
-   `move_closer_for_tags` below that.
-2. **Tag images for the demo:** shoot one shelf rail (or three) at ~50 cm with a phone
-   in the existing demo store style; 10-20 tags is enough to validate the whole path.
-   Alternatively render synthetic tags for the demo catalog and composite them onto a
-   shelf photo - faster, honest if labelled, and it makes reconciliation demonstrable
-   without a real store trip.
-3. **Tag detector dataset (later):** auto-label with the §4 pipeline, human-verify
-   once, train YOLO-nano. Target ~300 tag instances across 30-50 rail images.
-4. **Catalog:** add `jan` and `regular_price` columns (optional, additive).
-5. **Barcode decode:** `zxing-cpp` (pip, cross-platform) or `pyzbar` (needs the zbar
-   system library); decode the tag's barcode region only, at 2-3x upscale. Adopt once
-   capture quality allows - a decoded JAN is worth more than every other signal
-   combined.
-
----
-
-## 12. Risks and failure modes
-
-| Risk | Impact | Mitigation |
-| --- | --- | --- |
-| Demo images cannot validate tag OCR | the feature looks unproven at demo time | capture plan §11.1-2 before implementation lands; validate on the catalog templates meanwhile (67% measured) |
-| Catalog does not describe the photographed store | text/price confirmation is sparse (measured 10% / 3%) and every "mismatch" is noise | treat absence as neutral; validate on coherent data (templates, or a re-shot demo shelf); §6.1 |
-| OCR misreads a digit and a mismatch is reported | false compliance alarm | multi-pass voting (§3.2); mismatch is displayed, never auto-applied; compliance rules require the readable guard |
-| OCR load starves detection | frame rate drops | separate worker, band crops only, settle-and-skip cache, measured budget §3.3 |
-| Tag detector without training data | no tag boxes on unreadable rows | hybrid C→B; geometry fallback marks "something tag-shaped" without reading |
-| Catalog/tag data incoherent in the demo | reconciliation demo shows only mismatches | §6.1 demo coherence choice |
-| Japanese text normalization (kana variants, 半角/全角, size suffix) | name matching noise | normalize NFKC + strip size/`<310g>` before LCS |
-| Mobile lane needs an ADR and touches the kit's architecture | schedule risk in the other repo | ADR first, then a slice copied from `add-kmp-feature`; no kit changes |
-
----
-
-## 13. Phased plan
-
-| Phase | Deliverable | Acceptance |
-| --- | --- | --- |
-| **0. Capture assets** | one rail shot at ≤80 cm (or synthetic tags composited) | a tag crop where Vision and RapidOCR both read price + name |
-| **1. OCR service + packaging-text identity** | `OcrEngine` interface, Vision + RapidOCR + fake backends, band cropping, cache, vote; packaging text joins the identity decision as a confirmation signal | on the 21 catalog templates, packaging text confirms ≥ 60% of identities (measured baseline 67%) with **zero false contradictions**; on `image.jpg` the signal stays informational (measured 10%); frame loop timing unchanged (detect p50 within 10% of baseline) |
-| **2. Tag detection + OCR + association** | rail-band geometry, tag clustering, parsing, tag↔product association; API fields; web tag boxes + OCR card section | on the Phase-0 rail shot: ≥ 80% of tags detected, ≥ 70% of prices parsed correctly after voting; tap shows tag text in the web card |
-| **3. Reconciliation + compliance** | signal table, states, five rules, compliance strip, stability filter | price-mismatch and missing-tag-group findings reproduce on a seeded shelf; unreadable rows produce hints, never violations |
-| **4. Mobile lane** | ADR, camera bridge, Ktor WS client, domain models, iOS scan overlay + tap card, Android parity | on device: live boxes from the server, tap opens the product card with tag/OCR info |
-| **5. Data upgrades** | JAN + regular_price columns, barcode decode, tag detector training | JAN from a tag promotes an ambiguous identity to accepted in a test |
-
-Phases 1-3 are server + web (this repo). Phase 5 is independent.
-
-**Phase 4 does not have to wait.** The mobile lane can start against protocol v1
-exactly as it is today: the app gets live boxes, tap-to-inspect, and the product card
-with crop and feature matching. Tag/OCR fields are additive, so when Phase 2 lands the
-card gains the OCR section without a protocol change and without touching the app's
-transport layer. Starting it early is the parallel workstream; freezing Phase 2's
-schema first only removes the option of renaming a field later.
-
-Recommended parallel split:
-
-| Track | Owner | Files |
-| --- | --- | --- |
-| Server: OCR service, tag pass, reconciliation, compliance | this repo | `code/ocr.py`, `code/tags.py`, `code/live.py`, `code/engine.py`, `code/app.py` |
-| Web client: tag boxes, OCR card section, compliance strip | this repo | `code/web/**` (disjoint from the server files) |
-| Mobile: ADR, camera bridge, WS client, tap card | dorayaki app repo | `shared/**`, `iosApp/**`, `androidApp/**` |
-
----
-
-## 14. Open questions for the owner
-
-1. **Capture**: do we shoot real tags (a store trip, or the existing photos at closer
-   range), or composite synthetic tags for the demo catalog? The second is faster and
-   makes reconciliation demonstrable; the first is more honest as a product claim.
-   Either way the demo needs one shelf whose tags and catalog describe the same store.
-2. **Engine default**: accept RapidOCR as the cross-platform default, with macOS
-   Vision as the fast path on Mac? (PaddleOCR stays a candidate if Japanese accuracy
-   proves insufficient.)
-3. **Mobile order**: iOS first then Android, or both at once? (The kit's contract is
-   both; the demo is an iPad.)
-4. **Catalog**: add `jan` and `regular_price` now (needed for exact reconciliation),
-   or keep price-only reconciliation for the demo?
-5. **Parallel start**: should the mobile lane start now against protocol v1, in
-   parallel with the server work?
diff --git a/recognization/docs/research/2026-09-28-recognition-architecture/DECISION.md b/recognization/docs/research/2026-09-28-recognition-architecture/DECISION.md
deleted file mode 100644
index 7ae8363..0000000
--- a/recognization/docs/research/2026-09-28-recognition-architecture/DECISION.md
+++ /dev/null
@@ -1,1038 +0,0 @@
-# Dorayaki product recognition: architecture decision
-
-Audited 2026-09-28 · submodule `25dbf13` on `fix/live-recognition-quality` plus
-its uncommitted diff (snapshot: `git stash create` → `2785c07`) · auditor:
-Claude Opus 5.5. Implemented and re-measured 2026-09-29; every section that
-carries a later number says so, and the audit-time figures are kept beside them
-rather than replaced.
-Scope: identity pipeline (matcher, gallery, decision, evaluator, catalogs,
-labels), live track/label logic, the uncommitted diff. Not audited: detector
-quality, tag/planogram pipeline, iOS/Android rendering, on-device runtime.
-Evidence scripts and the corrected labels: [`evidence/`](evidence/README.md).
-
-## Verdict
-
-Build a **local-feature instance matcher plus a text lane, behind a calibrated
-open-set decision, with no global embedding in the critical path**. At the
-current scale the matcher runs exhaustively; past ~150 SKUs, a
-RootSIFT descriptor index replaces exhaustive matching. Generic DINOv2 is
-rejected for both classification and retrieval.
-
-The biggest risk is none of the architectures. It is that **there is no
-trustworthy benchmark**. The only labelled data is one photo, `add/image6.jpg`,
-and 26 of its 111 labels (23 %) were wrong. Every number in the handoff was
-computed against those labels. Label and catalog errors are larger than any
-architecture difference measured so far. They also hide the real product
-failure: displayed-label precision is 29–43 % because same-brand siblings the
-catalog doesn't know about get labelled with confidence.
-
-On corrected labels, with thresholds tuned on the same single photo (so read
-this as direction, not proof), the recommended design beats Van's contract on
-coverage and on precision together:
-
-| Live resolution, clean 7-SKU catalog, 61 in-catalog + 48 unknown facings | Correct shown | Coverage (of 61) | Precision of shown labels | Unknowns labelled |
-|---|---|---|---|---|
-| Van's matcher, always argmax (the floor) | 35 | 57 % | 35 % | 48 / 48 |
-| Current server gate (`decide_identity` defaults) | 32 | 52 % | 43 % | 22 / 48 |
-| Improved matcher, geometric-confidence gate only (τ=2) | 26 | 43 % | 79 % | — |
-| **Improved matcher + OCR veto + OCR lane (τ=2)** | **36** | **59 %** | **95 %** | **0 / 48** |
-| Same, looser (τ=1) | 40 | 66 % | 87 % | 2 / 48 |
-
-The one experiment that would most change this recommendation is a second,
-independently captured and labelled shelf visit. §7 defines it.
-
----
-
-## 1. Problem formulation
-
-Dorayaki's identity task is **open-set instance recognition over a small,
-store-scoped, enrolled catalog, with live-video evidence accumulation**.
-
-- **Instance, not category.** The classes are individual printed artworks
-  (JAN-level SKUs). Siblings share maker, palette, lattice pattern, mascot and
-  silhouette, and differ by a word ("コクうま 65%"), a colour (pink vs red
-  lattice), or a small logo. That is the regime of planar instance matching
-  (logo and text geometry), not of semantic embeddings. Measured: raw DINOv2 is
-  12–21 % top-1 on 7 SKUs; the local matcher is 76–98 %.
-- **Retrieval-shaped at scale, closed-set-shaped today.** With tens of SKUs,
-  every enrolled reference can be verified exhaustively. With thousands, it
-  becomes retrieval plus verification (§6).
-- **Open set is the dominant difficulty, and it comes in two kinds.** *Far*
-  unknowns (other categories) are rejected easily **once the accept lines are
-  calibrated against them**: 3.09 % at the old lines, 0.066 % at the shipped
-  ones, on 4580 crops from 64 unrelated Wikimedia supermarket photos, at a
-  threshold that keeps 42 of 48 correct image6 labels. The audit's 0.2-0.7 %
-  was measured on 602 crops from the `add/` photos with the audit's own variant
-  matcher; the shipped lane at the shipped lines was 3.09 % until 2026-09-29
-  (§ Re-measured, far unknowns).
-  *Near*
-  unknowns (siblings of enrolled products: Kewpie 450 g next to Kewpie からし,
-  Pure Select plain next to Pure Select コクうま) produce genuine,
-  geometrically consistent evidence and can't be thresholded away. Near
-  unknowns get fixed by **catalog completeness** and by **text**, not by a
-  better score.
-- **Multimodal by nature.** Text is the designed discriminator between
-  siblings; manufacturers print the variant name precisely so shoppers can
-  tell them apart. OCR identifies the right SKU among all 12 catalog rows on
-  41–42 of the 55 facings where it reads anything, including SKUs with no
-  enrolled image, using only catalog names and packshot text.
-- **Temporal.** Live video gives many cheap, noisy looks at one physical
-  facing. The right model is accumulated evidence per track, with a
-  provisional → confirmed label contract. The current model is one accepted
-  observation that locks the label forever.
-
-So the hierarchy is: store/category scope → candidate generation (trivial
-now, descriptor index later) → local geometric verification + text
-verification → calibrated open-set decision → temporal accumulation.
-
-## 2. Evidence audit
-
-`R` reliable · `C` directionally useful but confounded · `X` invalidated ·
-`U` still unknown. "Reproduced" means re-run in this audit with the scripts in
-`evidence/`.
-
-| # | Handoff claim | Verdict | What this audit found |
-|---|---|---|---|
-| 2 | Van's matcher is SIFT/ORB/BRISK + RANSAC, always argmax, no rejection | R | Scoring core in `code/feature_matcher.py` is byte-for-byte Van's (`git diff b443e3c`); only `decide_identity` was added. "SURF" is SIFT (SURF isn't in this OpenCV build). |
-| 2 | "170/170 named ≠ 170/170 correct" on image.jpg | R (logic) / U (numbers) | No labels exist for `image.jpg`; its accuracy was never measured. |
-| 3 | Current server collapsed onto hub SKUs (sku_003, sku_l09) | C | Hubness is real for DINO (50–60 % max-SKU share on the clean catalog, reproduced). The l09 collapse was mostly a wrong l09 reference plus shelf-cut templates. |
-| 3 | Live resolution degrades identity | X | Controlled rerun: with correctly scaled boxes, live is not worse than full (original matcher 35/42 live vs 32/42 full; improved 36 vs 40). |
-| 3 | DINO embeddings are inappropriate for fine-grained SKU identity | R | Reproduced: gallery top-1 21 % full / 12 % live, hub alarm. |
-| 4 | `stock-lopia` was corrupt, leaked, and incomplete | R | Worse than reported: its crops come from the eval photo, so scoring against it is memorization. It scores 94–97 % "identity", and the evaluator's leak check misses it because the re-cut crops aren't the same size. |
-| 7 | `eval_identity.py` is the right research direction | C | Right skeleton, four more defects (F-07). |
-| 8 | Bug #1, template IDs vs SKU IDs | R, and **still live in production** | Fixed only in the evaluator. The live path accepts `sku_l01_1`, then finds no catalog row for it (F-02). |
-| 9 | Hybrid 80 % > matcher 70 % | X | About one facing, on wrong labels. |
-| 10 | Bug #2, unscaled boxes caused the live collapse | R | Reproduced on *both* catalogs: unscaled boxes give 11–17 %, scaled boxes give 76–97 %. The confound is resolved. The coordinate fix, not the catalog cleanup, removed the collapse. |
-| 11 | Clean packshots are a dramatic improvement | R, with a caveat | True against the leak-free baseline. The l06 packshot shows a superseded package design (F-12). The l09 packshot was 305 px tall at audit time and has since been replaced by a 1000x1000 one, so that caveat no longer applies to the shipped catalog. |
-| 12 | Bug #3, the reject gate inside the shortlist | R | Hybrid-49 %-vs-20 % result is invalid. |
-| 13 | DINO top-K sweep: recall 71 %/100 %, K doesn't hurt the matcher | C | On a 7-SKU catalog, top-5 is 71 % of the catalog; that sweep can't speak to retrieval. Reproduced on v2: top-3 recall 79–81 %, top-5 95–98 %. |
-| 14 | Matcher ~8 ms vs hybrid ~45 ms | R, with a correction | The hybrid lane scores the **full** catalog and then filters (`eval_identity.py` `HybridLane.rank`), so its latency is DINO + full matcher by construction. The measurement is honest; it doesn't describe a real retrieval design. |
-| 15 | 69 % full / 66 % live after the l09 fix | R as arithmetic, X as a measurement | Reproduced exactly (24/35, 23/35) **on v1 labels**. On corrected labels the same matcher and catalog give 71 %/76 % after the gate and 76 %/83 % ungated top-1 ranking. The 69/66 gap that justified `crop_first_attempt` doesn't exist. |
-| 16 | Local matcher is the strongest identity primitive | R | And improvable: multi-scale RootSIFT + colour re-rank 40/42 vs 32/42 at full (sign test p = 0.04); at live 36 vs 35 (noise). |
-| 16 | DINO as coarse retriever at scale | X (this setting) | Recall@10 falls 42 → 32 → 17 → 7 of 42 as the catalog grows 7 → 57 → 207 → 493 SKUs. A RootSIFT descriptor index holds 37–38/42 recall@5 over the same range. |
-| 17 | Threshold alone can't create correct identities | R | And the missing identities are mostly catalog gaps (§3). |
-| 17 | Argmax precision ~20 % | C | 29 % full / 35 % live on corrected labels; the old number counted mislabelled facings. |
-| 21 | Live tests: 49 tests, 1 failure, 5 errors | X (stale) | `python -m unittest test_live` now passes 49/49 on both the dirty tree and the committed baseline. One test feeds a `str` result that raises inside a future callback and is swallowed, on both trees (F-13). |
-| 22 | "High confidence" list items 1–8 | R | All hold. Item 4 is now measured with corrected labels. |
-| 22 | Medium confidence 1 (69 %/66 %) | X | See row 15. |
-| 22 | Medium confidence 3 (more views help) | R direction | Leave-facing-out proxy (§3, E-5) supports it strongly, with a same-photo caveat. |
-| 22 | Not established 1–9 | U | 1 and 4 are now partly answered (§6); 2 partly (RootSIFT/multi-scale beat Van's composite); 3, 5, 6, 7, 8, 9 remain open. Item 9 (ground truth) was the most consequential open item: it was wrong. |
-
-### The four ceilings, separated
-
-On corrected labels (`eval/labels/image6.json`): 109 scored facings, 61
-in-catalog across 11 SKUs, 48 unknown, 2 excluded. The clean catalog enrolls 7
-SKUs covering 42 of the 61 in-catalog facings.
-
-| Ceiling | Value (live, improved matcher) | What limits it |
-|---|---|---|
-| A. Catalog | 42 / 61 = 69 % of in-catalog facings are answerable by image | l10 (11 facings), l07 (6), l04, l08 unenrolled; l12 has **zero** real facings on the shelf. **Since fixed:** l10 and l06 are enrolled, l12's facings are labelled, so the ceiling is 53/64 = 83 %. l04, l07, l08 and l12 remain unenrolled. |
-| B. Retrieval | n/a at 7 SKUs; RootSIFT index recall@5 = 38/42 at 493 SKUs | the 4 misses are catalog-quality cases (l06 package version, since fixed) |
-| C. Identity (ungated top-1 on enrolled) | 36 / 42 (86 %, Wilson 72–93 %) | l06 at live (1/4), two l09 facings that are mostly occluded |
-| D. Decision | 26 correct at 79 % precision (geometry only); 36 at 95 % (+ text) | near-unknown siblings: 9 of 11 unenrolled l10 facings → l09, Kewpie 450 g → l03. **Since fixed:** the size gate rejects the 450 g case; measured 77 % end-to-end at 91 % precision (full), 62 % at 93 % (live). |
-
-## 3. Root-cause model
-
-Ordered by how much of the gap to a good product each class explains.
-
-**Evaluator and ground-truth problems** (they decided every conclusion so far)
-- 23 % of v1 labels were wrong: 14 in-catalog facings marked unknown (Egg Care
-  #4–5, Fitte #22–23, Lucky #27–28, Kokuuma #40–45, Pure Select #59); 9
-  non-catalog items labelled as catalog SKUs (demi-glace pouches #31–32 as l09,
-  a garlic jar #16 as l05, a Heinz can #64 as l10, dressing bottles #76–79 as
-  l12, Kewpie 450 g #85 as l03); 2 neighbour shifts (#29 l08→l07, #75
-  l12→l11). The pattern (runs shifted along a shelf row, a `match` IoU field on
-  every row) looks like labels propagated from shelf-tag or box matching rather
-  than read from the packages.
-- The evaluator reports `top1` and `coverage` as the same quantity (accepted
-  correct / enrolled), so the identity ceiling and the decision ceiling were
-  never separated.
-- Precision excludes unenrolled in-catalog facings, which are the most
-  dangerous false accepts.
-- The gallery's candidate set comes from ground-truth labels.
-- Leak detection only catches same-size crops.
-
-**Catalog and data problems** (the largest recognition lever)
-- Incomplete category enrollment is what creates near unknowns. In the
-  leave-facing-out proxy (E-5), adding one shelf view for each of the 11 SKUs
-  on the shelf takes argmax from 34/52 to 47/52. The number that survives at
-  95 % precision goes from 14–16 to 32–39.
-- Wrong or stale references: the l09 history; the l06 package was a superseded
-  design (the match held at full resolution, failed at live: 1/4); the l09
-  packshot was 305 px, so its scores dwarfed others under Van's formula.
-  **All three fixed 2026-09-29:** l06's current package is enrolled, l09's view
-  is 1000x1000, and the upscale guard is in `LocalLane._load`.
-- Metadata integrity: `stock-lopia-clean/stock.csv` had no `sku_l09` row for
-  `sku_l09.jpg`, and its `sku_l10` row carried l09's stock counts with l10's
-  name and price. The catalog name for l12 ("焼肉のたれ 中辛", ¥299) named a
-  product that is not on the shelf. Several catalog sizes disagreed with the
-  shelf tags (l10 tag says 400 g). **All fixed 2026-09-29** - see the catalog
-  corrections section for the evidence behind each row.
-
-**Identity-model problems** (real but second-order on this data)
-- Scale mismatch: ~1000 px packshots vs 110–250 px crops is a 4–8× ratio.
-  ORB's 8×1.2 pyramid tops out at 3.6×, so ORB contributes **zero** inliers at
-  live resolution, and BRISK nearly none. Van's weighted composite is
-  effectively SIFT-only there, carrying dead weight.
-- Van's score, `inliers·1.5 + inliers/min(kp)·80`, isn't comparable across
-  templates of different resolution. With fewer than 4 matches it counts raw
-  ratio-test matches as "inliers" without any geometric check (`compute_geometric_inliers`).
-- Grayscale-only: pink (ゼロ) vs red (ハーフ) lattice is invisible to SIFT. The
-  hue re-rank fixes 2–3 facings in every configuration.
-
-**Open-set decision problems**
-- `decide_identity` for the inlier scale accepts a score ≥ 1.0 with a relative
-  margin ≥ 0.15. A single unverified match clears the floor, and the margin
-  can't reject a sibling with a strong partial match. It accepts 22–28 of 48
-  unknowns.
-
-**Live and tracking problems** (code-path evidence, unmeasured on device)
-- A confirmed track is never re-evaluated (`_on_classified` returns early when
-  `track.confirmed`), and with `vote_agree = 1` the first accepted observation
-  locks it. A wrong first label is permanent. A later, sharper, correct view
-  can't correct it.
-- Aggregation is the mean over the last three observations, with empty
-  observations counted as zeros. That's a heuristic, not evidence
-  accumulation.
-- Uncommitted diff: the crop blur gate returns without consuming an attempt,
-  so a permanently soft crop (a distant facing) is retried every `retry_gap`
-  frames for as long as it stays in view.
-
-**UX latency problems**
-- No provisional label exists on the wire or in the clients, so any multi-look
-  confirmation becomes dead time on screen. That's why `vote_agree = 2` was
-  (correctly) backed out.
-
-## 4. Architecture candidates
-
-"Recognition" = does it improve correct shown labels at fixed precision?
-"Scalability" = does it keep latency and recall flat as the catalog grows?
-
-| ID | Candidate | Tested here? | Recognition | Scalability | Verdict |
-|---|---|---|---|---|---|
-| A | Van's matcher, full reference set, argmax | yes | 32–35 / 42 identity; 29–35 % precision | linear, ~0.1–0.25 ms per reference | Floor. Not shippable (no open set). |
-| B | Matcher + engineering: RootSIFT, per-SKU reference pyramid (200/400 px tall), SKU grouping, similarity-constrained RANSAC, hue feature, calibrated gate | yes | 40/42 full (p = 0.04 vs A), 36/42 live; gate alone reaches 79–85 % precision at 36–43 % coverage | same as A | **Adopt** as identity core. |
-| C | Learned local features: ALIKED/DISK/SuperPoint + LightGlue; dense LoFTR/RoMa | no (weights not installed; downloading needs your approval) | Unknown. Headroom on enrolled facings is ≤ 6/42 at live, mostly catalog-caused. Likely strongest on small or blurred crops and package-version drift. | LightGlue is per-pair; tens of ms per pair on GPU and much more on CPU, so only viable as a top-K verifier | Postpone to experiment E-C. Adopt only if it clears the gate. |
-| D | Retrieval → Van's matcher | yes (DINO) | DINO recall collapses with scale (7/42 @10 at 493 SKUs) | DINO adds ~41 ms per crop | **Reject DINO retrieval.** |
-| D′ | RootSIFT descriptor index (FLANN) → vote → verify top-10 | yes | 35–37/42, equal to or slightly better than exhaustive | 1.7–3.9 ms per crop flat from 7 to 493 SKUs; build 171 ms for 64k descriptors | **Adopt past ~150 SKUs.** |
-| E | Retrieval → learned verifier | no | — | — | Only if C wins. |
-| F | OCR text lane against catalog text signatures | yes (macOS Vision) | Veto: precision 62 → 82 % at τ=0.5 for −1 correct. As a lane: +10 correct at live, including unenrolled SKUs. **Audit-time figures on v1 labels**; the current measurement is in § Re-measured 2026-09-29 (E-3): the lane adds 16 pp of coverage at full resolution while raising precision. | ~18–26 ms per crop on Mac; re-measured 23.3 ms p50 / 37.2 ms p95 on 40 production crops (`code/bench_ocr.py`); on-device on iOS (Vision) and Android (ML Kit) | **Adopt.** |
-| G | Fusion (geometry + colour + text + temporal + tag prior) | partly (geometry + colour + text) | Largest measured gain | — | **Adopt**; the text and geometry errors are complementary (text reads siblings apart; geometry covers text-poor faces). |
-| H | Store-trained identity head on DINO | no (it was trained on the eval shelf) | Leaked; no valid measurement exists | — | Retire with DINO. |
-| J | Shelf-tag / planogram prior | no (`tags.py` exists) | Every facing run on image6 sits above a readable tag with name and price | — | Strong hypothesis, H-02. |
-
-## 5. Recommended architecture (current scale: tens of SKUs)
-
-```
-frame / native crop
-  │
-  ├─ local lane:  RootSIFT on crop ─► verify against EVERY reference view
-  │               (exhaustive while refs ≲ 300), similarity-RANSAC,
-  │               per-SKU evidence = max over views:
-  │               {inliers, 2nd-best ratio, inlier hull cover, hue agreement}
-  │
-  ├─ text lane:   OCR (upsampled ×2 when crop height < 200 px)
-  │               ─► bigram similarity to every catalog row's text signature
-  │               (the row's name only, including rows with no image; adding
-  │                the packshot's own OCR was tried and rejected - see below)
-  │
-  ▼
-decision:  P(correct | local, text) from a small calibrated model
-           accepted  if P ≥ p_confirm            (target precision ≥ 95 %)
-           provisional if P ≥ p_provisional      (shown as tentative)
-           veto      if text names a different catalog row by margin
-           unknown   otherwise
-  │
-  ▼
-track:     log-odds accumulation per track across looks; confirm on
-           threshold or text agreement; re-open on strong contradiction
-           (hysteresis), never on a single weak frame
-```
-
-Why each piece earns its place:
-- **Local lane.** It's the only primitive that ranks fine-grained siblings
-  correctly: 36–40 of 42 enrolled facings, against 5–9 for DINO. It costs
-  ~4–10 ms per crop at 7 SKUs.
-- **Reference pyramid (200/400 px) and RootSIFT.** Together they fix the scale
-  mismatch that kills ORB/BRISK and makes scores comparable across references.
-  Drop ORB/BRISK unless ablation H-08 finds a case they win.
-- **Hue feature.** It's the cheapest fix for colour-coded variants (+2–3
-  facings everywhere).
-- **Text lane.** It's the only measured mechanism that rejects near-unknown
-  siblings, and it can name catalog SKUs that have no image yet. It
-  contributed +10 correct labels and 0 unknown accepts at live, τ=2.
-- **Calibrated decision.** It replaces a hand-set floor that accepts one
-  unverified match. It's calibrated on Tier-2 data, never on the photo it's
-  evaluated on.
-- **No DINO, no retrieval stage.** Neither adds recognition at this scale, and
-  DINO actively fails at larger scale. Exhaustive verification is cheaper than
-  one DINO forward pass until ~300–400 references.
-
-Placement: keep the matcher server-side for now; it's small and deterministic.
-Put OCR on the phone when tag and label OCR move there. Apple Vision and ML
-Kit are the official platform paths, matching the repo's `platform/` rule.
-Send the text with the crop so the server fuses both. Until then, run OCR on
-the server (macOS Vision is already wired in `code/ocr.py`).
-
-## 6. Scaling architecture
-
-Measured cost and recall to ~500 SKUs; beyond that, extrapolated and marked.
-
-| Catalog | Candidate generation | Verification | Evidence |
-|---|---|---|---|
-| 10–150 SKUs | none (exhaustive) | local + text | 3.7 ms at 7 SKUs, 7.7 ms at 57, 21 ms at 207 (small refs). Packshot pyramids cost ~2–3× per reference. |
-| 150–5,000 | RootSIFT descriptor index (FLANN now; FAISS IVF-PQ when memory matters), vote per SKU, top-10 | local + text on the shortlist | Measured flat 2–4 ms and recall@5 38/42 up to 493 SKUs. Descriptor memory at 5k SKUs × 2 views × ~500 descriptors ≈ 5 M × 128 B (uint8) ≈ 640 MB before PQ. **Extrapolated.** |
-| 5,000–100,000 | Scope first: store assortment → aisle/bay (tag or planogram prior) cuts candidates to hundreds. Then an aggregated-local-feature index (ASMK/VLAD-style) or a product-instance embedding fine-tuned on packshot↔shelf pairs. | local + text on ≤ 20 | **Hypothesis.** No store stocks 100k SKUs at once. Scoping by store and bay is the real scaling lever. Generic DINO is ruled out; a fine-tuned embedding needs paired training data that doesn't exist yet. |
-
-Exhaustive matching stops being sensible when its p95 exceeds the per-crop
-budget. At ~0.1–0.25 ms per reference and a 30 ms budget, that's roughly
-150–300 references (≈ 75–150 SKUs at 2 views). **Descriptor indexing removes
-the need for a global DINO pass**: at 493 SKUs it retrieves better than DINO
-at a tenth of the cost. The open question is same-category growth: hundreds of
-mayonnaises instead of other-category distractors. That's experiment E-6.
-
-## 7. Experimental plan
-
-Rule for every experiment: change one variable, run on fixed crops and a fixed
-catalog hash, log per facing (truth, enrolled?, per-SKU evidence vector, truth
-rank, decision, lane latencies), and report the four ceilings separately.
-
-### Benchmark tiers (build these first; they block every other decision)
-
-- **Tier 1, diagnostic:** `eval/labels/image6.json` (109 facings). Dev only.
-  Free to tune on. Its numbers never ship.
-- **Tier 2, held-out regression:** new captures that are never enrolled and
-  never tuned on. At least 3 visits across ≥ 2 LOPIA locations or days, same
-  aisle, both still photos and the phone's own 1920×1080 frames. Target ≥ 300
-  in-catalog facings over ≥ 20 SKUs, ≥ 300 near unknowns from the same aisle,
-  and the 602-crop far-unknown set from `add/`.
-  - Labels need provenance: shelf-tag correspondence plus readable package
-    text, two independent passes, disagreements marked `exclude`.
-  - Every label row carries `source`, `confidence`, and `labeler`. A label
-    file without them fails a schema check.
-- **Tier 3, live replay:** FrameRecorder walks (`code/record.py`) of the same
-  aisles with Tier-2-style labels, replayed through `code/eval_live.py`.
-
-### Experiments
-
-| ID | Question | Variable | Metric | Acceptance / falsifier |
-|---|---|---|---|---|
-| E-0 | Are the labels right? | none | Double-labelling agreement on Tier 2 | κ ≥ 0.9 on SKU identity. Otherwise fix labels before anything else. |
-| E-1 | Does B beat A on held-out data? | matcher variant | Paired identity top-1 on enrolled facings, McNemar | B ≥ A with p < 0.05 at native-crop resolution. If A ≥ B, keep Van's scoring and add only SKU grouping and the decision layer. |
-| E-2 | Is ORB/BRISK dead weight? | drop ORB/BRISK from B | top-1, latency | Drop them if top-1 changes by ≤ 1 facing per 100. |
-| E-3 | Does text move the frontier? | ± text lane | Correct shown at precision ≥ 95 %; near-unknown FAR | Text lane adds ≥ 5 pp coverage at 95 % precision. Otherwise text becomes veto only, or is removed. |
-| E-4 | Calibrated decision vs hand threshold | decision policy | Coverage at 95 % precision, calibration (ECE) on Tier 2 | Model ECE ≤ 0.05 and coverage ≥ hand threshold's. |
-| E-5 | How much is catalog completeness worth? | enroll the aisle's remaining SKUs with packshot + one shelf view from a *different* visit | Coverage at 95 % precision; near-unknown FAR | ≥ 70 % of the same-photo proxy gain (14→32 full, 16→39 live) survives cross-visit. If not, shelf views are overfitting to capture conditions. |
-| E-6 | Same-category scaling | grow the catalog with other sauce/mayo SKUs (packshots) | top-1, recall@K of descriptor index vs exhaustive; latency p50/p95 | Index recall@10 ≥ 98 % of exhaustive top-1 at ≥ 300 SKUs. Otherwise stay exhaustive and parallelize. |
-| E-C | Learned matcher | ALIKED+LightGlue (or DISK+LightGlue) as verifier over B's top-5 | top-1, coverage at 95 % precision, latency | Adopt only if +3 pp coverage at 95 % precision **and** p95 identity latency ≤ 80 ms server-side. |
-| E-7 | Live temporal model | log-odds accumulation + provisional labels vs current single-shot lock | Tier 3: time to provisional, time to correct confirmed, flips, correct labels lost | Time-to-first-correct p50 ≤ current single-shot; flips ≤ 2 %; correct-then-lost ≤ 1 %. |
-| E-8 | Does first-attempt native crop help? | `crop_first_attempt` on/off | Tier 3 correct confirmed labels, crop traffic | Keep only if +3 pp correct at ≤ 1.5× crop traffic. On Tier 1 it has no basis: live ≥ full. |
-| E-9 | Tag prior | ± shelf-tag evidence from `tags.py` | Coverage at 95 % precision | Adopt if +5 pp; also use it for catalog-gap discovery. |
-
-## 8. Migration strategy
-
-Snapshot first: the uncommitted work is saved as stash object `2785c07` (and as
-`tracked.diff` / `untracked.tgz` in the audit scratchpad). Nothing has been
-reverted.
-
-| Item | Class | Action |
-|---|---|---|
-| `code/eval_identity.py` (untracked) | evaluation infrastructure | **Keep and fix** (F-07). |
-| `gallery.py` `rank()` (diff) | evaluation infrastructure | Keep, eval-only; no production caller. |
-| `run-demo-server.sh` `--identity matcher` (diff) | clearly correct production fix | **Keep.** Also change the default `STOCK` away from `stock-lopia` (F-03). |
-| `live.py` `crop_first_attempt`, `max_attempts 2→4` | experimental | **Revert.** Its stated rationale (69 vs 66) is invalidated. Re-test as E-8. |
-| `live.py` blur gate early return, `sharpen_ratio`, `best_sharpness` | experimental, with a retry hazard | **Revert.** Re-introduce inside the E-7 evidence model, where a soft crop is a low-weight observation, not a free retry. |
-| `live.py` `vote_agree` + `_agreed_observations` | experimental | **Revert** (default 1 makes it dead code). E-7 replaces it. |
-| `test_live.py` change | encodes the reverted budget | Revert with it. |
-| `code/rebuild_catalog.py` (untracked) | catalog tooling | Keep. Add image↔CSV bijection, packshot-OCR vs name check, and eval-session refusal (enrollment protocol below). |
-| `stock-lopia-clean/` (untracked) | catalog data | Keep the images and provenance. Fix `stock.csv` (F-04) and the README's "Six SKUs" (it lists five). Replace the l06 packshot with the current package. |
-| `eval/labels/image6.json` | ground truth | Adopted; schema: `label`, `source`, `note`, `v1_label`. |
-| `identity.decide_identity` inlier branch | production | **Rewrite** as the calibrated decision (E-4); keep the cosine branch only while the gallery exists. |
-| Production score path (`engine.score_crop`, `classify_boxes`, `inspect`) | production | **Rewrite:** return per-SKU evidence grouped by canonical SKU (F-02). |
-| `feature_matcher.py` composite score | production | **Rewrite** into variant B behind an interface; keep Van's scorer as a selectable baseline lane for E-1. |
-| Identity interface | — | **Isolate:** `IdentityLane.evidence(crop) -> {sku: Evidence}` with `LocalLane`, `TextLane`, and `GalleryLane` (postponed); `DecisionPolicy` separate; `TrackEvidence` in live. |
-| DINO gallery, identity head, `finetune_identity.py` | production code | **Postpone and fence off:** not in the live path, not the default; delete after E-6 if nothing depends on it. |
-| Descriptor index | — | **Postpone** until the catalog passes ~150 SKUs (E-6). |
-| Provisional label on the wire + clients | protocol + native UI | **Postponed dependency** of E-7. Shared model gets a `provisional` identity state; Compose and SwiftUI render it as tentative. |
-
-### Enrollment protocol
-
-- **Views per SKU:** at least one current-package front packshot, stored as a
-  200/400/800 px-tall pyramid (source ≥ 800 px tall), plus at least one real
-  shelf capture at scan distance from a store visit that is **not** an
-  evaluation session. Add side or back views only for SKUs displayed rotated.
-- **Minimum usable resolution:** reject any reference view under 160 px tall
-  at its largest pyramid level. Crops that small carry no text geometry
-  (observed: facings under 60 px wide fail every lane).
-- **Metadata:** JAN, maker, product name as printed, size, package version or
-  date, source URL, capture session id, SHA-256.
-  - The enrollment tool validates a one-to-one match between images and CSV
-    rows.
-  - It OCRs each packshot and requires the catalog name's distinctive tokens
-    to appear (this check would have caught the l09/l10 mix-up and flagged the
-    l06 package version).
-  - It flags cross-SKU near-duplicates as "text-discriminated siblings".
-- **Leakage:** enrollment refuses any image whose capture session id appears
-  in an evaluation manifest. Pixel equality is not a leak test; re-cut crops
-  pass it.
-- **Versioning:** the catalog manifest has a content hash; every eval report
-  records catalog hash, label-file hash, and code SHA.
-- **Completeness rule:** a bay is "live-ready" when every SKU on its shelf tags
-  is enrolled or explicitly listed as text-only. Near-unknown precision
-  depends on this more than on any threshold.
-
-## 9. Completion gate
-
-Measured on **Tier 2 held-out** (and Tier 3 for time-to-label). Same crops,
-same catalog hash, same detector for both systems. "Van" means Van's scorer
-with always-argmax, run on that same catalog.
-
-| Gate | Definition | Pass |
-|---|---|---|
-| G1 coverage | correct shown labels ÷ all in-catalog facings (enrolled or not) | new ≥ Van; lower 95 % bound of the paired-bootstrap difference ≥ −2 pp |
-| G2 correctness | correct shown ÷ all shown | ≥ 95 % (Van: 29–35 % on Tier 1) |
-| G3 unknown handling | near-unknown false-accept rate; far-unknown false-accept rate | ≤ 5 %; ≤ 1 % (Van: 100 %) |
-| G4 time to label | Tier 3: p50 / p95 seconds from a facing entering view to a correct label shown (provisional counts only if later confirmed as the same SKU) | p50 ≤ 1.0 s and p95 ≤ 2.5 s, and p50 no worse than Van's click-to-label on a still (tap + ~50–100 ms) |
-| G5 stability | confirmed labels that later change; correct labels that disappear | ≤ 2 %; ≤ 1 % |
-| G6 per-SKU floor | recall of every enrolled SKU with ≥ 5 Tier-2 facings | ≥ 60 % (catches l06-type catalog faults) |
-| G7 hubness | max predicted-SKU share ÷ that SKU's true share | ≤ 2.0 |
-| G8 runtime | server identity latency p95 per crop; server memory | ≤ 60 ms; within the demo host's budget |
-
-Status 2026-09-29, offline on image6 plus the Wikimedia distractor sets:
-**G1's point estimate passes on the stream** (70-74 % coverage against Van's
-63 % on the same stream and catalog, 0 wrong against Van's 7), and the paired
-bootstrap is now computed: the shipped lane's correct set is a strict superset
-of Van's on 27 facings, so the 95 % lower bound is a hard +0.0 pp - a floor of
-this pairing, not a margin, and it clears the -2 pp half. **G2 passes** (95 %
-precision full, 97 % live). **G3's far-unknown half passes** (0.066 % on 4580
-unrelated crops at the shipped configuration); its near-unknown half is
-measured on image6 only, where the size gate rejects the documented 450 g case.
-**G8 passes** (identity p95 well inside 60 ms). **G4, G5, G6, G7 are not yet
-measured on held-out data** - Tier 2 does not exist, and G4/G5 need a recorded
-walk. Nothing here is a device-path result.
-
-Not complete until G1–G7 pass on the device path (Tier 3), not just offline.
-
----
-
-## Findings
-
-| ID | Finding | Severity | Evidence | Priority | Source |
-|---|---|---|---|---|---|
-| F-01 | image6 ground truth 23 % wrong | blocking | VERIFIED | P0 | open-world |
-| F-11 | No held-out benchmark exists (one photo) | blocking | VERIFIED | P0 | open-world |
-| F-02 | Production scores are per template, not per SKU → self-ambiguity and nameless labels | blocking | VERIFIED | P0 | open-world |
-| F-03 | Demo server defaults to the leaked, corrupt `stock-lopia` | blocking | VERIFIED | P0 | open-world |
-| F-04 | Clean catalog CSV ↔ image mismatch (l09/l10) | blocking | VERIFIED | P0 | open-world |
-| F-05 | Near-unknown siblings accepted; shown-label precision 29–43 % | blocking | VERIFIED | P0 | open-world |
-| F-07 | Evaluator defects beyond bugs #1–#3 | major | VERIFIED | P1 | open-world |
-| F-06 | Matcher scoring defects (scale, dead ORB/BRISK, unverified "inliers", grayscale) | major | VERIFIED | P1 | open-world |
-| F-08 | DINO unfit as classifier and retriever here | major | VERIFIED | P1 | open-world |
-| F-09 | Confirmed labels never re-evaluated; first accept locks | major | HIGH CONFIDENCE | P1 | open-world |
-| F-10 | Uncommitted live diff: unbounded blur retries; rationale invalidated | major | HIGH CONFIDENCE | P1 | open-world |
-| F-12 | l06 packshot is a superseded package design | major | VERIFIED | P2 | **fixed 2026-09-29** |
-| F-13 | Stale handoff test status; swallowed callback exception in a test fixture | polish | VERIFIED | P3 | open-world |
-
-Counts: 11 VERIFIED / 2 HIGH CONFIDENCE / 0 HYPOTHESIS (hypotheses listed
-separately) · 13 open-world. No reference baseline was used: this audit
-covers a recognition backend. The product-level floor is Van's system (G1–G4).
-
-### Workstreams
-
-1. **Evidence (single owner, first):** F-01, F-11, F-07. Blocks every
-   architecture change.
-2. **Catalog (parallel with 1):** F-03, F-04, F-12, enrollment tool checks.
-3. **Identity core (single owner, after 1):** F-02, F-06, text lane, decision
-   layer, E-1–E-4.
-4. **Live (after 3's interface exists):** F-10 revert now; F-09 and the
-   provisional label (E-7) later. The protocol plus native-UI part touches the
-   KMP app and needs its own owner.
-5. **Scaling (after 3):** E-6, and the descriptor index only when it's needed.
-
-### F-01 — image6 labels are 23 % wrong — P0 · blocking · VERIFIED
-- **Root cause:** labels appear propagated from tag/box matching (every row has
-  a `match` field) rather than read from packages.
-- **Evidence:** `evidence/image6-v1-labels-overlay.jpg`; the per-index list in §3.
-  Rescoring the same runs under v2 moves ungated live top-1 from 26/35 to 35/42.
-- **Target:** every scored label carries provenance; ambiguous facings are excluded.
-- **Change:** adopt `eval/labels/image6.json`; schema-check `label/source/note`.
-- **Validation:** E-0 double-labelling on Tier 2.
-- **Acceptance:** no architecture claim cites v1.
-
-### F-02 — template-level scores in production — P0 · blocking · VERIFIED
-- **Root cause:** `ProductFeatureMatcher.score_crop` keys scores by file stem;
-  `engine.score_crop` (`code/engine.py:424-448`), `classify_boxes`, and `inspect` pass
-  them straight to `decide_identity`. The live payload looks up
-  `identity.sku_id` in the catalog, which has only canonical ids.
-- **Evidence:** on `stock-lopia` (the demo default), 22/109 facings at full
-  and 29/109 at live are "ambiguous" only because the top two are views of the
-  same SKU. 68 of 69 (full) and 56 of 58 (live) accepted ids are
-  view-suffixed (`sku_l01_1`), which have no catalog row, so the payload
-  carries no `product` block.
-- **Target:** scores grouped by canonical SKU (max over views) before any
-  decision; the id shown is always a catalog row.
-- **Change:** group at the lane boundary (the future `IdentityLane.evidence`).
-  Share one canonicalizer with `rebuild_catalog.sku_of`.
-- **Validation:** unit test: two strong views of one SKU → accepted, canonical id.
-- **Acceptance:** zero view-suffixed ids on the wire.
-
-### F-03 — demo default catalog is the leaked one — P0 · blocking · VERIFIED
-- **Evidence:** `run-demo-server.sh:11` `STOCK="${1:-stock-lopia}"`. That
-  catalog is shelf crops cut from the eval photo, and it scores 94–97 %
-  "identity" on its own exam.
-- **Change:** default to the clean catalog once F-04 is fixed; refuse catalogs
-  without a provenance manifest.
-
-### F-04 — clean catalog metadata mismatch — P0 · blocking · VERIFIED
-- **Evidence:** `stock-lopia-clean/stock.csv` has no `sku_l09` row for
-  `sku_l09.jpg`; its `sku_l10` row has l09's counts (30/13/17) with l10's name
-  and price. Every l09 answer ships without product info; l10 has a row but no
-  image. The README says "Six SKUs … have no official pack shot" and lists five.
-- **Change:** restore the l09 row from `stock-lopia/stock.csv`, drop the
-  mangled l10 counts, fix the README, and add the bijection check to
-  `rebuild_catalog.py`.
-
-### F-05 — near-unknown siblings accepted — P0 · blocking · VERIFIED
-- **Evidence:** the current gate accepts 22–28/48 unknowns; 9/11 unenrolled
-  l10 facings go to l09 with margin 0.67–0.93 (higher than many correct l11
-  matches); Kewpie 450 g → l03 with 17–28 SIFT inliers.
-- **Target / change:** the decision layer + text veto (§5) + enrollment
-  completeness (E-5).
-- **Acceptance:** G2, G3.
-
-### F-06 — matcher scoring defects — P1 · major · VERIFIED
-- **Evidence:** ORB inliers are 0 on nearly every live crop (per-facing log
-  from `diag.py`). `compute_geometric_inliers` returns `len(good_matches)`
-  unverified when there are fewer than 4. The l09 reference (305 px) scores
-  85–129 while others score 10–40.
-- **Change:** variant B (§5), behind the lane interface, with Van's scorer kept
-  as a baseline lane.
-- **Acceptance:** E-1, E-2.
-
-### F-07 — evaluator defects — P1 · major · VERIFIED
-- `top1` ≡ `coverage` (both are accepted-correct ÷ enrolled), so the identity
-  and decision ceilings were never separated.
-- Precision's denominator excludes unenrolled in-catalog facings
-  (`covered` in `main`): it reports 50–53 % where the honest figure is 39–43 %.
-- The gallery's SKU set comes from ground-truth labels (`sku_ids` from
-  `held`).
-- Leak check requires the same shape.
-- `HybridLane` scores the full catalog, so its latency isn't a retrieval
-  design.
-- `group_by_sku` only handles the `sku_l` prefix.
-- **Change:** report the four ceilings explicitly (the per-facing log in
-  `diag.py`/`variants.py` is the template). Use the catalog, not labels, for
-  candidate sets. Refuse leakage by session manifest.
-
-### F-08 — DINO — P1 · major · VERIFIED
-- **Evidence:** gallery top-1 21 %/12 % with 50–60 % hub share; recall@10 of
-  7/42 at 493 SKUs (`dino_scale.py`); 41 ms per embedding.
-- **Change:** fence off (§8). The uncommitted `--identity matcher` default is
-  the right call.
-
-### F-09 — no identity re-evaluation — P1 · major · HIGH CONFIDENCE
-- **Evidence:** `code/live.py` `_on_classified`: `if track.state !=
-  STATE_DETECTED or track.confirmed: return`. With `vote_agree = 1` the first
-  accepted observation confirms.
-- **Target:** E-7's evidence model with provisional labels and hysteresis.
-- **Validation:** Tier 3 flips and correct-then-lost metrics.
-
-### F-10 — uncommitted live experiments — P1 · major · HIGH CONFIDENCE
-- **Evidence:** the blur gate `return`s before `track.attempts += 1`, so
-  eligibility never runs out for a soft crop. `crop_first_attempt` cites the
-  invalidated 69/66 numbers.
-- **Change:** revert per §8.
-
-### F-12 — l06 package version — P2 · major · **FIXED 2026-09-29**
-- **Evidence:** the packshot showed "内臓脂肪ケア フィッテ"; the shelf shows
-  "内臓脂肪を減らす キユーピーフィッテ". Improved matcher 4/4 at full, 1/4 at
-  live; 3/3 with a shelf reference.
-- **Fix:** the current package is enrolled. It was recorded as blocked on a
-  capture because Kewpie's own site serves the superseded design for that JAN;
-  an independent retailer listing carries the current one, and it matches the
-  shelf facings exactly (checked by eye against a crop of `add/image6.jpg`
-  before enrolling). Measured: live decision 60% → 64%, end-to-end 59% → 62%,
-  full decision 81% → 83%.
-- **Still open:** the packshot-OCR check that would have caught this. It is
-  implemented (`rebuild_catalog` refuses a view whose name/OCR score is below
-  0.70) but it is a false negative on l04 and l07, so it cannot yet be trusted
-  as a gate - see the catalog corrections section.
-
-### F-13 — stale test status — P3 · polish · VERIFIED
-- **Evidence:** 49/49 pass on both trees. An `AttributeError: 'str' object has
-  no attribute 'items'` is raised and swallowed inside a future callback in one
-  test, on both trees.
-- **Change:** make that fixture return a dict, or assert the callback path.
-
-## Hypotheses
-
-| ID | Suspected | Why it matters | The check that settles it |
-|---|---|---|---|
-| H-01 | Learned matchers (ALIKED/DISK + LightGlue) beat B on small, blurred, or re-designed packages | Could lift the identity ceiling | E-C (needs weight download approval) |
-| H-02 | Shelf-tag OCR prior raises coverage and precision | Tags are readable above every run on image6 | E-9 using `code/tags.py` |
-| H-03 | Cross-visit shelf references retain most of the leave-facing-out gain | Decides the enrollment protocol | E-5 |
-| H-04 | Evidence accumulation shortens time-to-correct-label vs single-shot lock | G4/G5 | E-7 on Tier 3 |
-| H-05 | On-device OCR is fast enough (< 30 ms per crop) and as accurate at native crop resolution | Where the text lane runs | Benchmark Vision on iPhone and ML Kit on Android over Tier-2 crops |
-| H-06 | Stream JPEG quality costs SIFT inliers at live | Cheap fix if true | Rerun `variants.py` on re-encoded crops at the client's quality |
-| H-07 | The hue feature is robust to store lighting | The colour gain could be lighting-specific | Tier 2 across stores and times |
-| H-08 | ORB/BRISK removal costs nothing | Simplifies and speeds the matcher | E-2 at native-crop resolution, where ORB helped the small l09 reference |
-| H-09 | Full-resolution OCR with ×2 upsampling matches live's text-lane gain | Full-res fusion was weaker (75 % precision) with ×1 OCR | Rerun `ocr_exp.py` + `fuse_eval.py` with full ×2 |
-
-## Preserved decisions
-
-- The matcher kept as a selectable baseline lane. The shipped default is now
-  `local` (variant B plus the text lane), which is what the measurements below
-  support; `--identity matcher` reproduces Van's scorer for E-1.
-- Bounded, drift-checked crop lane protocol (`aa25f99`, `13a4e0f`); its tests
-  pass.
-- `eval_identity.py`'s architecture-neutral lane framing and explicit
-  enrollment ceiling.
-- Clean-catalog provenance table and the `rebuild_catalog.py` refusal rules.
-- `vote_agree = 1` without provisional labels. Backing out `2` was right.
-
-## Implementation record (2026-09-28)
-
-What §8 asked for, and where it landed. Verified by the suite (274 tests) and by
-running the real server against `add/image6.jpg`.
-
-| §8 item | Status |
-|---|---|
-| F-02 per-SKU scores in `score_crop`, `classify_boxes`, `inspect` | Done. `group_by_sku` on every backend; no view-suffixed id reaches the wire. |
-| F-03 demo default catalog | Done. `run-demo-server.sh` defaults to `stock-lopia-clean`. |
-| F-04 clean catalog CSV | Done. All 12 rows; `sku_l09` added, `sku_l10` corrected. |
-| F-05 near-unknown siblings | Done via the text lane. 0 of 48 unknowns accepted at live resolution. |
-| F-06 variant B behind a lane interface | Done. `identity_lanes.LocalLane`; Van's scorer stays selectable as `--identity matcher`. |
-| F-07 evaluator defects | Done. Four separate ceilings, honest precision denominator, catalog-derived candidate set, `sku_of` grouping, leak signature. |
-| F-08 DINO fenced off | Done. Not the default, not in the live path, refuses to start without weights. |
-| F-10 uncommitted live experiments | Reverted. `crop_first_attempt`, the blur early return, `vote_agree` and `max_attempts 4` are gone. |
-| F-12 l06 current package | **Fixed.** The current package is enrolled from an independent retailer listing and matches the shelf facings exactly. Live decision 60% → 64%, end-to-end 59% → 62%. |
-| F-13 stale status / swallowed exception | Done. The fixture returns the matcher's `(scores, details)` shape; zero swallowed callback exceptions. |
-| F-09 re-evaluation with provisional labels | **Postponed as §8 specifies** — it is a dependency of E-7, which needs Tier-3 recordings that do not exist yet. |
-| Enrollment protocol: versioning | Done. Every eval report records `catalog_hash`, `labels_hash`, `image_hash`, `code_revision`. The catalog hash covers the CSV bytes and each view's bytes, not just the view names: names alone let a different packshot be swapped in under the same name with the hash unchanged. |
-| Enrollment protocol: sibling flag | Done. `rebuild_catalog` reports cross-SKU near-duplicates as text-discriminated siblings (cosine ≥ 0.98, same shape). Reported, not refused: they are legitimately different products, and only package text separates them. |
-| Enrollment protocol: completeness rule | Done. `--shelf-tags` turns it into a `live_ready` verdict; without the shelf's SKU list the verdict is `unknown`, not a guess. |
-| Enrollment protocol: pyramid floor | Done. `rebuild_catalog` reports views under 800 px tall. Every view in `stock-lopia-clean` is now 1000 px tall, so the check is clean on the shipped catalog; it still names any view that regresses. |
-
-Two defects found while wiring the lanes into the live path, both fixed:
-`live` re-sorted the local lane's scores by value, which discards the lane's own
-rank order (order comes from `rank_key`, the value is only the gate), and the
-early return on an empty local observation made the text lane's "answer on its
-own" job unreachable. Live now decides per crop on the lane's own order.
-
-Measured after the change, through the real server: `/v1/recognize` names 41 of
-112 detections at 90% precision with no view-suffixed ids; the live WebSocket
-names 12 at 100% precision with 0 unknown accepts, first label on frame 3.
-Those are detector-box figures, not label-matched ones: the 41 are detections
-whose box overlaps a labelled facing, so they measure the endpoint rather than
-the identity lane. The label-matched number is the evaluator's, below.
-
-## The baseline is Van's contract, not the gated matcher
-
-The `matcher` lane applies `decide_identity`, which is not what the original
-demo did - it answered every crop with its best-scoring SKU and had no accept
-line. Comparing against the gated matcher understates the baseline's coverage
-and overstates its precision, so the evaluator now has a `van` lane: the same
-scores, always answered. Every "vs Van" claim in this record uses that row.
-
-    .venv/bin/python code/eval_identity.py --stock stock-lopia-clean \
-      --labels eval/labels/image6.json \
-      --image add/image6.jpg --lanes van,matcher,local --live
-
-    full resolution — 109 scored facings (64 in-catalog, 45 unknown)
-      lane     catalog  identity  decision  end-to-end  precision  false acc
-      van          83%       77%       77%         64%        38%         68
-      matcher      83%       77%       74%         61%        51%         38
-      local        83%       92%       81%         75%        91%          5
-
-    live resolution — 109 scored facings (64 in-catalog, 45 unknown)
-      lane     catalog  identity  decision  end-to-end  precision  false acc
-      van          83%       79%       79%         66%        39%         67
-      matcher      83%       79%       74%         61%        54%         33
-      local        83%       85%       60%         59%        93%          3
-
-Against Van's real contract the local lane wins both axes at full resolution:
-75% at 91% against 64% at 38%. At live resolution it trades coverage for
-precision - 59% at 93% against Van's 66% at 39%. Van answers more facings
-correctly there and is wrong on 67 of them to the local lane's 3. That is the
-trade the decision makes deliberately: coverage is a gate (G1) and precision is
-not negotiable, so the lane abstains where Van guesses.
-
-**One accept line serves both resolutions.** An earlier draft of this section
-claimed full resolution needed a line near 6 to reach 95%, leaving 28/61
-correct. That was measured before the reference-upscaling and text-signature
-fixes and no longer holds. The separation is clean at both scales: correct
-facings sit at p25 2.07 (full) and 2.87 (live), wrong ones at p75 1.00 and
-1.66, so 2.0 is the boundary in both. Sweeping it at full resolution, through
-the evaluator (`--accept`, columns as it defines them: end-to-end is
-accepted-and-correct over the 64 in-catalog facings, precision is over the
-facings it answered). Re-measured 2026-09-29 after the accept lines were
-calibrated against unrelated shelves (§ Far unknowns); the 2.0 row is what the
-audit measured, the 4.0 row is the shipped line:
-
-    accept   end-to-end   precision   false accepts
-    2.0      75%          92%         4
-    3.0      66%          93%         3
-    4.0      66%          95%         2
-    5.0      62%          98%         1
-    6.0      59%          97%         1
-
-The line is still provisional - it is tuned on Tier 1 and needs Tier-2
-calibration - but it does not need to differ by input path, and at full
-resolution the design beats Van on both axes today.
-
-The live count was pacing-dependent, and the cause was measurable: OCR ran on
-every crop at every attempt, taking a crop from 5 ms to 25 ms and throughput
-from 789 to 60 crops/s with 8 workers. The reading is now cached on the track
-and later attempts re-run only the local lane. Measured through the real server
-on `add/image6.jpg`:
-
-    saturated pool   15.9 -> 49.4 crops/s (8 workers, 64 crops)
-    per crop         142 -> 92 ms on a 300x400 crop
-    3 s clip, send   19.7 mean labels (15-24, unstable) -> 24.0 (23-25, stable)
-    12 s clip, send  48/131 -> 53/131; result pacing 53/134 -> 50/135
-
-The 12 s numbers are within run-to-run noise; the short clip is where the
-pacing sensitivity lived, and it is now flat across pacings. Precision stays
-100% and 0 unknowns are accepted at every pacing.
-
-Re-measured 2026-09-29 as medians of three 12 s runs per pace, through the
-shipped server with the calibrated accept lines
-(`data/evaluations/2026-09-29-session/pace-medians/`):
-
-    pace     coverage (3 runs)        median   precision (3 runs)      median   wrong
-    send     63 / 70 / 67 %            67 %    77 / 88 / 97 %           88 %     0
-    result   63 / 63 / 70 %            63 %    86 / 83 / 90 %           86 %     0
-
-The two paces land within 4 pp of each other on coverage and 2 pp on precision,
-with 0 wrong labels in all six runs. The pacing sensitivity the audit measured
-is gone, and the spread across runs is now the run-to-run noise the audit said
-the 12 s numbers were inside.
-
-**The stream path now has a G1 number.** `code/score_stream.py` scores a
-recorded clip's labels against the ground truth, matching each track's
-`label_box` to the labelled facings. Three defects in the first version of
-that harness made the stream look far worse than it was, and all three are
-fixed: it counted any facing touching the window by one pixel (23 of its 47
-"in-catalog" facings were less than half visible), it scored a correct
-text-lane answer as a false accept because it checked enrollment before
-correctness, and it counted tracks rather than facings, which reported 138%
-coverage of a 24-facing window. The denominator is now the facings at least
-half inside the union of the window rects the client showed.
-
-Measured through the real server on `add/image6.jpg`, 20 s per run, local
-backend against the `van` lane on the same stream:
-
-    pan   lane    facings named   precision
-    0     van      11/24 (46%)      52%
-    0     local    11/24 (46%)     100%
-    2     van      12/24 (50%)      45%
-    2     local    18/24 (75%)     100%
-    12    van      11/24 (46%)      28%
-    12    local    16/24 (67%)      83%
-
-Re-measured 2026-09-29 with both servers on `stock-lopia-clean`, 20 s pan 12,
-`stream-gated/van_tracks.json` against `stream-gated/tracks_*.json`:
-
-    lane    facings named      precision   wrong   unenrolled false accepts
-    van      17/27 (63%)         53%        7            6
-    local    19-20/27 (70-74%)   77-85%     0            0
-
-Van names fewer facings than the local lane and is wrong on 7 of the 53 it
-does name, plus 6 accepts of products the catalog does not hold. G1 asks the
-new system to be no worse than Van on coverage; it is better on both axes.
-
-The paired bootstrap (`code/score_stream.py --van ...`) grades both runs on the
-same 27 facings: the local lane's correct set is a strict superset of Van's, so
-the difference is +7.4 to +11.1 pp and the 95 % lower bound is a hard +0.0 pp.
-That clears G1's -2 pp half, but read it as a floor of this pairing rather than
-a margin - no resample of a superset can go negative. One of the five recorded
-runs (`tracks_final.json`) is 17/27, level with Van, and does not clear the
-bound; the other four do.
-
-Two live-path bugs surfaced while measuring this, both fixed:
-
-- **Inherited labels carried frame id 0.** `_inherit_labels` set `label_box`
-  but never `label_frame_id`, so every inherited label was mapped back to the
-  source image with the first frame's offset. During a pan that puts the box a
-  whole pan-distance from the product it names. It now stamps the current
-  frame and anchors to the raw detection box rather than the smoothed track
-  box, which lags a pan by about one step.
-- **The reference lane upscaled views smaller than a pyramid level.** The
-  `sku_l09.jpg` enrolled at the time was 407x305, so its 400 px level was a
-  305->400 blow-up: a blur with no high-frequency detail to disagree with,
-  which won 31 of 54 crops on the shelf it was cut from. A view is now used
-  only at the levels it has. The guard is in `LocalLane._load` and still
-  applies; the file itself has since been replaced by a 1000x1000 packshot,
-  which is why the current run no longer exercises it.
-
-Coverage is still a gate (G1). Four 20 s pan replays of `add/image6.jpg`
-through the shipped lane name 63-70% of the in-catalog facings a window can
-show, at 76-84% precision (`score_stream.py` over
-`data/evaluations/2026-09-29-session/stream/fin_*.json`). Re-recorded
-2026-09-29 with the calibrated accept lines
-(`stream-gated/tracks_*.json`): 70-74% at 77-85%, with 0 wrong labels in all
-three runs against 0-1 before, and 8-14 unknown false accepts - the
-near-unknown case, which is the size gate's and the catalog's job rather than
-the accept line's. Van's contract on the same stream answers more and is wrong
-far more often, which is the trade the decision makes. The earlier "53 of 131
-tracks" figure was a harness artifact rather than a recognition limit.
-
-The shipped default scale now has test coverage. `make_session` builds only
-`matcher`-backend engines, so `scale == "local"` in `_on_classified` never ran
-in the suite - and that is the branch that was wrong twice. `LocalScaleEngine`
-injects the lanes' evidence into the live path, and `LocalScaleTests` pins the
-two jobs (a text-only read still labels the track; a text veto blocks a local
-accept). `DecisionPolicyTests` pins the veto/fill-in boundary, including the
-lane-order-vs-value case the rank re-sort broke. Both regressions were
-re-injected to confirm the tests fail for the intended reason. Suite 251 -> 291.
-
-Two further review findings, both fixed and both measured:
-
-- The evaluator had no always-argmax lane, so every "vs Van" claim compared
-  against the *gated* matcher rather than Van's contract. The `van` lane is
-  that contract; the numbers above use it.
-- A crop reading only "400g" scored 0.667 on three SKUs, above `TEXT_ACCEPT`,
-  so the text lane named a product from a size line. The fill-in job now
-  requires two non-digit bigrams; the veto is unchanged, so "65%" still
-  contradicts a local answer. Three tests cover it and all three fail when the
-  gate is removed.
-
-
-The headline number is reproducible from the repo, not from a scratch script:
-
-    .venv/bin/python code/eval_identity.py --stock stock-lopia-clean \
-      --labels eval/labels/image6.json \
-      --image add/image6.jpg --lanes local --live
-
-    live resolution: catalog 83% · identity 85% · decision 60% ·
-    end-to-end 59% · precision 93% · 20.3 ms/crop
-
-    false accepts: 3 of 109 scored facings — 2 of the 45 unknowns and 1
-    enrolled misidentification; unenrolled_false_accepts 0.
-
-The catalog figure fell from 87% to 83% because three facings were relabelled
-from unknown to `sku_l12` once the tare was identified (§ Re-measured
-2026-09-29), and `sku_l12` has no enrolled view. That is a catalog gap
-reported honestly, not a recognition regression: the same three facings were
-unanswerable before and after.
-
-### Re-measured 2026-09-29
-
-This is the state after the text lane's evidence floor landed and before the
-accept lines were calibrated against unrelated shelves; the current numbers are
-in § The baseline is Van's contract above (local 75% at 91% full, 59% at 93%
-live). Kept as the dated snapshot that shows what the floor alone changed.
-
-The text lane's evidence floor (shared bigrams between the read and the winning
-row's name) and the local lane's signature pass-through landed, and the
-decision ceiling was corrected: it divided a numerator that counted unenrolled
-facings by the enrolled count, so it read 117%.
-
-    full resolution — 109 scored facings (64 in-catalog, 45 unknown)
-      lane     catalog  identity  decision  end-to-end  precision  false acc
-      van          83%       77%       77%         64%        38%         68
-      matcher      83%       77%       74%         61%        51%         38
-      local        83%       92%       83%         77%        91%          5
-
-    live resolution — 109 scored facings (64 in-catalog, 45 unknown)
-      lane     catalog  identity  decision  end-to-end  precision  false acc
-      local        83%       91%       64%         62%        93%          3
-
-**E-3 answered.** `--no-text` runs the same crops and catalog without the text
-lane, which is the only way this question is answerable. Re-measured 2026-09-29
-after the ablation was corrected: the first version compared text-presence
-*and* a different argmax, because it re-sorted candidates by confidence value
-where the lane ranks by its own key. Both arms now go through the same
-`DecisionPolicy` ordering, so text is the only variable.
-
-Full resolution: end-to-end 66% at 95% precision with text, 56% at 86% without
-(2 false accepts against 6). Live: 56% at 97% against 48% at 100% (1 against
-0). The lane adds 9-10 pp of coverage at full resolution while raising precision
-by 10 pp, against E-3's +5 pp bar. At live resolution it adds 8 pp of coverage
-and costs 3 pp of precision - one false accept - which is the trade the accept
-lines already price. It is not a veto-only lane.
-
-Reproduce: `code/eval_identity.py --lanes local --live [--no-text]`.
-
-**Name-only signatures, and why.** The architecture sketch above originally
-said the text signature was "name + OCR of its packshot". That was tried and
-rejected: it is how l09 used to win l10's facings. l09's packshot prints
-"マヨネーズ", which is l10's actual name, so a row with a photographed packshot
-could explain a sibling's text better than the sibling's own name could. Every
-row in the catalog has a name, so the packshot adds nothing but that confusion,
-and `TextLane.scores_from_text` scores against the name alone.
-
-**E-2 answered.** `LocalLane` is RootSIFT-only; ORB and BRISK survive only in
-the `matcher` baseline lane, so there is nothing to drop from the shipped path.
-
-**Live path.** `_plan_classification` had lost its `in_flight` increment while
-a `label_frame_id` write was removed, so the guard never blocked, every frame
-re-planned the same tracks, and the executor queue grew until the server
-stopped answering. Restored. Label memory boxes now carry the shelf origin they
-were measured against and are shifted before the IoU test, so a pan no longer
-slides a stale box onto its neighbour. The text lane is resolved before the
-session lock is taken, removing a lock-ordering hazard against its build lock.
-
-Stream, 20 s pan, four runs: 63-70% coverage at 76-84% precision. The earlier
-"71-79% at 76-84%" was measured before the tare facings were relabelled from
-unknown to `sku_l12` and before the size gate landed; the denominator moved
-from 61 in-catalog facings to 64, and the size gate trades a little coverage
-for precision. Still-image endpoint: 53 named detections in 2.6 s, against 41
-before.
-
-### Catalog corrections 2026-09-29
-
-Three rows named products the shelf does not hold, all found by reading the
-shelf tags and the enrolled packshots rather than by trusting the CSV:
-
-| SKU | Was | Is | Evidence |
-|---|---|---|---|
-| `sku_l10` | ピュアセレクト マヨネーズ 450g | 400g | Shelf tag reads 400g; Ajinomoto's lineup is 250/400/600/1000g, no 450g. Packshot enrolled (JAN 4901001114030). |
-| `sku_l11` | キューピー ハーフマヨネーズ 450g | 400g | Shelf tag reads 〈400g〉; the enrolled packshot's own OCR reads 400g. |
-| `sku_l12` | 焼肉のたれ 中辛 | ユーラス 野菜をおいしく食べる旨たれ 380g | Tag under facings #46-48 reads that title at ¥299, the price the row carried; the package reads お子さまOK 甘めテイスト, the opposite of 中辛. |
-
-The l12 row was the dangerous one: the text lane scores a crop against the
-row's printed name, so a correct read of the real bottle would have been
-vetoed by a row describing a different product. Facings #46-48 were relabelled
-from unknown to `sku_l12` to match.
-
-Enrolling l10 took the catalog ceiling from 69% to 87% of in-catalog facings
-answerable by image; relabelling the tare facings moved it back to 83%,
-because `sku_l12` has no enrolled view. `sku_l04`, `sku_l07` and `sku_l08`
-remain unenrolled. l04 and l07 have exact packshots whose name/OCR check is a
-false negative, recorded in `data/packshots/research/` with the retailer, JAN
-and the reason the check fails. l08 could not be sourced at all: no retailer
-listing for a スパイシーストーム ペリペリミックス 30g exists, and the only
-corroborated ペリペリミックス is a different brand at 50g, so the row's name
-and size are themselves unverified.
-
-### The near-unknown case is now rejected
-
-The documented failure - Kewpie 450g bags the catalog does not hold, labelled
-as catalog rows - had no fix in the text ranking. Every mayonnaise row
-explains "マヨネーズ" equally, so the ranking ties at 1.0 and the
-strict-greater veto never fires; the veto compares two catalog rows and the
-crop's product is not one of them.
-
-`DecisionPolicy` now also checks the package size the read prints. Only a mass
-in `PACKAGE_MASS_RANGE` counts, because below it are fragments of a longer
-number ("20" out of "200g") and above it are two numbers run together ("5210"
-out of "210g"), and percentages are excluded because "カロリー 50% カット" is a
-claim about the contents. Measured on the corrected labels, full resolution:
-precision 86% -> 91%, false accepts 8 -> 5, coverage unchanged at 79%.
-
-### Far unknowns: the accept lines were tuned on a shelf that has no unknowns
-
-Every threshold in this document was fitted on image6, where each crop is a
-facing of a product the shelf actually holds. That is a closed-set tuning set,
-and it hid the far-unknown rate completely: the shipped lane accepted 82 of
-2650 crops from 60 Wikimedia Commons supermarket photos of milk, soy and snack
-shelves - 3.09 %, against G3's 1 %. (The crop count is the old full-resolution,
-`detect_conf=0.25` measurement; at the shipped configuration the same photos
-yield 4580 crops and the shipped lines accept 3 of them - 0.066 %.)
-
-Those photos share nothing with the LOPIA catalog, so every accept is a false
-accept by construction. Two mechanisms produced them:
-
-- **The text lane answered on two coincidental bigrams.** The vocabulary filter
-  drops bigrams no catalog name contains, so a short garbage read can end up
-  with a perfect ratio on two pairs. 20 of 26 text-lane answers rested on
-  exactly two: `避くて おいしい` named `sku_l12` through おい/いし.
-- **The local lane accepted pure geometry.** The residual accepts were matches
-  with no text overlap at all, scoring 2.26-4.81 against an accept line of 2.0.
-
-Both lines moved: `TEXT_MIN_SHARED` 2 -> 4, `LOCAL_ACCEPT` 2.0 -> 4.0. The
-joint sweep below re-measures both axes together through the shipped
-`DecisionPolicy` (`code/sweep_accept_lines.py`), on 4580 distractor crops from
-64 unrelated Wikimedia shelf photos at the shipped configuration (1280-long-side
-frames, `LiveSettings.conf=0.1`) and 109 image6 facings. An earlier version of
-this table swept only `TEXT_MIN_SHARED` while pinning the accept line at 4.0, so
-its "2.0" rows were not the old-line baseline and its "4.0 | 4" row duplicated
-"2.0 | 4"; the numbers below replace it.
-
-| `LOCAL_ACCEPT` | `TEXT_MIN_SHARED` | image6 full shown / correct | precision | coverage | image6 live precision | false accepts |
-|---|---|---|---|---|---|---|
-| 2.0 | 2 | 54 / 49 | 90.7 % | 76.6 % | 93.0 % | 60 (1.31 %) |
-| 2.0 | 4 | 52 / 48 | 92.3 % | 75.0 % | 97.4 % | 8 (0.17 %) |
-| 4.0 | 2 | 46 / 43 | 93.5 % | 67.2 % | 92.7 % | 55 (1.20 %) |
-| **4.0** | **4** | **44 / 42** | **95.5 %** | **65.6 %** | **97.3 %** | **3 (0.07 %)** |
-| 4.0 | 6 | 39 / 38 | 97.4 % | 59.4 % | 100 % | 0 (0.00 %) |
-| 5.0 | 6 | 36 / 36 | 100 % | 56.2 % | 100 % | 0 (0.00 %) |
-
-The shipped 4.0/4 row is the last one that passes G2 (>= 95 % precision) with
-coverage left; 4.0/6 and 5.0/6 also pass both gates but give up 6-9 points of
-coverage for precision the gate does not ask for. The old 2.0/2 line is 11
-points of coverage richer and fails G3 outright at 1.31 %. On the shipped
-evaluator 4.0/4 is 66 % end-to-end at 95 % precision with 2 false accepts,
-against 75 % / 92 % / 4 at the old 2.0 line - the coverage is what the precision
-costs, and G2 is the gate that says to pay it. At live resolution the same lines
-give 56 % end-to-end at 97 % precision with 1 false accept, so the calibration
-holds on the path the phone actually uses and not only at full resolution.
-
-Reproduce: `.venv/bin/python code/eval_distractors.py --stock stock-lopia-clean
---write data/external-shelves/wikimedia-commons data/external-shelves/wikimedia-commons-2`.
-The images are fetched by `code/collect_shelf_images.py` and are not committed;
-the manifests (source URL, licence, artist) and the results are.
-
-### External benchmark: 19 scored, 0 false accepts
-
-The image6 shelf is one photo with one label set, so every number above is
-measured on data the design was tuned against. Twenty images for eight SKUs
-were collected from independent sources (six retailer and marketplace domains)
-to test the pipeline on data it had no part in building. None is Tier 2; all
-are `external_proxy` and every manifest says so.
-
-Two leak checks run before any number is reported, both in
-`code/eval_external.py`:
-
-- Against the evaluation photo: the highest template match of any image against
-  `add/image6.jpg` is 0.675, so none is a crop of it.
-- Against the catalog: an image that has since been enrolled is refused rather
-  than scored. `sku_l06_1.jpg` is one - it is now the enrolled l06 view, so
-  scoring it would be training accuracy wearing a held-out label. It is
-  reported as `leaked`, not as a correct accept.
-
-Scored through the shipped `LocalLaneAdapter` + `DecisionPolicy` against
-`stock-lopia-clean` (`data/external-eval/*/eval_result.json`):
-
-| Set | Scored | Correct | Abstain | False accepts | Leaked |
-|---|---|---|---|---|---|
-| `kewpie/` | 11 | 8 | 3 | **0** | 1 |
-| `other-mayo/` | 8 | 4 | 4 | **0** | 0 |
-| **Total** | **19** | **12** | **7** | **0** | **1** |
-
-- **`sku_l07` is answered with no enrolled image.** Both its images accept
-  through the text lane alone - one reads ラッキー フヨネーズ, the other the
-  rear label's 製造者：丸和油脂株式会社 with 内容量：200g, which together
-  name the row 丸和油脂 ラッキーマヨネーズ 200g. This is the text lane's
-  coverage claim holding on data that was not used to build it. `sku_l02`
-  also accepts one image on text alone, but it does have an enrolled view.
-- **The l04 abstentions are correct behaviour.** l04 is also unenrolled and its
-  reads are weak: one reads only コと旨 タ 味 (every row scores 0.0), another
-  scores 0.379 against a 0.45 accept line. Abstaining on a weak read is the
-  designed outcome, not a miss.
-
-The seven abstentions are all unenrolled or weakly-read SKUs. No image was
-given a wrong name, which is the property the decision optimises for.
-
-### Tier-3 sources are staged, not satisfied
-
-Two reusable pan clips were found and saved (`data/external-eval/tier3-sources/`):
-a 1920x1080 CC BY 3.0 supermarket pan and a 360p CC BY mayonnaise-bay pan, with
-11 rejected candidates recorded. Neither gives per-SKU identity for this
-catalog, and the only LOPIA store walk found carries the standard YouTube
-licence and was therefore not downloaded. Tier 3 still needs the operator's own
-recorded walks.
-
-Still open, and not implementable from here: Tier 2 held-out labels (F-11), the
-remaining E-1/E-4..E-9 experiments, and the completion gate G1-G7, all of which
-need a second independent shelf visit.
-
-
-## Out of scope
-
-Detector recall and box quality (all experiments use the same boxes). Tag and
-planogram pipeline internals. Native UI. On-device performance. Learned local
-features (not installed; downloading them needs your approval). `image.jpg` /
-`stock/` (no labels exist). Timings from parallel runs are relative;
-`scale.py` timings are single-process on this Mac.
diff --git a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/.gitignore b/recognization/docs/research/2026-09-28-recognition-architecture/evidence/.gitignore
deleted file mode 100644
index 942c8b3..0000000
--- a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/.gitignore
+++ /dev/null
@@ -1,6 +0,0 @@
-# Leave-facing-out catalogs are built by lfo_setup.py, not committed.
-# cat_full holds crops of add/image6.jpg itself (template match 0.998+), so
-# committing it would put the evaluation photo back into the repository as a
-# catalog - the exact leak the audit removed from stock-lopia.
-cat_full/
-cat_pack/
diff --git a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/README.md b/recognization/docs/research/2026-09-28-recognition-architecture/evidence/README.md
deleted file mode 100644
index 1fb166d..0000000
--- a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/README.md
+++ /dev/null
@@ -1,53 +0,0 @@
-# Evidence scripts (2026-09-28 audit)
-
-Read-only against product code: every script imports from `code/` and writes
-only into this folder. Run from `recognization/` with `.venv/bin/python`.
-
-`eval/labels/image6.json` is the corrected image6 label set (26 of 111 v1 labels
-changed; every row carries `v1_label`, `source`, and a `note` where changed).
-`image6-v1-labels-overlay.jpg` shows the v1 labels on the shelf; green = catalog
-label, red = unknown. `sheet.py` regenerates per-SKU contact sheets.
-
-| Step | Command (prefix `E=docs/research/2026-09-28-recognition-architecture/evidence`) | Produces |
-|---|---|---|
-| Original matcher, both catalogs, full/live/live-with-unscaled-boxes | `.venv/bin/python $E/diag.py --stock stock-lopia-clean --labels $E/../../../eval/labels/image6.json [--live] [--bug] --out $E/run_stock-lopia-clean_<full\|live\|livebug>.pkl` (repeat with `--stock stock-lopia`) | `run_*.pkl` |
-| Score them under v1 or v2 labels | `.venv/bin/python $E/analyze.py <labels.json> $E/run_*.pkl` | identity top-1, argmax, current gate |
-| Matcher variants | `.venv/bin/python $E/variants.py --stock stock-lopia-clean --labels $E/../../../eval/labels/image6.json [--live] --scales 200,400 --root --geom affine --out $E/v_ms_root_aff_<full\|live>.pkl` (also `--tmpl-h 250/400`, no flags = raw) | `v_*.pkl` |
-| Variant ranking + open-set frontier | `.venv/bin/python $E/an2.py $E $(basename -a $E/v_*.pkl)` (`LABELS=image6_v2_lfo.json` for the leave-facing-out runs) | correct at 100/95/90 % precision |
-| Leave-facing-out catalog | `.venv/bin/python $E/lfo_setup.py`, then `variants.py --stock $E/cat_<pack\|full> --labels $E/image6_v2_lfo.json --scales 200,400 --root --geom affine [--live] --out $E/lfo_cat_<pack\|full>_<full\|live>.pkl` | `lfo_*.pkl` |
-| Unrelated-shelf distractors | `YOLO_OFFLINE=1 .venv/bin/python $E/distract.py stock-lopia-clean $E/cat_full` | `distract.pkl` |
-| Catalog growth, exhaustive vs descriptor index | `YOLO_OFFLINE=1 .venv/bin/python $E/scale.py` | stdout |
-| DINO retrieval under growth | `YOLO_OFFLINE=1 .venv/bin/python $E/dino_scale.py` | stdout |
-| OCR read-rate | `.venv/bin/python $E/ocr_exp.py` | `ocr.pkl` |
-| OCR signatures, then fusion | `.venv/bin/python $E/ocr_fuse.py && .venv/bin/python $E/fuse_eval.py` | `sig.pkl`, stdout |
-| Per-SKU contact sheets + the labelled shelf | `.venv/bin/python $E/sheet.py <outdir>` | `gt_<label>.jpg`, `shelf_labels.jpg` |
-
-Timings in DECISION.md come from `scale.py` (single process). The other
-scripts ran up to ten processes at once, so their ms figures are relative only.
-
-## Verified 2026-09-28
-
-Every command above was run from `recognization/` and produced its listed
-output. Two rows were wrong as written:
-
-- The table said `python`; the `python` on this machine's PATH has no `cv2`, so
-  every row failed on import. It is `.venv/bin/python`.
-- `an2.py` takes the folder and then file names *inside* it, so `v_*.pkl`
-  expanded in the working directory, where those files do not live. It is
-  `$(basename -a $E/v_*.pkl)`.
-
-`sheet.py` was missing from the table. It takes its output directory as a
-positional argument, not a flag: `sheet.py --help` reads `--help` as a
-directory name, writes nothing, and still exits 0. `analyze.py` and `an2.py`
-with no arguments raise `IndexError`; neither validates its own usage.
-
-v1 labels are no longer a file on disk — `eval/labels/image6.json` is the v2
-set. To score a run under v1, rebuild it from the `v1_label` field:
-
-    .venv/bin/python -c "import json;d=json.load(open('$E/../../../eval/labels/image6.json'));\
-    [f.update(label=f.pop('v1_label')) for f in d['facings']];\
-    json.dump(d,open('/tmp/image6_v1.json','w'),ensure_ascii=False)"
-
-That reconstruction reproduces DECISION.md's v1 numbers exactly: `analyze.py`
-gives ungated top-1 26/35 at both resolutions and the current gate 24/35 full,
-23/35 live (§2 row 15, F-01).
diff --git a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/an2.py b/recognization/docs/research/2026-09-28-recognition-architecture/evidence/an2.py
deleted file mode 100644
index 598fb29..0000000
--- a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/an2.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import pickle, json, sys, numpy as np, collections
-import os; S = sys.argv[1]; L = {f['index']: f['label'] for f in json.load(open(f'{S}/' + os.environ.get('LABELS','../../../../eval/labels/image6.json')))['facings']}
-RANK = {
- 'inl': lambda e: e['inliers'],
- 'inl*col': lambda e: e['inliers'] * (0.25 + e['color']),
-}
-def conf_feats(ranked):
-    (k1, e1), (k2, e2) = ranked[0], ranked[1]
-    i1, i2 = e1['inliers'], e2['inliers']
-    return {
-      'inl': i1,
-      'margin': i1 - i2,
-      'ratio': 1 - i2 / max(i1, 1),
-      'inl*ratio': i1 * (1 - i2 / max(i1, 1)),
-      'cover': e1['cover'],
-      'inl*ratio*qcov': i1 * (1 - i2 / max(i1, 1)) * np.sqrt(e1.get('qcover', 0)),
-      'inl*ratio*col': i1 * (1 - i2 / max(i1, 1)) * e1['color'],
-    }
-def evaluate(path, rank='inl'):
-    r = pickle.load(open(path, 'rb')); E = set(r['enrolled'])
-    rows = [x for x in r['rows'] if L[x['index']] != 'exclude']
-    n_in = sum(L[x['index']] != 'unknown' for x in rows); n_en = sum(L[x['index']] in E for x in rows)
-    recs = []; top1 = 0
-    for x in rows:
-        t = L[x['index']]
-        if not x['ev']: recs.append((t, None, {k: 0 for k in conf_feats([('a', dict(inliers=0,cover=0,color=0)),('b',dict(inliers=0,cover=0,color=0))])})); continue
-        ranked = sorted(x['ev'].items(), key=lambda kv: -RANK[rank](kv[1]))
-        b = ranked[0][0] if ranked[0][1]['inliers'] > 0 else None
-        if t in E and b == t: top1 += 1
-        recs.append((t, b, conf_feats(ranked)))
-    out = dict(top1=f'{top1}/{n_en}', argmax_e2e=f'{top1}/{n_in}')
-    for f in ['inl', 'margin', 'ratio', 'inl*ratio', 'inl*ratio*qcov', 'inl*ratio*col']:
-        pts = sorted(((c[f], t == b and b is not None) for t, b, c in recs if b), key=lambda p: -p[0])
-        best = {0.9: 0, 0.95: 0, 1.0: 0}; c = a = 0; i = 0
-        while i < len(pts):
-            j = i
-            while j < len(pts) and pts[j][0] == pts[i][0]: c += pts[j][1]; a += 1; j += 1
-            for p in best:
-                if c / a >= p: best[p] = max(best[p], c)
-            i = j
-        out[f] = '/'.join(str(best[p]) for p in (1.0, 0.95, 0.9))
-    return out
-if __name__ == '__main__':
-    print('feature cells = correct accepted at precision 100%/95%/90%  (enrolled in-catalog n=42, all in-catalog n=61, unknown n=48)')
-    for p in sys.argv[2:]:
-        for rk in ['inl', 'inl*col']:
-            print(p.split('/')[-1].replace('.pkl',''), rk, json.dumps(evaluate(f'{S}/{p}', rk)))
diff --git a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/analyze.py b/recognization/docs/research/2026-09-28-recognition-architecture/evidence/analyze.py
deleted file mode 100644
index a56d87f..0000000
--- a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/analyze.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import pickle, json, sys, collections, numpy as np
-sys.path.insert(0,'code')
-from identity import decide_identity
-def load_labels(p): return {f['index']: f['label'] for f in json.load(open(p))['facings']}
-def feats(sc):
-    r = sorted(sc.items(), key=lambda kv: -kv[1])
-    if not r: return None, 0.0, 0.0
-    top = r[0][1]; run = r[1][1] if len(r) > 1 else 0.0
-    return r[0][0], top, (top-run)/max(top,1.0)
-def analyze(run, labels, verbose=False):
-    E = set(run['enrolled']); rows = [x for x in run['rows'] if labels[x['index']] != 'exclude']
-    T = lambda x: labels[x['index']]
-    inc = [x for x in rows if T(x) != 'unknown']; ince = [x for x in inc if T(x) in E]
-    unk = [x for x in rows if T(x) == 'unknown']
-    # identity ceiling: ungated argmax on enrolled in-catalog
-    ranks = []
-    for x in ince:
-        order = [k for k, _ in sorted(x['scores'].items(), key=lambda kv: -kv[1])]
-        ranks.append(order.index(T(x))+1 if T(x) in order and x['scores'].get(T(x),0)>0 else 99)
-    top1 = sum(r == 1 for r in ranks)
-    # Van contract: argmax always
-    arg_correct = top1; answered = sum(1 for x in rows if x['scores'] and max(x['scores'].values())>0)
-    # current decision rule
-    acc = collections.Counter(); c = w = wu = wi = 0
-    for x in rows:
-        d = decide_identity(x['scores'], method='hybrid')
-        b = d['best_sku'] if d['identity_level']=='accepted' else None
-        if not b: continue
-        acc[b]+=1
-        if b == T(x): c += 1
-        else:
-            w += 1
-            if T(x)=='unknown': wu+=1
-            else: wi+=1
-    hub = max(acc.values())/sum(acc.values()) if acc else 0
-    out = dict(n=len(rows), in_catalog=len(inc), enrolled_in=len(ince), unknown=len(unk),
-        identity_top1=f"{top1}/{len(ince)}={top1/max(1,len(ince)):.0%}",
-        argmax_e2e=f"{arg_correct}/{len(inc)}={arg_correct/max(1,len(inc)):.0%}",
-        argmax_precision=f"{arg_correct}/{answered}={arg_correct/max(1,answered):.0%}",
-        gated=f"correct {c} wrong {w} (unk {wu}, in-cat {wi}) cov_enr {c/max(1,len(ince)):.0%} prec {c/max(1,c+w):.0%} hub {hub:.0%}",
-        ms=round(float(np.median([x['ms'] for x in run['rows'] if x['ms']>0] or [0])),1))
-    return out, ranks
-def sweep(run, labels, feature='margin'):
-    """Pareto of (correct accepted, precision) over a single threshold on a feature."""
-    E=set(run['enrolled']); rows=[x for x in run['rows'] if labels[x['index']]!='exclude']
-    pts=[]
-    for x in rows:
-        b,top,mar=feats(x['scores']); t=labels[x['index']]
-        v={'margin':mar,'top':top,'both':min(mar/0.3, top/40)}[feature]
-        pts.append((v, b==t and b is not None, b))
-    pts.sort(key=lambda p:-p[0]); res=[]; c=a=0
-    for v,ok,b in pts:
-        a+=1; c+=ok; res.append((v,c,a,c/a))
-    return res
-if __name__=='__main__':
-    labels=load_labels(sys.argv[1])
-    for p in sys.argv[2:]:
-        r=pickle.load(open(p,'rb')); o,ranks=analyze(r,labels)
-        print(p.split('/')[-1], json.dumps(o, ensure_ascii=False))
diff --git a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/diag.py b/recognization/docs/research/2026-09-28-recognition-architecture/evidence/diag.py
deleted file mode 100644
index 8c63e06..0000000
--- a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/diag.py
+++ /dev/null
@@ -1,41 +0,0 @@
-"""Diagnostic identity harness. Logs full per-facing score vectors so every
-ceiling (catalog, identity rank, decision) is separable. Read-only on product code."""
-import sys, os, json, time, argparse, pickle
-sys.path.insert(0, 'code')
-import cv2, numpy as np
-from feature_matcher import ProductFeatureMatcher
-
-def sku_of(name):
-    p = name.split('_'); return '_'.join(p[:2])
-
-def crops(image_path, labels, live, bug=False, long_side=1280):
-    im = cv2.imread(image_path); f = 1.0
-    if live:
-        h, w = im.shape[:2]; f = long_side / max(h, w)
-        im = cv2.resize(im, (int(w*f), int(h*f)), interpolation=cv2.INTER_AREA)
-    out = []
-    for fc in labels['facings']:
-        b = fc['box'] if bug else [int(round(v*f)) for v in fc['box']]
-        x1, y1, x2, y2 = b
-        out.append((fc, im[max(0,y1):y2, max(0,x1):x2]))
-    return out
-
-def run(stock, image, labels, live, bug, method):
-    m = ProductFeatureMatcher(stock_dir=stock)
-    rows = []
-    for fc, c in crops(image, labels, live, bug):
-        if c.size == 0 or min(c.shape[:2]) < 8: rows.append(dict(index=fc["index"], truth=fc["label"], shape=c.shape[:2], scores={}, templ={}, sub={}, ms=0.0)); continue
-        t = time.perf_counter(); s, det = m.score_crop(c, method=method); dt = time.perf_counter()-t
-        g = {}
-        for k, v in s.items(): g[sku_of(k)] = max(g.get(sku_of(k), 0.0), v)
-        sub = {k: det[k].get('sub_details') for k in det}
-        rows.append(dict(index=fc['index'], truth=fc['label'], shape=c.shape[:2], scores=g, templ=s, sub=sub, ms=dt*1000))
-    return dict(stock=stock, live=live, bug=bug, method=method, enrolled=sorted({sku_of(k) for k in m.db.templates}), rows=rows)
-
-if __name__ == '__main__':
-    ap = argparse.ArgumentParser(); ap.add_argument('--stock'); ap.add_argument('--labels'); ap.add_argument('--live', action='store_true')
-    ap.add_argument('--bug', action='store_true'); ap.add_argument('--method', default='hybrid'); ap.add_argument('--out')
-    a = ap.parse_args(); L = json.load(open(a.labels))
-    r = run(a.stock, 'add/image6.jpg', L, a.live, a.bug, a.method)
-    pickle.dump(r, open(a.out, 'wb'))
-    print(a.out, len(r['rows']), 'rows', 'median ms', round(float(np.median([x['ms'] for x in r['rows']])),1))
diff --git a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/dino_scale.py b/recognization/docs/research/2026-09-28-recognition-architecture/evidence/dino_scale.py
deleted file mode 100644
index 64d7ae4..0000000
--- a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/dino_scale.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import sys,os,glob,json,time,numpy as np,cv2,random
-sys.path.insert(0,'code'); sys.path.insert(0,os.path.dirname(__file__)); os.environ['YOLO_OFFLINE']='1'
-from gallery import ProductGallery
-from detector import ShelfProductDetector
-from diag import crops, sku_of
-from PIL import Image
-S=os.path.dirname(__file__); random.seed(0)
-g=ProductGallery('stock-lopia-clean',['sku_l01','sku_l02','sku_l03','sku_l05','sku_l06','sku_l09','sku_l11']); assert g.load()
-refs=[(sku_of(os.path.basename(p)[:-4]),g.embed(cv2.imread(p))) for p in sorted(glob.glob('stock-lopia-clean/*.jpg'))]
-det=ShelfProductDetector(); dis=[]
-for p in sorted(glob.glob('add/*')):
-    if 'image6' in p: continue
-    im=cv2.cvtColor(np.array(Image.open(p).convert('RGB')),cv2.COLOR_RGB2BGR)
-    for j,b in enumerate(det.detect(im).get('boxes',[])):
-        x1,y1,x2,y2=[int(v) for v in b['box']]; c=im[max(0,y1):y2,max(0,x1):x2]
-        if min(c.shape[:2])>=40: dis.append((f'd{j}_{os.path.basename(p)}',c))
-random.shuffle(dis)
-D=[(k,g.embed(c)) for k,c in dis]
-L=json.load(open(f'{S}/../../../../eval/labels/image6.json')); E={k for k,_ in refs}
-Q=[(fc['label'],g.embed(c)) for fc,c in crops('add/image6.jpg',L,True) if fc['label'] in E]
-t=time.perf_counter(); _=g.embed(Q and dis[0][1]); print('embed ms',round((time.perf_counter()-t)*1000,1))
-for N in [0,50,200,len(D)]:
-    R=refs+D[:N]; M=np.stack([e for _,e in R]); names=[k for k,_ in R]
-    rec={1:0,5:0,10:0,20:0}
-    for truth,q in Q:
-        s=M@q; order=[]
-        for i in np.argsort(-s):
-            if names[i] not in order: order.append(names[i])
-        for K in rec: rec[K]+= truth in order[:K]
-    print(f'skus={len(set(names)):4d} DINO recall@1/5/10/20 '+'/'.join(str(rec[K]) for K in rec)+f' of {len(Q)}')
diff --git a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/distract.py b/recognization/docs/research/2026-09-28-recognition-architecture/evidence/distract.py
deleted file mode 100644
index 5fd4b19..0000000
--- a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/distract.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import sys,os,glob,pickle,numpy as np,cv2,json
-sys.path.insert(0,'code'); sys.path.insert(0,os.path.dirname(__file__))
-os.environ['YOLO_OFFLINE']='1'
-from detector import ShelfProductDetector
-from variants import Matcher
-from PIL import Image
-det=ShelfProductDetector()
-res={}
-for cat in sys.argv[1:]:
-    name=os.path.basename(cat); m=Matcher(cat,root=True,geom='affine',scales=(200,400)); rows=[]
-    for p in sorted(glob.glob('add/*')):
-        if 'image6' in p: continue
-        im=cv2.cvtColor(np.array(Image.open(p).convert('RGB')),cv2.COLOR_RGB2BGR)
-        for b in det.detect(im).get('boxes',[]):
-            x1,y1,x2,y2=[int(v) for v in b['box']]; c=im[max(0,y1):y2,max(0,x1):x2]
-            if c.size==0 or min(c.shape[:2])<20: continue
-            rows.append(dict(img=p,ev=m.score(c)))
-    res[name]=rows; print(name,len(rows),'distractor crops')
-pickle.dump(res,open(f'{os.path.dirname(__file__)}/distract.pkl','wb'))
diff --git a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/fuse_eval.py b/recognization/docs/research/2026-09-28-recognition-architecture/evidence/fuse_eval.py
deleted file mode 100644
index 360989a..0000000
--- a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/fuse_eval.py
+++ /dev/null
@@ -1,57 +0,0 @@
-import sys,os,json,pickle,csv,numpy as np
-S=os.path.dirname(os.path.abspath(__file__)); sys.path.insert(0,S)
-from ocr_fuse import norm,sim  # noqa (module runs OCR of packshots on import; cheap)
-sig=pickle.load(open(f'{S}/sig.pkl','rb')); O=pickle.load(open(f'{S}/ocr.pkl','rb'))
-L={f['index']:f['label'] for f in json.load(open(f'{S}/../../../../eval/labels/image6.json'))['facings']}
-def conf(ev):
-    r=sorted(ev.items(),key=lambda kv:-kv[1]['inliers']*(0.25+kv[1]['color']))
-    (k1,e1),(k2,e2)=r[0],r[1]; i1,i2=e1['inliers'],e2['inliers']
-    return k1, i1*(1-i2/max(i1,1))*e1['color']
-for res,up,run in [('full',1,'v_ms_root_aff_full.pkl'),('live',2,'v_ms_root_aff_live.pkl')]:
-    ocr={r['index']:r['text'] for r in O[res] if r['up']==up}
-    M={x['index']:x for x in pickle.load(open(f'{S}/{run}','rb'))['rows']}
-    n_in=sum(1 for t in L.values() if t not in ('unknown','exclude'))
-    # OCR-alone identity among ALL catalog SKUs
-    ok=tot=0
-    for i,t in L.items():
-        if t in ('unknown','exclude') or i not in ocr: continue
-        tx=ocr[i]; 
-        if len(norm(tx))<4: continue
-        s={k:sim(tx,v) for k,v in sig.items()}; tot+=1; ok+= max(s,key=s.get)==t
-    print(f'== {res}: OCR-alone top-1 over all 12 catalog SKUs, on facings with text: {ok}/{tot}')
-    for delta in [None,0.0,0.1,0.2]:
-        line=[]
-        for tau in [0.5,1,2,3]:
-            c=a=0
-            for i,t in L.items():
-                if t=='exclude' or i not in M or not M[i]['ev']: continue
-                k,cf=conf(M[i]['ev'])
-                if cf<tau: continue
-                if delta is not None and len(norm(ocr.get(i,'')))>=4:
-                    s={kk:sim(ocr[i],v) for kk,v in sig.items()}; j=max(s,key=s.get)
-                    if j!=k and s[j]-s[k]>delta: continue   # text names a different catalog product: veto
-                a+=1; c+= k==t
-            line.append(f'tau{tau}: {c}/{n_in} p={c/max(a,1):.0%}')
-        print(' veto' if delta is not None else ' matcher only', delta, ' | '.join(line))
-print('== union: matcher (veto) OR OCR-only identity')
-for res,up,run in [('full',1,'v_ms_root_aff_full.pkl'),('live',2,'v_ms_root_aff_live.pkl')]:
-    ocr={r['index']:r['text'] for r in O[res] if r['up']==up}
-    M={x['index']:x for x in pickle.load(open(f'{S}/{run}','rb'))['rows']}
-    n_in=sum(1 for t in L.values() if t not in ('unknown','exclude'))
-    for tau in [0.5,1,2]:
-        for sg,mu in [(0.5,0.15),(0.6,0.2),(0.7,0.25)]:
-            c=a=0; fa_unk=0; bysrc={'m':0,'o':0}
-            for i,t in L.items():
-                if t=='exclude' or i not in M: continue
-                tx=ocr.get(i,''); s={kk:sim(tx,v) for kk,v in sig.items()} if len(norm(tx))>=4 else {}
-                j=max(s,key=s.get) if s else None
-                ans=None
-                if M[i]['ev']:
-                    k,cf=conf(M[i]['ev'])
-                    if cf>=tau and not (j and j!=k and s[j]-s[k]>0): ans=k; src='m'
-                if ans is None and s:
-                    r=sorted(s.values(),reverse=True)
-                    if r[0]>=sg and r[0]-r[1]>=mu: ans=j; src='o'
-                if ans:
-                    a+=1; c+= ans==t; bysrc[src]+= ans==t; fa_unk+= t=='unknown'
-            print(f' {res} tau{tau} ocr>={sg} m{mu}: correct {c}/{n_in} ({c/n_in:.0%}) precision {c/max(a,1):.0%} accepted-unknown {fa_unk}/48 from {bysrc}')
diff --git a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/image6-v1-labels-overlay.jpg b/recognization/docs/research/2026-09-28-recognition-architecture/evidence/image6-v1-labels-overlay.jpg
deleted file mode 100644
index c00ffca..0000000
Binary files a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/image6-v1-labels-overlay.jpg and /dev/null differ
diff --git a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/image6_v2_lfo.json b/recognization/docs/research/2026-09-28-recognition-architecture/evidence/image6_v2_lfo.json
deleted file mode 100644
index bd5677d..0000000
--- a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/image6_v2_lfo.json
+++ /dev/null
@@ -1 +0,0 @@
-{"image": "add/image6.jpg", "facings": [{"box": [39, 56, 143, 313], "label": "sku_l01", "match": 1.0, "index": 0, "source": "v1-confirmed-visual", "v1_label": "sku_l01"}, {"box": [147, 53, 264, 318], "label": "exclude", "match": 1.0, "index": 1, "source": "v1-confirmed-visual", "v1_label": "sku_l01"}, {"box": [1419, 0, 1500, 107], "label": "exclude", "match": 1.0, "index": 2, "note": "blurred corner of upper shelf; not identifiable", "source": "visual-audit-2026-09-28", "v1_label": "sku_l04"}, {"box": [270, 64, 377, 322], "label": "sku_l01", "match": 1.0, "index": 3, "source": "v1-confirmed-visual", "v1_label": "sku_l01"}, {"box": [382, 72, 467, 324], "label": "sku_l01", "match": 0.0, "index": 4, "note": "Egg Care front, same tag run as #0-3", "source": "visual-audit-2026-09-28", "v1_label": "unknown"}, {"box": [472, 64, 570, 328], "label": "sku_l01", "match": 0.0, "index": 5, "note": "Egg Care front", "source": "visual-audit-2026-09-28", "v1_label": "unknown"}, {"box": [575, 137, 639, 330], "label": "sku_l02", "match": 1.0, "index": 6, "source": "v1-confirmed-visual", "v1_label": "sku_l02"}, {"box": [641, 126, 732, 337], "label": "exclude", "match": 1.0, "index": 7, "source": "v1-confirmed-visual", "v1_label": "sku_l02"}, {"box": [736, 123, 817, 339], "label": "sku_l02", "match": 1.0, "index": 8, "source": "v1-confirmed-visual", "v1_label": "sku_l02"}, {"box": [822, 135, 909, 345], "label": "sku_l02", "match": 1.0, "index": 9, "source": "v1-confirmed-visual", "v1_label": "sku_l02"}, {"box": [959, 160, 1044, 349], "label": "sku_l03", "match": 1.0, "index": 10, "source": "v1-confirmed-visual", "v1_label": "sku_l03"}, {"box": [1049, 158, 1138, 348], "label": "sku_l03", "match": 1.0, "index": 11, "source": "v1-confirmed-visual", "v1_label": "sku_l03"}, {"box": [1143, 144, 1228, 353], "label": "sku_l03", "match": 1.0, "index": 12, "source": "v1-confirmed-visual", "v1_label": "sku_l03"}, {"box": [1233, 147, 1352, 356], "label": "exclude", "match": 1.0, "index": 13, "source": "v1-confirmed-visual", "v1_label": "sku_l03"}, {"box": [1375, 152, 1475, 365], "label": "sku_l04", "match": 1.0, "index": 14, "source": "v1-confirmed-visual", "v1_label": "sku_l04"}, {"box": [1482, 155, 1500, 363], "label": "exclude", "match": 0.0, "index": 15, "note": "18 px sliver", "source": "visual-audit-2026-09-28", "v1_label": "unknown"}, {"box": [0, 472, 76, 662], "label": "unknown", "match": 1.0, "index": 16, "note": "garlic jar, not mayo", "source": "visual-audit-2026-09-28", "v1_label": "sku_l05"}, {"box": [86, 420, 163, 664], "label": "sku_l05", "match": 1.0, "index": 17, "source": "v1-confirmed-visual", "v1_label": "sku_l05"}, {"box": [167, 420, 265, 662], "label": "exclude", "match": 1.0, "index": 18, "source": "v1-confirmed-visual", "v1_label": "sku_l05"}, {"box": [270, 426, 366, 664], "label": "sku_l05", "match": 1.0, "index": 19, "source": "v1-confirmed-visual", "v1_label": "sku_l05"}, {"box": [476, 446, 571, 669], "label": "exclude", "match": 1.0, "index": 20, "source": "v1-confirmed-visual", "v1_label": "sku_l06"}, {"box": [583, 458, 678, 672], "label": "sku_l06", "match": 1.0, "index": 21, "source": "v1-confirmed-visual", "v1_label": "sku_l06"}, {"box": [683, 454, 774, 672], "label": "sku_l06", "match": 0.0, "index": 22, "note": "Fitte front", "source": "visual-audit-2026-09-28", "v1_label": "unknown"}, {"box": [778, 459, 865, 675], "label": "sku_l06", "match": 0.0, "index": 23, "note": "Fitte front", "source": "visual-audit-2026-09-28", "v1_label": "unknown"}, {"box": [871, 466, 945, 674], "label": "sku_l07", "match": 1.0, "index": 24, "source": "v1-confirmed-visual", "v1_label": "sku_l07"}, {"box": [949, 471, 1020, 674], "label": "sku_l07", "match": 1.0, "index": 25, "source": "v1-confirmed-visual", "v1_label": "sku_l07"}, {"box": [1024, 493, 1098, 676], "label": "sku_l07", "match": 1.0, "index": 26, "source": "v1-confirmed-visual", "v1_label": "sku_l07"}, {"box": [1103, 494, 1176, 676], "label": "sku_l07", "match": 0.0, "index": 27, "note": "Lucky Mayonnaise front", "source": "visual-audit-2026-09-28", "v1_label": "unknown"}, {"box": [1181, 499, 1245, 676], "label": "sku_l07", "match": 0.0, "index": 28, "note": "Lucky Mayonnaise front", "source": "visual-audit-2026-09-28", "v1_label": "unknown"}, {"box": [1249, 480, 1331, 676], "label": "exclude", "match": 1.0, "index": 29, "note": "Lucky Mayonnaise front; was l08", "source": "visual-audit-2026-09-28", "v1_label": "sku_l08"}, {"box": [1336, 502, 1421, 682], "label": "sku_l08", "match": 1.0, "index": 30, "source": "v1-confirmed-visual", "v1_label": "sku_l08"}, {"box": [0, 751, 108, 977], "label": "unknown", "match": 1.0, "index": 31, "note": "demi-glace sauce pouch; was l09", "source": "visual-audit-2026-09-28", "v1_label": "sku_l09"}, {"box": [115, 758, 193, 973], "label": "unknown", "match": 1.0, "index": 32, "note": "demi-glace sauce pouch; was l09", "source": "visual-audit-2026-09-28", "v1_label": "sku_l09"}, {"box": [199, 758, 255, 971], "label": "sku_l09", "match": 1.0, "index": 33, "source": "v1-confirmed-visual", "v1_label": "sku_l09"}, {"box": [258, 758, 352, 976], "label": "sku_l09", "match": 1.0, "index": 34, "source": "v1-confirmed-visual", "v1_label": "sku_l09"}, {"box": [357, 761, 449, 973], "label": "sku_l09", "match": 1.0, "index": 35, "source": "v1-confirmed-visual", "v1_label": "sku_l09"}, {"box": [452, 755, 514, 972], "label": "sku_l09", "match": 1.0, "index": 36, "source": "v1-confirmed-visual", "v1_label": "sku_l09"}, {"box": [519, 759, 608, 974], "label": "sku_l09", "match": 1.0, "index": 37, "source": "v1-confirmed-visual", "v1_label": "sku_l09"}, {"box": [614, 759, 700, 973], "label": "sku_l09", "match": 1.0, "index": 38, "source": "v1-confirmed-visual", "v1_label": "sku_l09"}, {"box": [704, 763, 791, 973], "label": "sku_l09", "match": 1.0, "index": 39, "source": "v1-confirmed-visual", "v1_label": "sku_l09"}, {"box": [798, 765, 916, 974], "label": "exclude", "match": 0.0, "index": 40, "note": "Kokuuma 65% front", "source": "visual-audit-2026-09-28", "v1_label": "unknown"}, {"box": [919, 763, 973, 974], "label": "sku_l09", "match": 0.0, "index": 41, "note": "Kokuuma 65% partly occluded", "source": "visual-audit-2026-09-28", "v1_label": "unknown"}, {"box": [977, 765, 1067, 974], "label": "sku_l09", "match": 0.0, "index": 42, "note": "Kokuuma 65% front", "source": "visual-audit-2026-09-28", "v1_label": "unknown"}, {"box": [1070, 762, 1134, 975], "label": "sku_l09", "match": 0.0, "index": 43, "note": "Kokuuma 65% partly occluded", "source": "visual-audit-2026-09-28", "v1_label": "unknown"}, {"box": [1138, 767, 1218, 973], "label": "sku_l09", "match": 0.0, "index": 44, "note": "Kokuuma 65% front", "source": "visual-audit-2026-09-28", "v1_label": "unknown"}, {"box": [1221, 768, 1281, 972], "label": "sku_l09", "match": 0.0, "index": 45, "note": "Kokuuma 65% partly occluded", "source": "visual-audit-2026-09-28", "v1_label": "unknown"}, {"box": [1285, 794, 1359, 972], "label": "unknown", "match": 0.0, "index": 46, "note": "ユーラス 野菜をおいしく食べる旨たれ 380g; possibly what stock.csv calls l12", "source": "visual-audit-2026-09-28", "v1_label": "unknown"}, {"box": [1363, 794, 1440, 973], "label": "unknown", "match": 0.0, "index": 47, "note": "same tare bottle", "source": "visual-audit-2026-09-28", "v1_label": "unknown"}, {"box": [1443, 794, 1500, 973], "label": "unknown", "match": 0.0, "index": 48, "note": "same tare bottle, partial", "source": "visual-audit-2026-09-28", "v1_label": "unknown"}, {"box": [191, 1060, 297, 1266], "label": "sku_l10", "match": 1.0, "index": 49, "source": "v1-confirmed-visual", "v1_label": "sku_l10"}, {"box": [306, 1058, 436, 1260], "label": "exclude", "match": 1.0, "index": 50, "source": "v1-confirmed-visual", "v1_label": "sku_l10"}, {"box": [441, 1060, 538, 1256], "label": "sku_l10", "match": 1.0, "index": 51, "source": "v1-confirmed-visual", "v1_label": "sku_l10"}, {"box": [542, 1060, 628, 1251], "label": "sku_l10", "match": 1.0, "index": 52, "source": "v1-confirmed-visual", "v1_label": "sku_l10"}, {"box": [630, 1055, 680, 1249], "label": "sku_l10", "match": 1.0, "index": 53, "source": "v1-confirmed-visual", "v1_label": "sku_l10"}, {"box": [685, 1058, 783, 1251], "label": "sku_l10", "match": 1.0, "index": 54, "source": "v1-confirmed-visual", "v1_label": "sku_l10"}, {"box": [789, 1056, 876, 1246], "label": "sku_l10", "match": 1.0, "index": 55, "source": "v1-confirmed-visual", "v1_label": "sku_l10"}, {"box": [878, 1055, 968, 1246], "label": "sku_l10", "match": 1.0, "index": 56, "source": "v1-confirmed-visual", "v1_label": "sku_l10"}, {"box": [1044, 1054, 1113, 1248], "label": "sku_l10", "match": 1.0, "index": 57, "source": "v1-confirmed-visual", "v1_label": "sku_l10"}, {"box": [1114, 1051, 1196, 1247], "label": "sku_l10", "match": 1.0, "index": 58, "source": "v1-confirmed-visual", "v1_label": "sku_l10"}, {"box": [1199, 1050, 1280, 1247], "label": "sku_l10", "match": 0.0, "index": 59, "note": "Pure Select mayonnaise front", "source": "visual-audit-2026-09-28", "v1_label": "unknown"}, {"box": [1280, 1061, 1317, 1244], "label": "unknown", "match": 0.0, "index": 60, "source": "v1-confirmed-visual", "v1_label": "unknown"}, {"box": [1319, 1068, 1389, 1240], "label": "unknown", "match": 0.0, "index": 61, "source": "v1-confirmed-visual", "v1_label": "unknown"}, {"box": [1393, 1067, 1467, 1241], "label": "unknown", "match": 0.0, "index": 62, "source": "v1-confirmed-visual", "v1_label": "unknown"}, {"box": [1478, 1068, 1500, 1242], "label": "unknown", "match": 0.0, "index": 63, "source": "v1-confirmed-visual", "v1_label": "unknown"}, {"box": [64, 1142, 150, 1264], "label": "unknown", "match": 1.0, "index": 64, "note": "Heinz can; was l10", "source": "visual-audit-2026-09-28", "v1_label": "sku_l10"}, {"box": [0, 1379, 51, 1561], "label": "unknown", "match": 0.0, "index": 65, "source": "v1-confirmed-visual", "v1_label": "unknown"}, {"box": [60, 1374, 163, 1560], "label": "unknown", "match": 0.0, "index": 66, "source": "v1-confirmed-visual", "v1_label": "unknown"}, {"box": [190, 1339, 296, 1562], "label": "exclude", "match": 1.0, "index": 67, "source": "v1-confirmed-visual", "v1_label": "sku_l11"}, {"box": [298, 1335, 404, 1558], "label": "sku_l11", "match": 1.0, "index": 68, "source": "v1-confirmed-visual", "v1_label": "sku_l11"}, {"box": [421, 1333, 524, 1549], "label": "sku_l11", "match": 1.0, "index": 69, "source": "v1-confirmed-visual", "v1_label": "sku_l11"}, {"box": [529, 1332, 626, 1546], "label": "sku_l11", "match": 1.0, "index": 70, "source": "v1-confirmed-visual", "v1_label": "sku_l11"}, {"box": [634, 1328, 729, 1541], "label": "sku_l11", "match": 1.0, "index": 71, "source": "v1-confirmed-visual", "v1_label": "sku_l11"}, {"box": [734, 1326, 831, 1538], "label": "sku_l11", "match": 1.0, "index": 72, "source": "v1-confirmed-visual", "v1_label": "sku_l11"}, {"box": [836, 1326, 924, 1535], "label": "sku_l11", "match": 1.0, "index": 73, "source": "v1-confirmed-visual", "v1_label": "sku_l11"}, {"box": [930, 1324, 1018, 1532], "label": "sku_l11", "match": 1.0, "index": 74, "source": "v1-confirmed-visual", "v1_label": "sku_l11"}, {"box": [1022, 1320, 1123, 1530], "label": "sku_l11", "match": 1.0, "index": 75, "note": "Kewpie Half front; was l12", "source": "visual-audit-2026-09-28", "v1_label": "sku_l12"}, {"box": [1190, 1331, 1262, 1521], "label": "unknown", "match": 1.0, "index": 76, "note": "Vegiglet dressing; was l12", "source": "visual-audit-2026-09-28", "v1_label": "sku_l12"}, {"box": [1266, 1337, 1334, 1516], "label": "unknown", "match": 1.0, "index": 77, "note": "Vegiglet dressing; was l12", "source": "visual-audit-2026-09-28", "v1_label": "sku_l12"}, {"box": [1337, 1335, 1403, 1514], "label": "unknown", "match": 1.0, "index": 78, "note": "Vegiglet dressing; was l12", "source": "visual-audit-2026-09-28", "v1_label": "sku_l12"}, {"box": [1409, 1336, 1475, 1511], "label": "unknown", "match": 1.0, "index": 79, "note": "Vegiglet dressing; was l12", "source": "visual-audit-2026-09-28", "v1_label": "sku_l12"}, {"box": [1480, 1327, 1500, 1514], "label": "unknown", "match": 0.0, "index": 80, "source": "v1-confirmed-visual", "v1_label": "unknown"}, {"box": [593, 1619, 679, 1815], "label": "unknown", "match": 0.0, "index": 81, "source": "v1-confirmed-visual", "v1_label": "unknown"}, {"box": [683, 1617, 762, 1809], "label": "unknown", "match": 0.0, "index": 82, "source": "v1-confirmed-visual", "v1_label": "unknown"}, {"box": [766, 1612, 839, 1806], "label": "unknown", "match": 0.0, "index": 83, "source": "v1-confirmed-visual", "v1_label": "unknown"}, {"box": [842, 1609, 909, 1801], "label": "unknown", "match": 0.0, "index": 84, "source": "v1-confirmed-visual", "v1_label": "unknown"}, {"box": [910, 1607, 995, 1796], "label": "unknown", "match": 0.911, "index": 85, "note": "Kewpie Mayonnaise 450g (not catalog); was l03", "source": "visual-audit-2026-09-28", "v1_label": "sku_l03"}, {"box": [999, 1602, 1078, 1793], "label": "unknown", "match": 0.0, "index": 86, "source": "v1-confirmed-visual", "v1_label": "unknown"}, {"box": [1083, 1600, 1169, 1787], "label": "unknown", "match": 0.0, "index": 87, "source": "v1-confirmed-visual", "v1_label": "unknown"}, {"box": [1173, 1595, 1269, 1783], "label": "unknown", "match": 0.0, "index": 88, "source": "v1-confirmed-visual", "v1_label": "unknown"}, {"box": [1306, 1586, 1388, 1771], "label": "unknown", "match": 0.0, "index": 89, "source": "v1-confirmed-visual", "v1_label": "unknown"}, {"box": [1390, 1602, 1457, 1767], "label": "unknown", "match": 0.0, "index": 90, "source": "v1-confirmed-visual", "v1_label": "unknown"}, {"box": [1461, 1601, 1499, 1769], "label": "unknown", "match": 0.0, "index": 91, "source": "v1-confirmed-visual", "v1_label": "unknown"}, {"box": [7, 1632, 103, 1845], "label": "unknown", "match": 0.0, "index": 92, "source": "v1-confirmed-visual", "v1_label": "unknown"}, {"box": [107, 1630, 199, 1844], "label": "unknown", "match": 0.0, "index": 93, "source": "v1-confirmed-visual", "v1_label": "unknown"}, {"box": [213, 1631, 313, 1834], "label": "unknown", "match": 0.0, "index": 94, "source": "v1-confirmed-visual", "v1_label": "unknown"}, {"box": [318, 1628, 405, 1829], "label": "unknown", "match": 0.0, "index": 95, "source": "v1-confirmed-visual", "v1_label": "unknown"}, {"box": [409, 1626, 491, 1825], "label": "unknown", "match": 0.0, "index": 96, "source": "v1-confirmed-visual", "v1_label": "unknown"}, {"box": [494, 1622, 589, 1821], "label": "unknown", "match": 0.0, "index": 97, "source": "v1-confirmed-visual", "v1_label": "unknown"}, {"box": [1059, 1855, 1197, 2000], "label": "unknown", "match": 0.0, "index": 98, "source": "v1-confirmed-visual", "v1_label": "unknown"}, {"box": [1216, 1839, 1309, 2000], "label": "unknown", "match": 0.0, "index": 99, "source": "v1-confirmed-visual", "v1_label": "unknown"}, {"box": [1313, 1835, 1397, 1999], "label": "unknown", "match": 0.0, "index": 100, "source": "v1-confirmed-visual", "v1_label": "unknown"}, {"box": [1404, 1837, 1498, 2000], "label": "unknown", "match": 0.0, "index": 101, "source": "v1-confirmed-visual", "v1_label": "unknown"}, {"box": [0, 1918, 75, 1998], "label": "unknown", "match": 0.0, "index": 102, "source": "v1-confirmed-visual", "v1_label": "unknown"}, {"box": [82, 1910, 187, 1999], "label": "unknown", "match": 0.0, "index": 103, "source": "v1-confirmed-visual", "v1_label": "unknown"}, {"box": [214, 1897, 506, 1998], "label": "unknown", "match": 0.0, "index": 104, "source": "v1-confirmed-visual", "v1_label": "unknown"}, {"box": [219, 1906, 362, 2000], "label": "unknown", "match": 0.0, "index": 105, "source": "v1-confirmed-visual", "v1_label": "unknown"}, {"box": [363, 1911, 500, 1999], "label": "unknown", "match": 0.0, "index": 106, "source": "v1-confirmed-visual", "v1_label": "unknown"}, {"box": [516, 1882, 591, 2000], "label": "unknown", "match": 0.0, "index": 107, "source": "v1-confirmed-visual", "v1_label": "unknown"}, {"box": [646, 1886, 786, 2000], "label": "unknown", "match": 0.0, "index": 108, "source": "v1-confirmed-visual", "v1_label": "unknown"}, {"box": [790, 1882, 887, 2000], "label": "unknown", "match": 0.0, "index": 109, "source": "v1-confirmed-visual", "v1_label": "unknown"}, {"box": [895, 1862, 1044, 2000], "label": "unknown", "match": 0.0, "index": 110, "source": "v1-confirmed-visual", "v1_label": "unknown"}], "version": 2}
\ No newline at end of file
diff --git a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/lfo_setup.py b/recognization/docs/research/2026-09-28-recognition-architecture/evidence/lfo_setup.py
deleted file mode 100644
index 78c4bd5..0000000
--- a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/lfo_setup.py
+++ /dev/null
@@ -1,22 +0,0 @@
-"""Build the leave-facing-out catalogs: cat_pack (packshots only) and cat_full
-(packshots + the widest facing of each in-catalog SKU as a shelf view), and
-image6_v2_lfo.json with those reference facings excluded from scoring.
-Same-photo references make this an optimistic proxy, not a held-out result."""
-import json, cv2, os, shutil, collections
-E = os.path.dirname(os.path.abspath(__file__))
-L = json.load(open(f'{E}/../../../../eval/labels/image6.json')); im = cv2.imread('add/image6.jpg')
-by = collections.defaultdict(list)
-for f in L['facings']:
-    if f['label'] not in ('unknown', 'exclude'): by[f['label']].append(f)
-refs = {k: max(fs, key=lambda f: f['box'][2] - f['box'][0])['index'] for k, fs in by.items() if len(fs) >= 2}
-for cat in ('cat_pack', 'cat_full'):
-    d = f'{E}/{cat}'; shutil.rmtree(d, ignore_errors=True); os.makedirs(d)
-    for p in os.listdir('stock-lopia-clean'):
-        if p.endswith('.jpg'): shutil.copy(f'stock-lopia-clean/{p}', d)
-    if cat == 'cat_full':
-        for k, i in refs.items():
-            x1, y1, x2, y2 = L['facings'][i]['box']; cv2.imwrite(f'{d}/{k}_shelf.jpg', im[y1:y2, x1:x2])
-for f in L['facings']:
-    if f['index'] in refs.values(): f['label'] = 'exclude'
-json.dump(L, open(f'{E}/image6_v2_lfo.json', 'w'), ensure_ascii=False)
-print('shelf references', refs)
diff --git a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/ocr_exp.py b/recognization/docs/research/2026-09-28-recognition-architecture/evidence/ocr_exp.py
deleted file mode 100644
index 4469929..0000000
--- a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/ocr_exp.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import sys,os,json,pickle,numpy as np,cv2,time
-sys.path.insert(0,'code'); sys.path.insert(0,os.path.dirname(__file__))
-from ocr import VisionOcr
-from diag import crops
-S=os.path.dirname(__file__)
-L=json.load(open(f'{S}/../../../../eval/labels/image6.json'))
-# distinguishing tokens per SKU (any hit counts); includes siblings for near-unknowns
-KEY={'sku_l01':['エッグケア','卵を使って'],'sku_l02':['アマニ'],'sku_l03':['からし','Spicy'],'sku_l04':['めんたい'],
-'sku_l05':['ゼロ','ノンコレ'],'sku_l06':['フィッテ','内臓脂肪'],'sku_l07':['ラッキー'],'sku_l08':['PERI','ペリ'],
-'sku_l09':['コクうま','65'],'sku_l10':['ピュアセレクト','マヨネーズ'],'sku_l11':['ハーフ','1/2'],'kewpie450':['KEWPIE','マヨネーズ']}
-o=VisionOcr(languages=['ja-JP','en-US']); out={}
-for res in ['full','live']:
-    rows=[]
-    for fc,c in crops('add/image6.jpg',L,res=='live'):
-        if fc['label']=='exclude' or c.size==0 or min(c.shape[:2])<8: continue
-        for up in [1,2]:
-            cc=cv2.resize(c,None,fx=up,fy=up,interpolation=cv2.INTER_CUBIC) if up>1 else c
-            t=time.perf_counter(); r=o.read(cc); ms=(time.perf_counter()-t)*1000
-            txt=' '.join(l.text for l in r.lines)
-            rows.append(dict(index=fc['index'],truth=fc['label'],up=up,text=txt,ms=ms))
-    out[res]=rows
-pickle.dump(out,open(f'{S}/ocr.pkl','wb'))
-for res,rows in out.items():
-    for up in [1,2]:
-        rr=[r for r in rows if r['up']==up]; tr=[r for r in rr if r['truth'] in KEY]
-        hit=sum(any(k in r['text'] for k in KEY[r['truth']]) for r in tr)
-        print(res,'up',up,f'in-catalog key-token hit {hit}/{len(tr)}','median ms',round(np.median([r['ms'] for r in rr]),1))
-for r in out['live']:
-    if r['up']==2 and r['truth']!='unknown': print(r['index'],r['truth'],'|',r['text'][:80])
diff --git a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/ocr_fuse.py b/recognization/docs/research/2026-09-28-recognition-architecture/evidence/ocr_fuse.py
deleted file mode 100644
index 75d41dc..0000000
--- a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/ocr_fuse.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import sys,os,json,pickle,csv,unicodedata,numpy as np,cv2,glob
-sys.path.insert(0,'code'); from ocr import VisionOcr
-S=os.path.dirname(__file__)
-def norm(s):
-    s=unicodedata.normalize('NFKC',s).upper()
-    tr=str.maketrans('ッャュョァィゥェォヮヵヶユ','ツヤユヨアイウエオワカケユ'); s=s.translate(tr)
-    s=s.replace('キユーピー','キユピ').replace('キューピー','キユピ')
-    return ''.join(ch for ch in s if ch.isalnum() or ch=='%')
-def grams(s): return {s[i:i+2] for i in range(len(s)-1)}
-def sim(a,b):
-    A,B=grams(norm(a)),grams(norm(b))
-    return len(A&B)/max(1,len(A)) if A else 0.0   # share of OCR bigrams explained by signature
-# leak-free signatures: catalog names (all SKUs) + OCR of enrolled packshots
-names={r['SKU_ID']:r['product_name'] for r in csv.DictReader(open('stock-lopia/stock.csv'))}
-if __name__=='__main__':
-  o=VisionOcr(languages=['ja-JP','en-US']); sig={}
-  for k,n in names.items():
-    sig[k]=n
-    p=f'stock-lopia-clean/{k}.jpg'
-    if os.path.exists(p): sig[k]+=' '+' '.join(l.text for l in o.read(cv2.imread(p)).lines)
-  for k,v in sig.items(): print(k, v[:90])
-  pickle.dump(sig,open(f'{S}/sig.pkl','wb'))
diff --git a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/scale.py b/recognization/docs/research/2026-09-28-recognition-architecture/evidence/scale.py
deleted file mode 100644
index 9eaefc0..0000000
--- a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/scale.py
+++ /dev/null
@@ -1,71 +0,0 @@
-"""Catalog growth: packshots + N distractor references from unrelated shelves.
-Compares exhaustive local matching vs a global RootSIFT descriptor index
-(vote -> shortlist K -> geometric verification). No DINO pass."""
-import sys,os,glob,json,time,numpy as np,cv2,random
-sys.path.insert(0,'code'); sys.path.insert(0,os.path.dirname(__file__)); os.environ['YOLO_OFFLINE']='1'
-from detector import ShelfProductDetector
-from diag import crops, sku_of
-from PIL import Image
-S=os.path.dirname(__file__); random.seed(0)
-sift=cv2.SIFT_create(nfeatures=2000)
-def root(d):
-    if d is None: return None
-    d=d/(np.abs(d).sum(1,keepdims=True)+1e-7); return np.sqrt(d).astype(np.float32)
-def feat(img,h):
-    if h and img.shape[0]>h: img=cv2.resize(img,(max(1,int(img.shape[1]*h/img.shape[0])),h),interpolation=cv2.INTER_AREA)
-    g=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY); kp,d=sift.detectAndCompute(g,None); return kp,root(d),g.shape
-# references
-refs=[]
-for p in sorted(glob.glob('stock-lopia-clean/*.jpg')):
-    img=cv2.imread(p); k=sku_of(os.path.basename(p)[:-4])
-    for h in (200,400): kp,d,sh=feat(img,h); refs.append((k,kp,d,sh))
-det=ShelfProductDetector(); dis=[]
-for p in sorted(glob.glob('add/*')):
-    if 'image6' in p: continue
-    im=cv2.cvtColor(np.array(Image.open(p).convert('RGB')),cv2.COLOR_RGB2BGR)
-    for j,b in enumerate(det.detect(im).get('boxes',[])):
-        x1,y1,x2,y2=[int(v) for v in b['box']]; c=im[max(0,y1):y2,max(0,x1):x2]
-        if min(c.shape[:2])>=40: dis.append((f'd_{os.path.basename(p)}_{j}',c))
-random.shuffle(dis); print('distractor refs available',len(dis))
-L=json.load(open(f'{S}/../../../../eval/labels/image6.json')); Q=[(fc,c) for fc,c in crops('add/image6.jpg',L,True) if fc['label']!='exclude' and min(c.shape[:2])>=8]
-Qf=[(fc['label'],)+feat(c,None) for fc,c in Q]
-bf=cv2.BFMatcher(cv2.NORM_L2)
-def verify(kq,dq,r):
-    k,kp,d,sh=r
-    if dq is None or d is None or len(d)<2 or len(dq)<2: return 0
-    good=[m for m,n in (p for p in bf.knnMatch(dq,d,k=2) if len(p)==2) if m.distance<0.75*n.distance]
-    if len(good)<4: return 0
-    src=np.float32([kq[m.queryIdx].pt for m in good]); dst=np.float32([kp[m.trainIdx].pt for m in good])
-    H,mask=cv2.estimateAffinePartial2D(src,dst,method=cv2.RANSAC,ransacReprojThreshold=0.02*max(sh))
-    return int(mask.sum()) if mask is not None else 0
-E={r[0] for r in refs}
-for N in [0,50,200,len(dis)]:
-    R=refs+[(k,)+feat(c,None) for k,c in dis[:N]]
-    # exhaustive
-    t=time.perf_counter(); ok=0; fa=0; n=0
-    best_all=[]
-    for truth,kq,dq,_ in Qf:
-        sc={}
-        for r in R: sc[r[0]]=max(sc.get(r[0],0),verify(kq,dq,r))
-        b=max(sc,key=sc.get); best_all.append(sc)
-        if truth in E: n+=1; ok+= b==truth
-    ex_ms=(time.perf_counter()-t)*1000/len(Qf)
-    # descriptor index
-    D=np.vstack([r[2] for r in R if r[2] is not None]); owner=np.concatenate([[i]*len(r[2]) for i,r in enumerate(R) if r[2] is not None])
-    t0=time.perf_counter(); fl=cv2.FlannBasedMatcher(dict(algorithm=1,trees=4),dict(checks=64)); fl.add([D]); fl.train(); build=time.perf_counter()-t0
-    rec={5:0,10:0,20:0}; t=time.perf_counter(); ok_idx=0
-    for truth,kq,dq,_ in Qf:
-        votes={}
-        if dq is not None and len(dq)>=2:
-            for m,nn in (p for p in fl.knnMatch(dq,k=2) if len(p)==2):
-                if m.distance<0.8*nn.distance:
-                    k=R[owner[m.trainIdx]][0]; votes[k]=votes.get(k,0)+1
-        order=sorted(votes,key=lambda k:-votes[k])
-        if truth in E:
-            for K in rec: rec[K]+= truth in order[:K]
-        short=set(order[:10]); sc={}
-        for r in R:
-            if r[0] in short: sc[r[0]]=max(sc.get(r[0],0),verify(kq,dq,r))
-        if truth in E and sc: ok_idx+= max(sc,key=sc.get)==truth
-    ix_ms=(time.perf_counter()-t)*1000/len(Qf)
-    print(f'refs={len(R):4d} skus={len({r[0] for r in R}):4d} | exhaustive top1 {ok}/{n} {ex_ms:6.1f} ms/crop | index recall@5/10/20 {rec[5]}/{rec[10]}/{rec[20]} of {n}, index+verify(K=10) top1 {ok_idx}/{n} {ix_ms:6.1f} ms/crop, build {build*1000:.0f} ms, {len(D)} descriptors')
diff --git a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/sheet.py b/recognization/docs/research/2026-09-28-recognition-architecture/evidence/sheet.py
deleted file mode 100644
index 8413883..0000000
--- a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/sheet.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import json,cv2,numpy as np,sys,glob,os
-d=json.load(open('eval/labels/image6.json'));im=cv2.imread('add/image6.jpg')
-S=sys.argv[1]
-H=300
-def tile(img,txt,col=(0,0,0)):
-    h,w=img.shape[:2];s=H/h;img=cv2.resize(img,(max(1,int(w*s)),H))
-    pad=np.full((H+30,img.shape[1]+6,3),255,np.uint8);pad[30:,3:3+img.shape[1]]=img
-    cv2.putText(pad,txt,(2,20),cv2.FONT_HERSHEY_SIMPLEX,0.5,col,1);return pad
-groups={}
-for f in d['facings']:
-    x1,y1,x2,y2=f['box'];c=im[y1:y2,x1:x2]
-    groups.setdefault(f['label'],[]).append(tile(c,f"#{f['index']}"))
-for lab,ts in groups.items():
-    ref=glob.glob(f'stock-lopia-clean/{lab}.jpg')
-    row=([tile(cv2.imread(ref[0]),'REF',(0,0,255))] if ref else [])+ts
-    rows=[row[i:i+12] for i in range(0,len(row),12)]
-    out=[]
-    for r in rows:
-        w=sum(t.shape[1] for t in r);canvas=np.full((H+30,max(w,10),3),255,np.uint8);x=0
-        for t in r: canvas[:,x:x+t.shape[1]]=t;x+=t.shape[1]
-        out.append(canvas)
-    W=max(o.shape[1] for o in out);out=[np.pad(o,((0,0),(0,W-o.shape[1]),(0,0)),constant_values=255) for o in out]
-    cv2.imwrite(f'{S}/gt_{lab}.jpg',np.vstack(out))
-vis=im.copy()
-for f in d['facings']:
-    x1,y1,x2,y2=f['box'];c=(0,0,255) if f['label']=='unknown' else (0,200,0)
-    cv2.rectangle(vis,(x1,y1),(x2,y2),c,2);cv2.putText(vis,f"{f['index']}:{f['label'].replace('sku_','')}",(x1+2,y1+15),cv2.FONT_HERSHEY_SIMPLEX,0.45,c,1)
-cv2.imwrite(f'{S}/shelf_labels.jpg',vis)
diff --git a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/variants.py b/recognization/docs/research/2026-09-28-recognition-architecture/evidence/variants.py
deleted file mode 100644
index 0cf1b30..0000000
--- a/recognization/docs/research/2026-09-28-recognition-architecture/evidence/variants.py
+++ /dev/null
@@ -1,65 +0,0 @@
-"""Matcher variants. Each variant returns per-SKU evidence dicts so ranking and
-open-set features can be analysed separately."""
-import sys, os, json, time, glob, pickle, argparse
-import cv2, numpy as np
-sys.path.insert(0, os.path.dirname(__file__)); from diag import crops, sku_of
-
-class Matcher:
-    def __init__(self, stock, tmpl_h=None, root=False, geom='homog', nfeat=2000, scales=(None,)):
-        self.sift = cv2.SIFT_create(nfeatures=nfeat); self.root = root; self.geom = geom
-        self.T = []
-        for p in sorted(glob.glob(f'{stock}/*.jp*g')+glob.glob(f'{stock}/*.png')):
-            img = cv2.imread(p); name = os.path.splitext(os.path.basename(p))[0]
-            for h in scales:
-                im = img if h is None else cv2.resize(img, (max(1,int(img.shape[1]*h/img.shape[0])), h), interpolation=cv2.INTER_AREA)
-                g = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY); kp, des = self.sift.detectAndCompute(g, None)
-                des = self._root(des)
-                hsv = cv2.cvtColor(im, cv2.COLOR_BGR2HSV)
-                self.T.append(dict(name=name, sku=sku_of(name), kp=kp, des=des, shape=g.shape, hist=self._hist(hsv)))
-        self.bf = cv2.BFMatcher(cv2.NORM_L2)
-    def _root(self, des):
-        if des is None or not self.root: return des
-        des = des / (np.abs(des).sum(1, keepdims=True) + 1e-7); return np.sqrt(des).astype(np.float32)
-    @staticmethod
-    def _hist(hsv):
-        m = (hsv[...,1] > 60) & (hsv[...,2] > 50)  # chromatic pixels only
-        h = cv2.calcHist([hsv], [0], m.astype(np.uint8), [18], [0, 180]).ravel()
-        return h / (h.sum() + 1e-6)
-    def score(self, crop):
-        g = cv2.cvtColor(crop, cv2.COLOR_BGR2GRAY); kq, dq = self.sift.detectAndCompute(g, None); dq = self._root(dq)
-        hq = self._hist(cv2.cvtColor(crop, cv2.COLOR_BGR2HSV))
-        ev = {}
-        for t in self.T:
-            e = dict(inliers=0, cover=0.0, color=float(np.minimum(hq, t['hist']).sum()), nq=len(kq))
-            if dq is not None and t['des'] is not None and len(dq) >= 2 and len(t['des']) >= 2:
-                good = [m for m, n in (p for p in self.bf.knnMatch(dq, t['des'], k=2) if len(p) == 2) if m.distance < 0.75*n.distance]
-                if len(good) >= 4:
-                    src = np.float32([kq[m.queryIdx].pt for m in good]); dst = np.float32([t['kp'][m.trainIdx].pt for m in good])
-                    if self.geom == 'homog':
-                        H, mask = cv2.findHomography(src, dst, cv2.RANSAC, 0.02*max(t['shape']))
-                    else:
-                        H, mask = cv2.estimateAffinePartial2D(src, dst, method=cv2.RANSAC, ransacReprojThreshold=0.02*max(t['shape']))
-                    if mask is not None:
-                        inl = mask.ravel().astype(bool); n = int(inl.sum())
-                        if n >= 3:
-                            pts = dst[inl]; hull = cv2.convexHull(pts)
-                            e['inliers'] = n; e['cover'] = float(cv2.contourArea(hull) / (t['shape'][0]*t['shape'][1]))
-                            # crop-side coverage too: does the match span the query?
-                            qh = cv2.convexHull(src[inl]); e['qcover'] = float(cv2.contourArea(qh)/(g.shape[0]*g.shape[1]))
-            k = t['sku']
-            if k not in ev or e['inliers'] > ev[k]['inliers']: ev[k] = e
-        return ev
-
-if __name__ == '__main__':
-    ap = argparse.ArgumentParser(); ap.add_argument('--stock'); ap.add_argument('--labels'); ap.add_argument('--live', action='store_true')
-    ap.add_argument('--tmpl-h', type=int, default=None); ap.add_argument('--root', action='store_true'); ap.add_argument('--geom', default='homog')
-    ap.add_argument('--scales', default=''); ap.add_argument('--out')
-    a = ap.parse_args(); L = json.load(open(a.labels))
-    scales = tuple(int(s) for s in a.scales.split(',')) if a.scales else ((a.tmpl_h,) if a.tmpl_h else (None,))
-    t0 = time.perf_counter(); m = Matcher(a.stock, root=a.root, geom=a.geom, scales=scales); init = time.perf_counter()-t0
-    rows = []
-    for fc, c in crops('add/image6.jpg', L, a.live):
-        if c.size == 0 or min(c.shape[:2]) < 8: rows.append(dict(index=fc['index'], ev={}, ms=0)); continue
-        t = time.perf_counter(); ev = m.score(c); rows.append(dict(index=fc['index'], ev=ev, ms=(time.perf_counter()-t)*1000, shape=c.shape[:2]))
-    pickle.dump(dict(rows=rows, enrolled=sorted({t['sku'] for t in m.T}), init_s=init, args=vars(a)), open(a.out, 'wb'))
-    print(a.out, 'init', round(init,2), 'median ms', round(float(np.median([r['ms'] for r in rows if r['ms']])),1))
diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/DECISION.md b/recognization/docs/research/2026-09-29-shelf-intelligence/DECISION.md
deleted file mode 100644
index 7fa9b6d..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/DECISION.md
+++ /dev/null
@@ -1,230 +0,0 @@
-# Dorayaki: shelf-intelligence decision
-
-Measured 2026-09-29 · server `product-recognition-demo/recognization` at
-`fbf31d7` plus the coverage-first diff described in §F · evidence in
-[`evidence/`](evidence/) · every number below is read from a JSON in that
-directory, named inline.
-
-Scope: the live decision path (group inference, OCR scheduling, decision
-levels), the catalog the server matches against, and the two native overlays.
-Not in scope: detector quality, tag/planogram pipeline, on-device runtime.
-
-The previous phase ([`../2026-09-28-recognition-architecture/DECISION.md`](../2026-09-28-recognition-architecture/DECISION.md))
-settled *what* matches a product: a local-feature instance matcher plus a text
-lane behind a calibrated open-set gate. This phase settles *how much of a shelf
-to label, how fast, and how to spend compute doing it*.
-
-## Verdict
-
-**Label every detected product with its best candidate immediately, mark it
-provisional, and refine it as stronger evidence arrives.** Keep the accepted
-line where it is; stop treating abstention as quality. Group adjacent facings
-so one strong observation can answer for a run, and spend OCR on group
-representatives and ambiguous tracks instead of every crop.
-
-The measured case for it, on `add/image6.jpg` at live resolution
-(`ocr-ablation.json`, `anytime-policies.json`):
-
-| Policy | Shown | Correct | Precision | Coverage of 64 in-catalog facings | Blank |
-|---|---|---|---|---|---|
-| strict / individual | 39 | 38 | 97.4 % | 59.4 % | 64.2 % |
-| **coverage-first (shipped)** | **63** | **55** | **87.3 %** | **86.0 %** | **42.2 %** |
-
-The shipped policy shows 24 more products and gets 17 more of them right, at
-the cost of 7 more wrong labels. That is the trade the product asked for: a
-shelf where 86 % of products carry a name reads as working; one where 59 % do
-reads as broken.
-
-## A. Where the live time goes
-
-`replay.py` on the running server, `image6` at 8 fps (`replay-image6-8fps-*.json`):
-
-| Stage | p50 | p95 |
-|---|---|---|
-| decode | 8–12 ms | 20 ms |
-| detect | 67–107 ms | 131–278 ms |
-| track | 1–3 ms | 6 ms |
-| total | 90–140 ms | 200–300 ms |
-
-Detection dominates frame cost by an order of magnitude; tracking is free.
-Identity work is off the frame path (a worker pool), so it shows up as
-*label latency*, not frame latency. `scheduler-benchmark.json` prices it:
-218 classification tasks, worker p50 147 ms with OCR on every attempt versus
-19.6 ms with OCR once per track. **OCR is ~87 % of per-task identity cost.**
-
-Ranked bottlenecks: (1) OCR invocations, (2) matcher invocations per facing,
-(3) detector. Only the first two are ours to schedule.
-
-## B. Compute and evidence schedule
-
-`scheduler-benchmark.json`, same 218 tasks:
-
-| Schedule | OCR calls | Worker p50 | First facing | All first facings | Wall | CPU |
-|---|---|---|---|---|---|---|
-| every attempt | 218 | 147 ms | 1177 ms | 1890 ms | 4.32 s | 4.48 s |
-| once per track | 109 | 20 ms | 1235 ms | 2680 ms | 2.77 s | 3.42 s |
-| ambiguous track only | 83 | 16 ms | 806 ms | 1951 ms | 2.05 s | 2.97 s |
-| **group representative** | **49** | **16 ms** | **454 ms** | **1168 ms** | **1.30 s** | **2.64 s** |
-
-Group representatives cut OCR calls 4.4× and wall time 3.3×, and they make the
-first label appear 2.6× sooner. The accuracy cost is small and measured
-(`ocr-ablation.json`, group metrics): full-resolution coverage 57.8 % → 56.0 %,
-precision 87.3 % → 80.3 %; at live resolution coverage *rises* 56.0 % → 58.7 %
-and precision 90.2 % → 87.5 %. Ship the group-representative schedule; the
-`ambiguous_track` schedule is the fallback when grouping is unavailable.
-
-The shipped live path already does the cheap half of this: OCR runs once per
-track (`live.py` caches `text_read` on the track), and the local lane re-runs
-on every attempt.
-
-## C. Repeated-facing group detection
-
-`analysis.json` (`resolutions.full` and `.live`) scores eleven pairwise signals
-on 83 adjacent pairs from `image6` (72 same-SKU, 3 sibling, 8 unrelated), with
-leave-one-row-out thresholds:
-
-| Signal | AUC same-vs-sibling | AUC same-vs-unrelated | Precision | Recall | Boundary acc. |
-|---|---|---|---|---|---|
-| hue histogram | 0.977 | 0.967 | 98.3 % | 80.6 % | 81.9 % |
-| logo pixels | 0.968 | 0.981 | 100 % | 93.1 % | 94.0 % |
-| pixels | 0.944 | 0.979 | 100 % | 88.9 % | 90.4 % |
-| inliers | 0.944 | 0.944 | 100 % | 88.9 % | 90.4 % |
-| height | 0.972 | 0.775 | 92.6 % | 87.5 % | 83.1 % |
-| aspect | 0.403 | 0.691 | 93.9 % | 43.1 % | 48.2 % |
-| ocr_jaccard | 0.667 | 0.806 | 97.7 % | 58.3 % | 62.7 % |
-| **combined** | **0.991** | **0.990** | **98.5 %** | **91.7 %** | **91.6 %** |
-
-Use the **combined** signal: it is the only one that is simultaneously strong on
-siblings (the hard case) and on unrelated pairs. Hue alone is nearly as good and
-is the cheapest single signal; logo pixels is the best single signal but needs a
-logo detector. Aspect ratio and OCR similarity are actively misleading on
-siblings (AUC 0.40 and 0.67) — do not gate a group on either.
-
-At live resolution the combined signal holds: AUC 0.991 / 0.984, precision
-96.9 %, recall 87.5 %, boundary accuracy 86.7 %.
-
-## D. Group-to-individual inference
-
-`representative.json` prices propagating a group identity to its members, on
-`image6` (109 facings, 64 in-catalog):
-
-| Arm | Matcher calls | Total ms | Shown | Correct | Precision | Coverage | Rescued | Damaged |
-|---|---|---|---|---|---|---|---|---|
-| sharpest-one | 47 | 1114 | 50 | 46 | 92.0 % | 45.9 % | 1 | 8 |
-| sharpest-two | 70 | 1413 | 52 | 48 | 92.3 % | 47.7 % | 1 | 6 |
-| **all (no grouping)** | **109** | **2024** | **63** | **55** | **87.3 %** | **57.8 %** | **2** | **0** |
-
-At full resolution, recognising only the sharpest member of each group **damages
-8 correct labels** to rescue 1. At live resolution the same arm rescues 3 and
-damages 8. Group propagation is therefore **not** a coverage win on this shelf:
-the members are individually recognisable, and the group's answer is only as
-good as its weakest member when the group is wrong.
-
-Decision: **use groups to schedule compute (C, B), not to overwrite individual
-labels.** Propagate a group identity only to members whose own evidence is
-empty, and never over a member that produced a confident individual answer.
-The `rescued_correct` column is the ceiling for that rule; it is small here and
-should be re-measured on a shelf with more blur and occlusion.
-
-## E. Sibling boundaries
-
-The combined signal's boundary accuracy is 91.6 % at full resolution, 86.7 % at
-live (`analysis.json`). The failure mode is a boundary missed, not a boundary
-invented: precision 98.5 % vs recall 91.7 %. A missed boundary merges two
-sibling runs into one group.
-
-Because §D says groups do not overwrite individual labels, a missed boundary is
-now a *scheduling* error (one representative OCR'd for two SKUs), not a
-labelling error. That is the safe direction to fail. Two rules keep it safe:
-
-- A member whose own individual answer disagrees with the group's is a boundary
-  candidate, not an outlier to be smoothed away.
-- A group never spans a detected price-tag change or a gap wider than the
-  shelf's own facing pitch.
-
-## F. Provisional and confirmed transitions
-
-Shipped in `live.py` `_on_classified`, `identity.py`, `identity_lanes.py`:
-
-- `accepted` — the decision cleared its gate. `state: recognized`,
-  `identity_level: accepted`, `confirmed = True`, never re-classified.
-- `provisional` — the decision produced a candidate but did not clear the gate.
-  `state: recognized`, `identity_level: provisional`, `confirmed = False`, and
-  the track **stays eligible for re-classification** so a later, sharper attempt
-  can upgrade it to accepted or correct it.
-- `unrecognized` — no candidate at all, or the attempt budget is spent with no
-  provisional label on screen. `state: unrecognized`, no chip.
-
-A provisional label is never blanked once shown: when the budget runs out the
-track keeps its guess and simply stops being re-classified. An inherited
-provisional label stays re-classifiable; an inherited accepted label does not.
-
-Measured on the wire (`replay-image6-8fps-0.5x-0.json`): 65 of 79 tracks
-labelled, 742 accepted + 309 provisional observations, first label at 1.27 s.
-`identity_level` is the only new wire field; an older client that ignores it
-renders a provisional label as a normal one, which is the intended degradation.
-
-Both overlays render provisional distinctly: amber chip and dashed box
-(`LiveScanOverlayPolicy.isProvisional(identity)`, `ScanScreen.kt`,
-`LiveScanOverlay.swift`).
-
-## G. Operating recommendation
-
-`far-unknown-policies.json` prices the accept floor on 4580 crops from 64
-unrelated Wikimedia shelf photos:
-
-| Policy | local_accept | Accepts | Rate |
-|---|---|---|---|
-| strict | 4.0 | 3 | 0.066 % |
-| balanced | 2.0 | 8 | 0.175 % |
-| coverage-first | 0.5 | 83 | 1.81 % |
-| near-argmax | 0.0 + fallback | 119 | 2.60 % |
-
-The shipped `LOCAL_ACCEPT` stays at **4.0** for the *accepted* level: it is the
-line that keeps far-unknown false accepts at 0.066 %, and the accepted level is
-what the product treats as settled. Coverage comes from the **provisional**
-level, which is a separate, much looser rule and is not gated by this number.
-
-This is the key structural choice: **do not lower the accept line to raise
-coverage.** Lowering it to 0.5 would raise far-unknown false accepts 27× and
-would still leave the shelf's own hard cases (siblings) unresolved. Showing the
-best candidate as provisional gets the coverage without moving the line that
-protects precision.
-
-`anytime-policies.json` confirms the shape: the strict policy reaches 59.4 %
-coverage and stays there at every time budget; coverage-first reaches 86 % and
-is already at 28 % of facings by 250 ms.
-
-## H. Measured improvement
-
-| Metric | Before | After |
-|---|---|---|
-| Catalog the server matches | 7 SKUs / 13 views | **60 SKUs / 86 views** |
-| Tracks labelled, `image6` 8 fps 0.5× | 57 / 80 | **65 / 79** |
-| In-catalog facings shown | 59.4 % | **86.0 %** |
-| Precision of shown labels | 97.4 % | 87.3 % |
-| First label | 623 ms | 1.27 s |
-| OCR calls per 218 tasks | 218 | 49 (group-representative schedule) |
-| Wall time per 218 tasks | 4.32 s | 1.30 s |
-
-The catalog change is `code/build_stock_all.py`, which merges `stock`,
-`stock-lopia`, `stock-mediya` and `stock-milk150` into `stock-all` (111 images,
-60 SKUs; `stock-demo` is byte-identical to mediya+milk150 and is not a source).
-SKU ids are unique across the folders, so the merge is a copy plus a CSV concat.
-
-## Limitations
-
-- Every shelf number is one photo, `add/image6.jpg`, and its labels are the
-  corrected ones from the previous phase. The group signals and the
-  representative arms are measured on that photo's own facings.
-- The far-unknown rates come from a cache that records no catalog, so the
-  catalog match is read off the worktree, not attested by the cache.
-- `scheduler-benchmark.json` and `anytime-policies.json` are diagnostic
-  counterfactuals: they price schedules against frozen evidence, not against a
-  live deployment. Transport and grouping time are excluded.
-- The native overlays were changed but not compiled: no JDK is installed on
-  this machine, so `./gradlew` cannot run. The shared signature change is
-  mechanical and every caller was updated, but this is unverified by a compiler.
-- Group propagation (§D) is deliberately not shipped as a labelling rule. Its
-  ceiling on this shelf is 1–3 rescued labels against 8 damaged; re-measure on a
-  shelf with real blur and occlusion before revisiting.
diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/analyze.py b/recognization/docs/research/2026-09-29-shelf-intelligence/analyze.py
deleted file mode 100644
index 5eba6a8..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/analyze.py
+++ /dev/null
@@ -1,267 +0,0 @@
-"""A-D/F: paired policies, geometry-only rows, label-blind inference, oracle membership.
-No production edits. All accuracy counters use the frozen observation file.
-"""
-from pathlib import Path
-import sys
-import json
-import time
-import itertools
-import math
-from collections import Counter
-import numpy as np
-
-ROOT=Path(__file__).resolve().parents[3]
-sys.path.insert(0,str(ROOT/'code'))
-from identity_lanes import DecisionPolicy
-DIR=Path(__file__).resolve().parent
-POLICIES={'strict':4.,'balanced':2.,'coverage-first':.5,'near-argmax':0.}
-
-
-def decide(o, policy, signatures, use_text=True):
-    p=DecisionPolicy(local_accept=POLICIES[policy],signatures=signatures)
-    text=o['text'] if use_text else {}
-    read=o['read'] if use_text else ''
-    d=p.decide(o['rank'],text,text_read=read)
-    answer=d['best_sku']
-    # Near-argmax retains actual geometric candidates even when the confidence
-    # margin is negative; no arbitrary catalog row when there are no features.
-    if policy=='near-argmax' and not answer and o['rank']:
-        candidate=o['rank'][0][0]
-        tr=sorted(text.items(),key=lambda x:x[1],reverse=True)
-        if not p._contradicts_size(read,candidate) and not (tr and tr[0][0]!=candidate and tr[0][1]>text.get(candidate,0)):
-            answer=candidate
-    return answer
-
-
-def pair_value(p, name):
-    s=p['signals']
-    if name=='combined':
-        return .35*s['hue']+.35*s['pixels']+.30*min(1,s['inliers']/10)
-    return s[name]
-
-
-def geometric_edge(p):
-    s=p['signals']
-    return -.25<=s['gap']<=.75 and s['height']>=.65 and s['bottom']<=.25
-
-
-def infer_groups(data, edge_yes, anchors):
-    """Contiguous appearance-supported runs; strong contradiction is a hard cut."""
-    edges={(p['a'],p['b']):edge_yes[i] and geometric_edge(p) for i,p in enumerate(data['pairs'])}
-    groups=[]
-    for row in data['rows']:
-        group=[]; support=None
-        for item in row:
-            anchor=anchors.get(item)
-            if group and (not edges.get((group[-1],item),False) or (anchor and support and anchor!=support)):
-                groups.append(group); group=[]; support=None
-            group.append(item)
-            if anchor: support=anchor
-        if group: groups.append(group)
-    return groups
-
-
-def strength(o, answer):
-    if not answer: return 0.
-    if o['rank'] and o['rank'][0][0]==answer:
-        return max(0,o['rank'][0][1])+2*o['text'].get(answer,0)
-    return 2*o['text'].get(answer,0)
-
-
-def propagate(observations, groups, answers, signatures, guarded=False):
-    """One strongest independently supported prototype, not majority voting.
-    Group evidence can rescue weak members, never overwrite strict contradiction.
-    """
-    lookup={x['id']:x for x in observations}
-    result=dict(answers); rescued=[]
-    strict={i:decide(o,'strict',signatures) for i,o in lookup.items()}
-    guard=DecisionPolicy(signatures=signatures)
-    for group in groups:
-        seeds=[i for i in group if strict[i]]
-        if not seeds: continue
-        seed=max(seeds,key=lambda i:strength(lookup[i],strict[i]))
-        sku=strict[seed]
-        if guarded:
-            # Two distinct facings corroborate a seed, or one unusually strong
-            # geometric observation does. Repeated reads of one crop do not.
-            corroborated=sum(strict[i]==sku for i in seeds)>=2
-            strong_visual=bool(lookup[seed]['rank'] and lookup[seed]['rank'][0][0]==sku and lookup[seed]['rank'][0][1]>=8)
-            if not (corroborated or strong_visual): continue
-            # Size contradictions belong to the entire repeated-artwork run.
-            # Abstain from propagation rather than spread a lone OCR mistake.
-            if any(guard._contradicts_size(lookup[i]['read'],sku) for i in group): continue
-        for i in group:
-            o=lookup[i]
-            if strict[i] and strict[i]!=sku: continue
-            if guard._contradicts_size(o['read'],sku): continue
-            # Independently decisive text also splits a weak visual member.
-            text_sku=guard.decide([],o['text'],text_read=o['read'])['best_sku']
-            if text_sku and text_sku!=sku: continue
-            if result[i]!=sku:
-                rescued.append({'id':i,'seed':seed,'from':result[i],'to':sku})
-                result[i]=sku
-    return result,rescued
-
-
-def metrics(observations, answers, families, before=None):
-    total=len(observations); known=sum(o['truth']!='unknown' for o in observations)
-    shown=correct=unknown=sibling=wrong_known=0
-    rescue=correction=damage=0
-    for o in observations:
-        i=o['id']; a=answers.get(i); t=o['truth']
-        shown+=bool(a); correct+=bool(a and a==t)
-        unknown+=bool(a and t=='unknown')
-        wrong_known+=bool(a and t!='unknown' and a!=t)
-        sibling+=bool(a and a!=t and t in families and families.get(a)==families[t])
-        if before is not None:
-            rescue+=bool(not before.get(i) and a==t)
-            correction+=bool(before.get(i) and before[i]!=t and a==t)
-            damage+=bool(before.get(i)==t and a!=t)
-    return {'facings':total,'catalog_facings':known,'shown':shown,'correct':correct,'wrong':shown-correct,
-            'visible_coverage':shown/total,'correct_coverage':correct/known,'precision':correct/shown if shown else None,
-            'false_label_rate':(shown-correct)/total,'unknown_false_accepts':unknown,'unknown_total':total-known,
-            'sibling_confusions':sibling,'wrong_catalog':wrong_known,'blank_ratio':1-shown/total,
-            'rescued_correct':rescue,'corrected_wrong':correction,'damaged_correct':damage}
-
-
-def confusion(actual,predicted):
-    tp=sum(a and p for a,p in zip(actual,predicted)); fp=sum(not a and p for a,p in zip(actual,predicted))
-    tn=sum(not a and not p for a,p in zip(actual,predicted)); fn=sum(a and not p for a,p in zip(actual,predicted))
-    return {'tp':tp,'fp':fp,'tn':tn,'fn':fn,'precision':tp/(tp+fp) if tp+fp else None,'recall':tp/(tp+fn) if tp+fn else None,'boundary_accuracy':(tp+tn)/max(1,len(actual)),'boundary_recall':tn/(tn+fp) if tn+fp else None}
-
-
-def auc(pos,neg):
-    return sum((a>b)+.5*(a==b) for a in pos for b in neg)/(len(pos)*len(neg)) if pos and neg else None
-
-
-def fit_threshold(pairs, name):
-    # Weighted classification error, so long repeated runs cannot hide boundaries.
-    candidates=sorted(set(pair_value(p,name) for p in pairs))
-    candidates=[-1e-9]+[(a+b)/2 for a,b in zip(candidates,candidates[1:])]+[max(candidates,default=0)+1e-9]
-    def loss(t):
-        yes=[p for p in pairs if p['same']]; no=[p for p in pairs if not p['same']]
-        return sum(pair_value(p,name)<t for p in yes)/max(1,len(yes))+sum(pair_value(p,name)>=t for p in no)/max(1,len(no))
-    return min(candidates,key=lambda t:(loss(t),-t))
-
-
-def chain_dp(data, edge_yes, observations, signatures):
-    """Potts chain MAP by Viterbi, O(n*K^2), no framework or truth input."""
-    lookup={o['id']:o for o in observations}; skus=[None]+list(signatures)
-    edge={(p['a'],p['b']):edge_yes[i] and geometric_edge(p) for i,p in enumerate(data['pairs'])}
-    result={}
-    for row in data['rows']:
-        unary=[]
-        for i in row:
-            o=lookup[i]; values={s:e['inliers']*(.25+e['color']) for s,e in o['local'].items()}
-            maximum=max(values.values(),default=1.)
-            u=[.4]+[values.get(s,0)/maximum+.4*o['text'].get(s,0) for s in skus[1:]]
-            anchor=decide(o,'strict',signatures)
-            if anchor:
-                u=[v if s==anchor else v-4 for s,v in zip(skus,u)]
-            unary.append(np.asarray(u))
-        score=unary[0]; backs=[]
-        for n in range(1,len(row)):
-            reward=.6 if edge.get((row[n-1],row[n]),False) else 0.
-            table=score[:,None]+np.eye(len(skus))*reward
-            back=table.argmax(axis=0); score=unary[n]+table[back,np.arange(len(skus))]; backs.append(back)
-        best=int(score.argmax()); labels=[best]
-        for back in reversed(backs): best=int(back[best]); labels.append(best)
-        for i,k in zip(row,reversed(labels)): result[i]=skus[k]
-    return result
-
-
-def main():
-    evidence=json.loads((DIR/'evidence/observations.json').read_text()); annotation=json.loads((DIR/'group-truth.json').read_text())
-    families=annotation['families']; report={'provenance':evidence['provenance'],'resolutions':{}}
-    for res,data in evidence['resolutions'].items():
-        observations=data['observations']; lookup={o['id']:o for o in observations}
-        keys={o['id']:o['truth'] for o in observations if o['truth']!='unknown'}
-        family=dict(families)
-        for g in annotation['unknown_visual_groups']:
-            keys.update({i:g['key'] for i in g['members']}); family[g['key']]=g['family']
-        scored=[]
-        for p in data['pairs']:
-            if p['a'] not in keys or p['b'] not in keys: continue
-            a,b=keys[p['a']],keys[p['b']]
-            scored.append({**p,'same':a==b,'class':'same' if a==b else 'sibling' if family[a]==family[b] else 'unrelated'})
-        signal_report={}
-        names=['hue','pixels','logo_pixels','edge','inliers','match_fraction','layout_cover','aspect','height','ocr_jaccard','combined']
-        predictions={}
-        for name in names:
-            thresholds={r:fit_threshold([p for p in scored if p['row']!=r],name) for r in range(len(data['rows']))}
-            predictions[name]=[pair_value(p,name)>=thresholds[p['row']] and geometric_edge(p) for p in data['pairs']]
-            byclass={c:[pair_value(p,name) for p in scored if p['class']==c] for c in ['same','sibling','unrelated']}
-            signal_report[name]={'thresholds_leave_row_out':thresholds,'same_vs_sibling_auc':auc(byclass['same'],byclass['sibling']),'same_vs_unrelated_auc':auc(byclass['same'],byclass['unrelated']), 'classes':{c:{'n':len(v),'p50':float(np.median(v)) if v else None} for c,v in byclass.items()},'held_row_confusion':confusion([p['same'] for p in scored],[pair_value(p,name)>=thresholds[p['row']] and geometric_edge(p) for p in scored])}
-        # Preselected combined arm; do not pick the best held-row score after seeing truth.
-        edge_yes=predictions['combined']
-        anchors={o['id']:decide(o,'strict',evidence['signatures']) for o in observations}
-        start=time.perf_counter(); groups=infer_groups(data,edge_yes,anchors); group_ms=(time.perf_counter()-start)*1000
-        # One boundary variable: correspondence is required in addition to the
-        # combined score. Stops color-only joins between different artworks.
-        guarded_edges=[yes and p['signals']['inliers']>=4 for yes,p in zip(edge_yes,data['pairs'])]
-        guarded_groups=infer_groups(data,guarded_edges,anchors)
-        appearance_groups=infer_groups(data,guarded_edges,{})
-        oracle=[]
-        for row in data['rows']:
-            run=[]; key=None
-            for i in row:
-                nextkey=keys.get(i,('unscored',i))
-                if run and key!=nextkey: oracle.append(run); run=[]
-                key=nextkey; run.append(i)
-            if run:oracle.append(run)
-        all_pairs=list(itertools.combinations(sorted(keys),2))
-        def memberships(gs): return {i:g for g,ids in enumerate(gs) for i in ids}
-        membership=memberships(groups); oracle_membership=memberships(oracle)
-        group_eval=confusion([oracle_membership[a]==oracle_membership[b] for a,b in all_pairs],[membership[a]==membership[b] for a,b in all_pairs])
-        guarded_membership=memberships(guarded_groups)
-        guarded_eval=confusion([oracle_membership[a]==oracle_membership[b] for a,b in all_pairs],[guarded_membership[a]==guarded_membership[b] for a,b in all_pairs])
-        boundary_eval=confusion([p['same'] for p in scored],[guarded_membership[p['a']]==guarded_membership[p['b']] for p in scored])
-        results={}; answers_output={}
-        for policy in POLICIES:
-            base={o['id']:decide(o,policy,evidence['signatures']) for o in observations}
-            results[policy]={}; answers_output[policy]={}
-            for mode,gs in [('individual',[[o['id']] for o in observations]),('oracle',oracle),('inferred',groups),('seed_guard',groups),('boundary_guard',guarded_groups),('guarded',guarded_groups)]:
-                start=time.perf_counter(); answers,changed=propagate(observations,gs,base,evidence['signatures'],guarded=mode in ('seed_guard','guarded')) if mode!='individual' else (base,[]); fusion_ms=(time.perf_counter()-start)*1000
-                results[policy][mode]={**metrics(observations,answers,families,base),'changes':changed,'fusion_ms':fusion_ms}
-                answers_output[policy][mode]=answers
-        start=time.perf_counter(); dp=chain_dp(data,edge_yes,observations,evidence['signatures']); dp_ms=(time.perf_counter()-start)*1000
-        results['dp']={**metrics(observations,dp,families,answers_output['coverage-first']['individual']),'inference_ms':dp_ms}
-        # Utility sensitivity, static one-second exposure proxy; temporal loss is in replay.
-        sensitivity={}
-        for wrong_cost in [.5,1.,2.,4.,8.]:
-            cells={f'{p}/{mode}':(m['facings']-m['shown']+wrong_cost*m['wrong'])/m['facings'] for p,arms in results.items() if p!='dp' for mode,m in arms.items()}
-            sensitivity[str(wrong_cost)]={'loss':cells,'best':min(cells,key=cells.get)}
-            deployable={k:v for k,v in cells.items() if not k.endswith('/oracle')}
-            sensitivity[str(wrong_cost)]['best_nonoracle']=min(deployable,key=deployable.get)
-        report['resolutions'][res]={'pair_counts':dict(Counter(p['class'] for p in scored)), 'signals':signal_report,'pairs':scored,'groups':groups,'oracle_groups':oracle,'group_ms':group_ms,'group_membership':group_eval,'policies':results,'answers':answers_output,'utility_sensitivity':sensitivity}
-        report['resolutions'][res]['guarded_groups']=guarded_groups
-        report['resolutions'][res]['appearance_groups']=appearance_groups
-        report['resolutions'][res]['guarded_group_membership']=guarded_eval
-        report['resolutions'][res]['guarded_adjacent_boundaries']=boundary_eval
-        report['resolutions'][res]['guarded_edges']=guarded_edges
-        print(res,'groups',len(groups),'pair counts',report['resolutions'][res]['pair_counts'])
-        for p in POLICIES:
-            print(p,{mode:(m['shown'],m['correct'],m['wrong'],m['rescued_correct'],m['damaged_correct']) for mode,m in results[p].items()})
-    (DIR/'evidence/analysis.json').write_text(json.dumps(report,ensure_ascii=False,indent=2))
-    plot(report)
-
-
-def plot(report):
-    # SVG uses stdlib only; both axes and denominators are explicit.
-    colors={'strict':'#475569','balanced':'#2563eb','coverage-first':'#059669','near-argmax':'#dc2626'}
-    parts=['<svg xmlns="http://www.w3.org/2000/svg" width="1000" height="510" viewBox="0 0 1000 510"><rect width="1000" height="510" fill="white"/>']
-    for j,res in enumerate(['full','live']):
-        x0=70+j*490; y0=405
-        parts.append(f'<text x="{x0}" y="30" font-size="18">{res}: visible coverage / precision</text><path d="M{x0},50 V405 H{x0+390}" fill="none" stroke="black"/>')
-        for t in [0,.25,.5,.75,1]:
-            parts.append(f'<text x="{x0+390*t-10}" y="430" font-size="12">{t:.0%}</text><text x="{x0-40}" y="{y0-350*t}" font-size="12">{t:.0%}</text>')
-        for p in POLICIES:
-            for mode in ['individual','guarded']:
-                m=report['resolutions'][res]['policies'][p][mode]; x=x0+390*m['visible_coverage']; y=y0-350*(m['precision'] or 0)
-                parts.append(f'<circle cx="{x}" cy="{y}" r="5" fill="{colors[p] if mode=="guarded" else "white"}" stroke="{colors[p]}"><title>{p}/{mode}: {m["shown"]}/109 shown, {m["correct"]}/64 correct, {m["wrong"]} false</title></circle>')
-        parts.append(f'<text x="{x0}" y="458" font-size="12">Any labels / 109; filled = guarded groups, hollow = individual</text>')
-    for n,(policy,color) in enumerate(colors.items()):
-        parts.append(f'<text x="{70+n*225}" y="491" font-size="14" fill="{color}">{policy}</text>')
-    parts.append('</svg>');(DIR/'evidence/frontier.svg').write_text(''.join(parts))
-
-if __name__=='__main__': main()
diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/anytime.py b/recognization/docs/research/2026-09-29-shelf-intelligence/anytime.py
deleted file mode 100644
index 9577e1e..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/anytime.py
+++ /dev/null
@@ -1,58 +0,0 @@
-"""Policy counterfactuals on measured worker evidence arrival times.
-All crops enter together; these are not camera/UI end-to-end timings.
-"""
-from pathlib import Path
-import json
-from statistics import median
-from analyze import POLICIES,decide,infer_groups,propagate,metrics
-
-DIR=Path(__file__).resolve().parent;OUT=DIR/'evidence'
-
-
-def main():
-    frozen=json.loads((OUT/'observations.json').read_text())
-    data=frozen['resolutions']['live'];sig=frozen['signatures']
-    edges=json.loads((OUT/'analysis.json').read_text())['resolutions']['live']['guarded_edges']
-    events=json.loads((OUT/'anytime-events.json').read_text())
-    families=json.loads((DIR/'group-truth.json').read_text())['families']
-    report={'clock':'Measured local/OCR completion; frozen appearance edges are precomputed, grouping and transport time excluded. Diagnostic counterfactual, not live deployment.','policies':{}}
-    horizon=events['complete_ms']+1000
-    for policy in POLICIES:
-        for grouped in (False,True):
-            rows={o['id']:{**o,'rank':[],'local':{},'text':{},'read':''} for o in data['observations']}
-            history={i:[(0.,None)] for i in rows};snapshots={};current={i:None for i in rows}
-            for event in sorted(events['events'],key=lambda e:e['ms']):
-                for delay in (250,500,1000):
-                    if delay<event['ms'] and str(delay) not in snapshots:snapshots[str(delay)]=metrics(list(rows.values()),current,families)
-                o=rows[event['id']]
-                if event['kind']=='local':o['rank']=event['rank']
-                else:o['read']=event['read'];o['text']=event['text']
-                obs=list(rows.values());base={i:decide(o,policy,sig) for i,o in rows.items()}
-                groups=infer_groups(data,edges,{i:decide(o,'strict',sig) for i,o in rows.items()})
-                current=propagate(obs,groups,base,sig,guarded=True)[0] if grouped else base
-                for i,sku in current.items():
-                    if sku!=history[i][-1][1]:history[i].append((event['ms'],sku))
-            for delay in (250,500,1000):snapshots.setdefault(str(delay),metrics(list(rows.values()),current,families))
-            first=[];stable=[];flips=0;corrections=0;blank_ms=wrong_ms=sibling_ms=0.
-            for i,h in history.items():
-                truth=rows[i]['truth'];named=[at for at,sku in h if sku]
-                if named:first.append(min(named))
-                if h[-1][1]==truth and truth!='unknown':stable.append(h[-1][0])
-                previous=None
-                for (_,sku),(end,_) in zip(h,h[1:]+[(horizon,None)]):
-                    if sku and previous and sku!=previous:
-                        flips+=1;corrections+=sku==truth
-                    if sku:previous=sku
-                for (at,sku),(end,_) in zip(h,h[1:]+[(horizon,None)]):
-                    duration=end-at
-                    if not sku:blank_ms+=duration
-                    elif sku!=truth:
-                        wrong_ms+=duration
-                        if truth in families and families.get(sku)==families[truth]:sibling_ms+=duration
-            final=metrics(list(rows.values()),current,families)
-            assert final['correct']+final['wrong']==final['shown']<=109
-            report['policies'][policy+('/grouped' if grouped else '/individual')]={'final':final,'timed':snapshots,'first_any_ms':min(first) if first else None,'first_per_facing_p50_ms':median(first) if first else None,'time_to_final_correct_p50_ms':median(stable) if stable else None,'identity_flips':flips,'wrong_to_correct_events':corrections,'blank_facing_ms':blank_ms,'wrong_facing_ms':wrong_ms,'wrong_sibling_facing_ms':sibling_ms,'loss_proxy':(blank_ms+1.5*sibling_ms+4*(wrong_ms-sibling_ms)+200*flips)/(109*horizon),'horizon_ms':horizon}
-    (OUT/'anytime-policies.json').write_text(json.dumps(report,ensure_ascii=False,indent=2))
-    for name,r in report['policies'].items():print(name,r['final']['shown'],r['final']['correct'],round(r['first_per_facing_p50_ms'],1),r['identity_flips'],round(r['loss_proxy'],3))
-
-if __name__=='__main__':main()
diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/bench_scheduler.py b/recognization/docs/research/2026-09-29-shelf-intelligence/bench_scheduler.py
deleted file mode 100644
index 388c84c..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/bench_scheduler.py
+++ /dev/null
@@ -1,70 +0,0 @@
-"""Actual eight-worker local/OCR calls; fixed crops, one variable per arm."""
-from pathlib import Path
-import json
-import sys
-import time
-import statistics
-import threading
-import concurrent.futures
-import os
-import psutil
-
-ROOT=Path(__file__).resolve().parents[3]
-sys.path.insert(0,str(ROOT/'code'))
-import cv2
-from identity_lanes import LocalLane,TextLane
-from ocr import build_ocr_engine
-from eval_identity import _catalog_rows
-DIR=Path(__file__).resolve().parent; OUT=DIR/'evidence'
-
-
-def percentile(v,n):
-    return round(sorted(v)[min(len(v)-1,int((len(v)-1)*n))],2)
-
-
-def main():
-    cv2.setNumThreads(1)
-    image=cv2.imread(str(ROOT/'add/image6.jpg'))
-    scale=1280/max(image.shape[:2]);image=cv2.resize(image,(round(image.shape[1]*scale),round(image.shape[0]*scale)),interpolation=cv2.INTER_AREA)
-    evidence=json.loads((OUT/'observations.json').read_text());rows=evidence['resolutions']['live']['observations']
-    schedule=json.loads((OUT/'ocr-ablation.json').read_text())['arms']['live']
-    crops={o['id']:image[o['box'][1]:o['box'][3],o['box'][0]:o['box'][2]] for o in rows}
-    local=LocalLane(str(ROOT/'stock-lopia-clean'))
-    text=TextLane.from_catalog(_catalog_rows(str(ROOT/'stock-lopia-clean')),str(ROOT/'stock-lopia-clean'),build_ocr_engine('vision'),lock=threading.Lock())
-    report={}
-    local.rank(next(iter(crops.values())))
-    text.read(next(iter(crops.values())))
-    # These benchmarks do not decode, detect, track or transmit; they isolate
-    # classifier worker contention with the same 109 observed shelf crops.
-    modes=[('every_attempt',2,lambda i,a:True),('once_per_track',2,lambda i,a:a==0),('ambiguous_track',2,lambda i,a:a==0 and i in schedule['ambiguous_track']['ocr_ids']),('group_representative',2,lambda i,a:a==0 and i in schedule['group_representative']['ocr_ids'])]
-    proc=psutil.Process()
-    for name,attempts,ocr_on in modes:
-        tasks=[(i,a,ocr_on(i,a)) for a in range(attempts) for i in crops]
-        durations=[];first={};done={};ocr_count=0
-        cpu0=proc.cpu_times();started=time.perf_counter()
-        def work(item):
-            i,a,use_ocr=item;start=time.perf_counter();rank=local.rank(crops[i]);read=text.read(crops[i]) if use_ocr else ''
-            return i,a,round((time.perf_counter()-start)*1000,2),bool(read)
-        with concurrent.futures.ThreadPoolExecutor(max_workers=8) as pool:
-            for f in concurrent.futures.as_completed([pool.submit(work,item) for item in tasks]):
-                i,a,elapsed,read=f.result();now=(time.perf_counter()-started)*1000
-                durations.append(elapsed);first.setdefault(i,now);done[i]=now;ocr_count+=bool(read)
-        wall=time.perf_counter()-started;cpu1=proc.cpu_times();cpu=cpu1.user+cpu1.system-cpu0.user-cpu0.system
-        report[name]={'tasks':len(tasks),'ocr_invocations':sum(int(k) for _,_,k in tasks),'ocr_nonempty_reads':ocr_count,'worker_ms_p50':percentile(durations,.5),'worker_ms_p95':percentile(durations,.95),'first_facing_ms_p50':percentile(list(first.values()),.5),'all_first_facings_ms':round(max(first.values()),1),'wall_s':round(wall,3),'throughput_tasks_s':round(len(tasks)/wall,1),'cpu_s':round(cpu,2),'first_results':{str(i):round(v,1) for i,v in first.items()}}
-        print(name,report[name]['wall_s'],report[name]['all_first_facings_ms'],report[name]['ocr_invocations'],flush=True)
-    (OUT/'scheduler-benchmark.json').write_text(json.dumps(report,indent=2))
-    # Separate visual completion from serialized text: no OCR lock occupies a
-    # local worker. Measure evidence arrival, not phone rendering or inference.
-    started=time.perf_counter();events=[]
-    with concurrent.futures.ThreadPoolExecutor(max_workers=8) as pool:
-        pending={pool.submit(local.rank,crop):i for i,crop in crops.items()}
-        for future in concurrent.futures.as_completed(pending):
-            events.append({'id':pending[future],'kind':'local','ms':(time.perf_counter()-started)*1000,'rank':future.result()})
-    visual_done=(time.perf_counter()-started)*1000
-    for i in schedule['ambiguous_track']['ocr_ids']:
-        read=text.read(crops[i])
-        events.append({'id':i,'kind':'text','ms':(time.perf_counter()-started)*1000,'read':read,'text':text.scores_from_text(read)})
-    (OUT/'anytime-events.json').write_text(json.dumps({'provenance':evidence['provenance'],'visual_done_ms':visual_done,'complete_ms':(time.perf_counter()-started)*1000,'events':events},ensure_ascii=False,indent=2))
-    print('visual_first',round(visual_done,1),'ms to all visual results',flush=True)
-
-if __name__=='__main__':main()
diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/collect.py b/recognization/docs/research/2026-09-29-shelf-intelligence/collect.py
deleted file mode 100644
index 3e71bee..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/collect.py
+++ /dev/null
@@ -1,118 +0,0 @@
-"""Frozen A-F observations; imports shipped lanes, never edits production.
-Run from recognization/: .venv/bin/python docs/research/2026-09-29-shelf-intelligence/collect.py
-"""
-from pathlib import Path
-import sys
-import json
-import time
-from dataclasses import asdict
-
-ROOT = Path(__file__).resolve().parents[3]
-sys.path.insert(0, str(ROOT / 'code'))
-import cv2
-import numpy as np
-from identity_lanes import LocalLane, TextLane, Evidence, _root_sift, _hue_histogram
-from ocr import build_ocr_engine
-from eval_identity import catalog_hash, file_hash, code_revision, _catalog_rows
-
-OUT = Path(__file__).resolve().parent / 'evidence'
-
-
-def rows_from_boxes(items):
-    """Bottom-aligned shelf rows, using geometry only, no labels or catalog IDs."""
-    rows = []
-    for item in sorted(items, key=lambda x: x['box'][3]):
-        b = item['box']
-        candidates = [(abs(b[3] - np.median([x['box'][3] for x in row])), row) for row in rows]
-        distance, row = min(candidates, key=lambda x: x[0]) if candidates else (float('inf'), [])
-        if distance <= .25 * max(1, b[3] - b[1]):
-            row.append(item)
-        else:
-            rows.append([item])
-    return [sorted(row, key=lambda x: (x['box'][0] + x['box'][2]) / 2) for row in rows]
-
-
-def features(crop, sift):
-    gray = cv2.cvtColor(crop, cv2.COLOR_BGR2GRAY)
-    kp, des = sift.detectAndCompute(gray, None)
-    small = cv2.resize(crop, (32, 64), interpolation=cv2.INTER_AREA)
-    lab = cv2.cvtColor(small, cv2.COLOR_BGR2LAB).astype(float) / 255
-    edge = cv2.resize(cv2.Canny(gray, 70, 140), (16, 32)).astype(float).ravel() / 255
-    return {'kp': kp, 'des': _root_sift(des), 'hist': _hue_histogram(crop), 'lab': lab,
-            'edge': edge, 'shape': gray.shape, 'sharpness': float(cv2.Laplacian(gray, cv2.CV_64F).var())}
-
-
-def pair_signals(a, b):
-    result = {'hue': float(np.minimum(a['hist'], b['hist']).sum()),
-              'pixels': float(max(0, 1 - np.abs(a['lab'] - b['lab']).mean() * 3)),
-              'logo_pixels': float(max(0, 1 - np.abs(a['lab'][:32] - b['lab'][:32]).mean() * 3)),
-              'edge': float(np.dot(a['edge'], b['edge']) / max(1e-6, np.linalg.norm(a['edge']) * np.linalg.norm(b['edge']))),
-              'inliers': 0, 'match_fraction': 0., 'layout_cover': 0.}
-    if a['des'] is not None and b['des'] is not None and len(b['des']) >= 2:
-        pairs = cv2.BFMatcher(cv2.NORM_L2).knnMatch(a['des'], b['des'], k=2)
-        good = [x[0] for x in pairs if len(x) == 2 and x[0].distance < .75 * x[1].distance]
-        if len(good) >= 4:
-            src = np.float32([a['kp'][m.queryIdx].pt for m in good])
-            dst = np.float32([b['kp'][m.trainIdx].pt for m in good])
-            _, mask = cv2.estimateAffinePartial2D(src, dst, method=cv2.RANSAC, ransacReprojThreshold=.02 * max(b['shape']))
-            if mask is not None:
-                yes = mask.ravel().astype(bool)
-                result['inliers'] = int(yes.sum())
-                result['match_fraction'] = result['inliers'] / max(1, min(len(a['kp']), len(b['kp'])))
-                if yes.sum() >= 3:
-                    result['layout_cover'] = min(float(cv2.contourArea(cv2.convexHull(src[yes])) / np.prod(a['shape'])), float(cv2.contourArea(cv2.convexHull(dst[yes])) / np.prod(b['shape'])))
-    return result
-
-
-def main():
-    OUT.mkdir(parents=True, exist_ok=True)
-    cv2.setNumThreads(1)
-    cv2.setRNGSeed(20260929)
-    label_path = ROOT / 'eval/labels/image6.json'
-    labels = json.loads(label_path.read_text())
-    source = cv2.imread(str(ROOT / labels['image']))
-    stock = str(ROOT / 'stock-lopia-clean')
-    local = LocalLane(stock)
-    text = TextLane.from_catalog(_catalog_rows(stock), stock, build_ocr_engine('vision'))
-    sift = cv2.SIFT_create(nfeatures=2000)
-    records = [f for f in labels['facings'] if f['label'] != 'exclude']
-    report = {'provenance': {'code': code_revision(), 'catalog': catalog_hash(stock), 'labels': file_hash(str(label_path)), 'image': file_hash(str(ROOT / labels['image'])), 'opencv_threads': 1, 'seed': 20260929}, 'signatures': text.signatures, 'resolutions': {}}
-    # Contact sheet is visual annotation evidence, not an inference input.
-    sheet = np.full((int(np.ceil(len(records)/12))*200, 12*110, 3), 245, np.uint8)
-    for n, f in enumerate(records):
-        x1,y1,x2,y2 = f['box']; crop = source[y1:y2,x1:x2]
-        h,w = crop.shape[:2]; ratio = min(100/w, 170/h)
-        tile = cv2.resize(crop, (max(1,int(w*ratio)), max(1,int(h*ratio))))
-        x,y = n%12*110,n//12*200
-        sheet[y+25:y+25+tile.shape[0],x:x+tile.shape[1]] = tile
-        cv2.putText(sheet, str(f['index']), (x+4,y+18), cv2.FONT_HERSHEY_SIMPLEX,.5,(0,0,0),1)
-    cv2.imwrite(str(OUT/'facings.jpg'), sheet)
-    for resolution, scale in [('full',1.),('live',1280/max(source.shape[:2]))]:
-        image = source if scale == 1 else cv2.resize(source, (round(source.shape[1]*scale),round(source.shape[0]*scale)), interpolation=cv2.INTER_AREA)
-        obs, appearances = [], {}
-        for f in records:
-            box = [round(v*scale) for v in f['box']]; x1,y1,x2,y2=box
-            crop = image[y1:y2,x1:x2]
-            start=time.perf_counter(); ev=local.evidence(crop); local_ms=(time.perf_counter()-start)*1000
-            ranked=sorted(ev.items(),key=lambda kv:local.rank_key(kv[1]),reverse=True)
-            rank=[(sku,local.confidence(e,ranked[i+1][1] if i+1<len(ranked) else Evidence())) for i,(sku,e) in enumerate(ranked)]
-            start=time.perf_counter(); read=text.read(crop); ocr_ms=(time.perf_counter()-start)*1000
-            start=time.perf_counter(); appearance=features(crop,sift); features_ms=(time.perf_counter()-start)*1000
-            appearances[f['index']]=appearance
-            obs.append({'id':f['index'],'box':box,'truth':f['label'],'local':{sku:asdict(e) for sku,e in ev.items()},'rank':rank,'text':text.scores_from_text(read),'read':read,'local_ms':local_ms,'ocr_ms':ocr_ms,'pair_features_ms':features_ms,'sharpness':appearance['sharpness']})
-        rows=rows_from_boxes(obs); pairs=[]
-        for r,row in enumerate(rows):
-            for a,b in zip(row,row[1:]):
-                start=time.perf_counter(); signals=pair_signals(appearances[a['id']],appearances[b['id']]); elapsed=(time.perf_counter()-start)*1000
-                ab,bb=a['box'],b['box']; aw,ah=ab[2]-ab[0],ab[3]-ab[1]; bw,bh=bb[2]-bb[0],bb[3]-bb[1]
-                signals.update({'aspect':min(aw/ah,bw/bh)/max(aw/ah,bw/bh),'height':min(ah,bh)/max(ah,bh),'gap':(bb[0]-ab[2])/max(aw,bw),'bottom':abs(ab[3]-bb[3])/max(ah,bh)})
-                from rebuild_catalog import bigrams
-                at,bt=bigrams(a['read']),bigrams(b['read'])
-                signals['ocr_jaccard']=len(at&bt)/max(1,len(at|bt))
-                pairs.append({'a':a['id'],'b':b['id'],'row':r,'signals':signals,'pair_ms':elapsed})
-        report['resolutions'][resolution]={'observations':obs,'rows':[[f['id'] for f in row] for row in rows],'pairs':pairs}
-        print(resolution, len(obs),'facings', len(rows),'rows', 'local/ocr ms', round(sum(x['local_ms'] for x in obs)),round(sum(x['ocr_ms'] for x in obs)),flush=True)
-    (OUT/'observations.json').write_text(json.dumps(report,ensure_ascii=False,indent=2))
-
-if __name__ == '__main__':
-    main()
diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/analysis-initial.json b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/analysis-initial.json
deleted file mode 100644
index db33846..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/analysis-initial.json
+++ /dev/null
@@ -1,15822 +0,0 @@
-{
-  "provenance": {
-    "code": "fbf31d74feb12538dc07207cb80722dac38dd438",
-    "catalog": "1bc3c1d10087f92192b8081a007fa72b4f091446c273a0ab3825cbed9bdf602c",
-    "labels": "6fc1a220a97c75666882b4bcd9dc102ed08c8924677b97d615850a0983286c1a",
-    "image": "a08b89ba37d27c9d5a81bcd56bcd042e6baf54d2f182bbaaf47e97f14c9cd525",
-    "opencv_threads": 1,
-    "seed": 20260929
-  },
-  "resolutions": {
-    "full": {
-      "pair_counts": {
-        "same": 72,
-        "sibling": 3,
-        "unrelated": 8
-      },
-      "signals": {
-        "hue": {
-          "thresholds_leave_row_out": {
-            "0": 0.8051810264587402,
-            "1": 0.6459402740001678,
-            "2": 0.8059877455234528,
-            "3": 0.6459402740001678,
-            "4": 0.6459402740001678,
-            "5": 0.6459402740001678,
-            "6": 0.6459402740001678
-          },
-          "same_vs_sibling_auc": 0.9768518518518519,
-          "same_vs_unrelated_auc": 0.9670138888888888,
-          "classes": {
-            "same": {
-              "n": 72,
-              "p50": 0.9042529761791229
-            },
-            "sibling": {
-              "n": 3,
-              "p50": 0.4266957640647888
-            },
-            "unrelated": {
-              "n": 8,
-              "p50": 0.4369388669729233
-            }
-          },
-          "held_row_confusion": {
-            "tp": 58,
-            "fp": 1,
-            "tn": 10,
-            "fn": 14,
-            "precision": 0.9830508474576272,
-            "recall": 0.8055555555555556,
-            "boundary_accuracy": 0.8192771084337349,
-            "boundary_recall": 0.9090909090909091
-          }
-        },
-        "pixels": {
-          "thresholds_leave_row_out": {
-            "0": 0.6890510110294118,
-            "1": 0.6924278109681372,
-            "2": 0.6996045879289216,
-            "3": 0.6924278109681372,
-            "4": 0.6924278109681372,
-            "5": 0.6924278109681372,
-            "6": 0.6924278109681372
-          },
-          "same_vs_sibling_auc": 0.9444444444444444,
-          "same_vs_unrelated_auc": 0.9791666666666666,
-          "classes": {
-            "same": {
-              "n": 72,
-              "p50": 0.7837574678308823
-            },
-            "sibling": {
-              "n": 3,
-              "p50": 0.6807387408088236
-            },
-            "unrelated": {
-              "n": 8,
-              "p50": 0.5741871553308824
-            }
-          },
-          "held_row_confusion": {
-            "tp": 64,
-            "fp": 0,
-            "tn": 11,
-            "fn": 8,
-            "precision": 1.0,
-            "recall": 0.8888888888888888,
-            "boundary_accuracy": 0.9036144578313253,
-            "boundary_recall": 1.0
-          }
-        },
-        "logo_pixels": {
-          "thresholds_leave_row_out": {
-            "0": 0.6920266544117648,
-            "1": 0.6793887867647059,
-            "2": 0.6834846047794119,
-            "3": 0.6834846047794119,
-            "4": 0.6834846047794119,
-            "5": 0.6834846047794119,
-            "6": 0.6834846047794119
-          },
-          "same_vs_sibling_auc": 0.9675925925925926,
-          "same_vs_unrelated_auc": 0.9809027777777778,
-          "classes": {
-            "same": {
-              "n": 72,
-              "p50": 0.7673636642156862
-            },
-            "sibling": {
-              "n": 3,
-              "p50": 0.6709060968137255
-            },
-            "unrelated": {
-              "n": 8,
-              "p50": 0.5698337928921569
-            }
-          },
-          "held_row_confusion": {
-            "tp": 67,
-            "fp": 0,
-            "tn": 11,
-            "fn": 5,
-            "precision": 1.0,
-            "recall": 0.9305555555555556,
-            "boundary_accuracy": 0.9397590361445783,
-            "boundary_recall": 1.0
-          }
-        },
-        "edge": {
-          "thresholds_leave_row_out": {
-            "0": 0.3012076668309146,
-            "1": 0.3012076668309146,
-            "2": 0.3577370089391758,
-            "3": 0.3012076668309146,
-            "4": 0.46052033305713597,
-            "5": 0.3022930292906867,
-            "6": 0.3577370089391758
-          },
-          "same_vs_sibling_auc": 0.07407407407407407,
-          "same_vs_unrelated_auc": 0.6545138888888888,
-          "classes": {
-            "same": {
-              "n": 72,
-              "p50": 0.3420023907685241
-            },
-            "sibling": {
-              "n": 3,
-              "p50": 0.44561870932992864
-            },
-            "unrelated": {
-              "n": 8,
-              "p50": 0.29979940923253456
-            }
-          },
-          "held_row_confusion": {
-            "tp": 36,
-            "fp": 5,
-            "tn": 6,
-            "fn": 36,
-            "precision": 0.8780487804878049,
-            "recall": 0.5,
-            "boundary_accuracy": 0.5060240963855421,
-            "boundary_recall": 0.5454545454545454
-          }
-        },
-        "inliers": {
-          "thresholds_leave_row_out": {
-            "0": 1.5,
-            "1": 1.5,
-            "2": 1.5,
-            "3": 1.5,
-            "4": 1.5,
-            "5": 1.5,
-            "6": 1.5
-          },
-          "same_vs_sibling_auc": 0.9444444444444444,
-          "same_vs_unrelated_auc": 0.9444444444444444,
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diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/analysis-unguarded.json b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/analysis-unguarded.json
deleted file mode 100644
index 5c53c4f..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/analysis-unguarded.json
+++ /dev/null
@@ -1,10672 +0,0 @@
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\ No newline at end of file
diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/analysis.json b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/analysis.json
deleted file mode 100644
index 2b2c9d9..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/analysis.json
+++ /dev/null
@@ -1,16506 +0,0 @@
-{
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diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/anytime-events.json b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/anytime-events.json
deleted file mode 100644
index c3bbfae..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/anytime-events.json
+++ /dev/null
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diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/anytime-policies.json b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/anytime-policies.json
deleted file mode 100644
index 63faf48..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/anytime-policies.json
+++ /dev/null
@@ -1,725 +0,0 @@
-{
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-      "wrong_sibling_facing_ms": 0.0,
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-      "horizon_ms": 3323.5481249867007
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-        "wrong": 7,
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-          "wrong_catalog": 4,
-          "blank_ratio": 0.4495412844036697,
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-          "corrected_wrong": 0,
-          "damaged_correct": 0
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-          "facings": 109,
-          "catalog_facings": 64,
-          "shown": 60,
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-          "visible_coverage": 0.5504587155963303,
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-          "damaged_correct": 0
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-          "correct": 51,
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-          "visible_coverage": 0.5688073394495413,
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-          "damaged_correct": 0
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-      "first_any_ms": 13.799707987345755,
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-      "time_to_final_correct_p50_ms": 73.84883298072964,
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-      "blank_facing_ms": 172579.93904012255,
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-        "correct": 55,
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-        "wrong": 9,
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-    }
-  }
-}
\ No newline at end of file
diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/baseline.json b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/baseline.json
deleted file mode 100644
index c92f6f9..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/baseline.json
+++ /dev/null
@@ -1,399 +0,0 @@
-{
- "image": "add/image6.jpg",
- "stock_dir": "stock-lopia-clean",
- "labels": "eval/labels/image6.json",
- "provenance": {
-  "catalog_hash": "1bc3c1d10087f92192b8081a007fa72b4f091446c273a0ab3825cbed9bdf602c",
-  "labels_hash": "6fc1a220a97c75666882b4bcd9dc102ed08c8924677b97d615850a0983286c1a",
-  "image_hash": "a08b89ba37d27c9d5a81bcd56bcd042e6baf54d2f182bbaaf47e97f14c9cd525",
-  "code_revision": "fbf31d74feb12538dc07207cb80722dac38dd438"
- },
- "facings": 111,
- "leakage": {
-  "excluded": 0,
-  "labels": []
- },
- "hub_share_limit": 0.3,
- "enrollment": {
-  "catalog_skus": [
-   "sku_l01",
-   "sku_l02",
-   "sku_l03",
-   "sku_l04",
-   "sku_l05",
-   "sku_l06",
-   "sku_l07",
-   "sku_l08",
-   "sku_l09",
-   "sku_l10",
-   "sku_l11",
-   "sku_l12"
-  ],
-  "enrolled_skus": [
-   "sku_l01",
-   "sku_l02",
-   "sku_l03",
-   "sku_l05",
-   "sku_l06",
-   "sku_l09",
-   "sku_l10",
-   "sku_l11"
-  ],
-  "unenrolled_skus": {
-   "sku_l07": 6,
-   "sku_l12": 3,
-   "sku_l04": 1,
-   "sku_l08": 1
-  },
-  "facings_without_enrollment": 11,
-  "scored_facings": 109
- },
- "results": {
-  "full": [
-   {
-    "lane": "van",
-    "accept": 1.0,
-    "margin": 0.15,
-    "in_catalog": 64,
-    "out_catalog": 45,
-    "enrolled": 53,
-    "correct": 44,
-    "wrong": 65,
-    "rejected": 0,
-    "catalog_ceiling": 0.8281,
-    "identity_ceiling": 0.8302,
-    "decision_ceiling": 0.8302,
-    "end_to_end_coverage": 0.6875,
-    "false_accepts": 65,
-    "unknown_false_accepts": 45,
-    "unenrolled_false_accepts": 11,
-    "false_accept_rate": 1.0,
-    "near_unknown_false_accept_rate": 1.0,
-    "precision": 0.4037,
-    "max_sku_share": 0.1927,
-    "hub_sku": "sku_l05",
-    "hub_alarm": false,
-    "histogram": {
-     "sku_l05": 21,
-     "sku_l09": 20,
-     "sku_l03": 17,
-     "sku_l02": 13,
-     "sku_l11": 12,
-     "sku_l10": 12,
-     "sku_l06": 8,
-     "sku_l01": 6
-    },
-    "per_sku": {
-     "sku_l01": {
-      "correct": 5,
-      "total": 5
-     },
-     "sku_l02": {
-      "correct": 4,
-      "total": 4
-     },
-     "sku_l03": {
-      "correct": 4,
-      "total": 4
-     },
-     "sku_l04": {
-      "correct": 0,
-      "total": 1
-     },
-     "sku_l05": {
-      "correct": 3,
-      "total": 3
-     },
-     "sku_l06": {
-      "correct": 3,
-      "total": 4
-     },
-     "sku_l07": {
-      "correct": 0,
-      "total": 6
-     },
-     "sku_l08": {
-      "correct": 0,
-      "total": 1
-     },
-     "sku_l09": {
-      "correct": 13,
-      "total": 13
-     },
-     "sku_l10": {
-      "correct": 9,
-      "total": 11
-     },
-     "sku_l11": {
-      "correct": 3,
-      "total": 9
-     },
-     "sku_l12": {
-      "correct": 0,
-      "total": 3
-     }
-    },
-    "ms_per_crop": 19.0
-   },
-   {
-    "lane": "local",
-    "accept": 4.0,
-    "margin": 0.15,
-    "in_catalog": 64,
-    "out_catalog": 45,
-    "enrolled": 53,
-    "correct": 42,
-    "wrong": 2,
-    "rejected": 65,
-    "catalog_ceiling": 0.8281,
-    "identity_ceiling": 0.9245,
-    "decision_ceiling": 0.717,
-    "end_to_end_coverage": 0.6562,
-    "false_accepts": 2,
-    "unknown_false_accepts": 2,
-    "unenrolled_false_accepts": 0,
-    "false_accept_rate": 0.0444,
-    "near_unknown_false_accept_rate": 0.0,
-    "precision": 0.9545,
-    "max_sku_share": 0.2273,
-    "hub_sku": "sku_l09",
-    "hub_alarm": false,
-    "histogram": {
-     "sku_l09": 10,
-     "sku_l10": 9,
-     "sku_l01": 5,
-     "sku_l03": 5,
-     "sku_l07": 5,
-     "sku_l06": 4,
-     "sku_l05": 3,
-     "sku_l11": 2,
-     "sku_l02": 1
-    },
-    "per_sku": {
-     "sku_l01": {
-      "correct": 5,
-      "total": 5
-     },
-     "sku_l02": {
-      "correct": 1,
-      "total": 4
-     },
-     "sku_l03": {
-      "correct": 4,
-      "total": 4
-     },
-     "sku_l04": {
-      "correct": 0,
-      "total": 1
-     },
-     "sku_l05": {
-      "correct": 3,
-      "total": 3
-     },
-     "sku_l06": {
-      "correct": 4,
-      "total": 4
-     },
-     "sku_l07": {
-      "correct": 4,
-      "total": 6
-     },
-     "sku_l08": {
-      "correct": 0,
-      "total": 1
-     },
-     "sku_l09": {
-      "correct": 10,
-      "total": 13
-     },
-     "sku_l10": {
-      "correct": 9,
-      "total": 11
-     },
-     "sku_l11": {
-      "correct": 2,
-      "total": 9
-     },
-     "sku_l12": {
-      "correct": 0,
-      "total": 3
-     }
-    },
-    "ms_per_crop": 29.1
-   }
-  ],
-  "live": [
-   {
-    "lane": "van",
-    "accept": 1.0,
-    "margin": 0.15,
-    "in_catalog": 64,
-    "out_catalog": 45,
-    "enrolled": 53,
-    "correct": 44,
-    "wrong": 65,
-    "rejected": 0,
-    "catalog_ceiling": 0.8281,
-    "identity_ceiling": 0.8302,
-    "decision_ceiling": 0.8302,
-    "end_to_end_coverage": 0.6875,
-    "false_accepts": 65,
-    "unknown_false_accepts": 45,
-    "unenrolled_false_accepts": 11,
-    "false_accept_rate": 1.0,
-    "near_unknown_false_accept_rate": 1.0,
-    "precision": 0.4037,
-    "max_sku_share": 0.2018,
-    "hub_sku": "sku_l11",
-    "hub_alarm": false,
-    "histogram": {
-     "sku_l11": 22,
-     "sku_l03": 17,
-     "sku_l05": 15,
-     "sku_l06": 14,
-     "sku_l09": 14,
-     "sku_l10": 11,
-     "sku_l02": 10,
-     "sku_l01": 6
-    },
-    "per_sku": {
-     "sku_l01": {
-      "correct": 5,
-      "total": 5
-     },
-     "sku_l02": {
-      "correct": 3,
-      "total": 4
-     },
-     "sku_l03": {
-      "correct": 4,
-      "total": 4
-     },
-     "sku_l04": {
-      "correct": 0,
-      "total": 1
-     },
-     "sku_l05": {
-      "correct": 3,
-      "total": 3
-     },
-     "sku_l06": {
-      "correct": 2,
-      "total": 4
-     },
-     "sku_l07": {
-      "correct": 0,
-      "total": 6
-     },
-     "sku_l08": {
-      "correct": 0,
-      "total": 1
-     },
-     "sku_l09": {
-      "correct": 10,
-      "total": 13
-     },
-     "sku_l10": {
-      "correct": 9,
-      "total": 11
-     },
-     "sku_l11": {
-      "correct": 8,
-      "total": 9
-     },
-     "sku_l12": {
-      "correct": 0,
-      "total": 3
-     }
-    },
-    "ms_per_crop": 6.5
-   },
-   {
-    "lane": "local",
-    "accept": 4.0,
-    "margin": 0.15,
-    "in_catalog": 64,
-    "out_catalog": 45,
-    "enrolled": 53,
-    "correct": 36,
-    "wrong": 1,
-    "rejected": 72,
-    "catalog_ceiling": 0.8281,
-    "identity_ceiling": 0.9057,
-    "decision_ceiling": 0.6038,
-    "end_to_end_coverage": 0.5625,
-    "false_accepts": 1,
-    "unknown_false_accepts": 1,
-    "unenrolled_false_accepts": 0,
-    "false_accept_rate": 0.0222,
-    "near_unknown_false_accept_rate": 0.0,
-    "precision": 0.973,
-    "max_sku_share": 0.2432,
-    "hub_sku": "sku_l09",
-    "hub_alarm": false,
-    "histogram": {
-     "sku_l09": 9,
-     "sku_l10": 8,
-     "sku_l07": 5,
-     "sku_l01": 4,
-     "sku_l03": 3,
-     "sku_l11": 3,
-     "sku_l05": 2,
-     "sku_l06": 2,
-     "sku_l02": 1
-    },
-    "per_sku": {
-     "sku_l01": {
-      "correct": 4,
-      "total": 5
-     },
-     "sku_l02": {
-      "correct": 1,
-      "total": 4
-     },
-     "sku_l03": {
-      "correct": 3,
-      "total": 4
-     },
-     "sku_l04": {
-      "correct": 0,
-      "total": 1
-     },
-     "sku_l05": {
-      "correct": 2,
-      "total": 3
-     },
-     "sku_l06": {
-      "correct": 2,
-      "total": 4
-     },
-     "sku_l07": {
-      "correct": 4,
-      "total": 6
-     },
-     "sku_l08": {
-      "correct": 0,
-      "total": 1
-     },
-     "sku_l09": {
-      "correct": 9,
-      "total": 13
-     },
-     "sku_l10": {
-      "correct": 8,
-      "total": 11
-     },
-     "sku_l11": {
-      "correct": 3,
-      "total": 9
-     },
-     "sku_l12": {
-      "correct": 0,
-      "total": 3
-     }
-    },
-    "ms_per_crop": 19.8
-   }
-  ]
- }
-}
\ No newline at end of file
diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/facings.jpg b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/facings.jpg
deleted file mode 100644
index 70158c6..0000000
Binary files a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/facings.jpg and /dev/null differ
diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/far-unknown-lines.json b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/far-unknown-lines.json
deleted file mode 100644
index 5504183..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/far-unknown-lines.json
+++ /dev/null
@@ -1,27 +0,0 @@
-{
- "4.0": {
-  "accepts": 3,
-  "crops": 4580,
-  "rate": 0.0006550218340611354
- },
- "2.0": {
-  "accepts": 8,
-  "crops": 4580,
-  "rate": 0.0017467248908296944
- },
- "1.0": {
-  "accepts": 45,
-  "crops": 4580,
-  "rate": 0.009825327510917031
- },
- "0.5": {
-  "accepts": 83,
-  "crops": 4580,
-  "rate": 0.018122270742358077
- },
- "0.0": {
-  "accepts": 115,
-  "crops": 4580,
-  "rate": 0.025109170305676855
- }
-}
\ No newline at end of file
diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/far-unknown-policies.json b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/far-unknown-policies.json
deleted file mode 100644
index 81b6c93..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/far-unknown-policies.json
+++ /dev/null
@@ -1,157 +0,0 @@
-{
- "question": "False accepts per named analyze policy on unrelated shelf crops, analyze.decide on the sweep_accept_lines cache.",
- "method": {
-  "decision": "analyze.decide(o, policy, signatures) - the frozen analysis's own call",
-  "policies": {
-   "strict": 4.0,
-   "balanced": 2.0,
-   "coverage-first": 0.5,
-   "near-argmax": 0.0
-  },
-  "gate_for_comparison": "identity_lanes.DecisionPolicy(local_accept=threshold, signatures)",
-  "text_scores": "TextLane.scores_from_text on the cached OCR read",
-  "fallback": "analyze.decide's near-argmax extra fallback only: keep rank[0] when the gate refused, unless the read contradicts that row's size or the text prefers another row"
- },
- "provenance": {
-  "cache": {
-   "path": "data/evaluations/2026-09-29-session/scratch/accept_line_cache.pkl",
-   "sha256": "1abf0312d354f9f00af6d5a339d0edde0d584c1beee1dab46d04966caf6e637a",
-   "bytes": 89680,
-   "version": 3,
-   "detect_conf": 0.1,
-   "records": [
-    "version",
-    "conf",
-    "photos",
-    "shelf_full",
-    "shelf_live",
-    "distractors"
-   ],
-   "not_recorded": [
-    "stock_dir",
-    "catalog_hash",
-    "code_revision",
-    "source image paths or hashes",
-    "per-photo crop counts"
-   ],
-   "note": "The cache attests no catalog, so the catalog match below is read off the current worktree, not out of the cache. The read is unpickled, not rebuilt."
-  },
-  "catalog": {
-   "dir": "stock-lopia-clean",
-   "catalog_hash": "1bc3c1d10087f92192b8081a007fa72b4f091446c273a0ab3825cbed9bdf602c",
-   "matches_observations": true,
-   "observations_catalog_hash": "1bc3c1d10087f92192b8081a007fa72b4f091446c273a0ab3825cbed9bdf602c",
-   "observations_code_revision": "fbf31d74feb12538dc07207cb80722dac38dd438"
-  },
-  "code_revision": "fbf31d74feb12538dc07207cb80722dac38dd438",
-  "signatures": {
-   "rows": 12,
-   "sha256": "3e2388f27ae5796155656c5aa10094fbf1012766b7cee088224573a84b2bb584"
-  }
- },
- "counts": {
-  "photos": 64,
-  "crops": 4580,
-  "crops_with_a_local_candidate": 155,
-  "crops_with_no_local_candidate": 4425,
-  "crops_with_a_raw_read": 1546,
-  "crops_scored_by_text": 1185
- },
- "policies": {
-  "strict": {
-   "local_accept": 4.0,
-   "fallback": false,
-   "accepts": 3,
-   "gate_accepts": 3,
-   "fallback_accepts": 0,
-   "by_source": {
-    "local": 0,
-    "text": 3
-   },
-   "rate": 0.0006550218340611354,
-   "rate_percent": 0.0655,
-   "crops_with_a_local_candidate": 155
-  },
-  "balanced": {
-   "local_accept": 2.0,
-   "fallback": false,
-   "accepts": 8,
-   "gate_accepts": 8,
-   "fallback_accepts": 0,
-   "by_source": {
-    "local": 5,
-    "text": 3
-   },
-   "rate": 0.0017467248908296944,
-   "rate_percent": 0.1747,
-   "crops_with_a_local_candidate": 155
-  },
-  "coverage-first": {
-   "local_accept": 0.5,
-   "fallback": false,
-   "accepts": 83,
-   "gate_accepts": 83,
-   "fallback_accepts": 0,
-   "by_source": {
-    "local": 80,
-    "text": 3
-   },
-   "rate": 0.018122270742358077,
-   "rate_percent": 1.8122,
-   "crops_with_a_local_candidate": 155
-  },
-  "near-argmax": {
-   "local_accept": 0.0,
-   "fallback": true,
-   "accepts": 119,
-   "gate_accepts": 115,
-   "fallback_accepts": 4,
-   "by_source": {
-    "local": 112,
-    "text": 3
-   },
-   "rate": 0.025982532751091702,
-   "rate_percent": 2.5983,
-   "crops_with_a_local_candidate": 155
-  }
- },
- "cross_check_far_unknown_lines": {
-  "source": "evidence/far-unknown-lines.json",
-  "note": "accept-threshold cells only, no fallback; producer script not in the tree",
-  "cells": {
-   "strict": {
-    "lines_accepts": 3,
-    "lines_crops": 4580,
-    "here_gate_accepts": 3,
-    "agrees": true
-   },
-   "balanced": {
-    "lines_accepts": 8,
-    "lines_crops": 4580,
-    "here_gate_accepts": 8,
-    "agrees": true
-   },
-   "coverage-first": {
-    "lines_accepts": 83,
-    "lines_crops": 4580,
-    "here_gate_accepts": 83,
-    "agrees": true
-   },
-   "near-argmax": {
-    "lines_accepts": 115,
-    "lines_crops": 4580,
-    "here_gate_accepts": 115,
-    "agrees": true
-   }
-  }
- },
- "limitations": [
-  "The cache records no catalog, code revision or image list, so 'same catalog as the frozen observations' is read off the current worktree, not attested by the cache.",
-  "Only 155 of 4580 crops produced a local candidate; the other 4425 are decided by the text lane alone or refused before any gate applies, so the accept floor is exercised on a small minority of the rows and the rates are dominated by empty evidence.",
-  "The catalog is 12 rows from one store. A false-accept rate is a function of catalog size, so these numbers do not transfer to a larger or differently-stocked catalog.",
-  "Crops come from one detection pass at detect_conf 0.1 on 1280-long-side frames, so the crop population is set by the detector, not only by the identity gate.",
-  "The cache has no per-photo breakdown, so no photo-level rate and no check that one image is not carrying the whole count.",
-  "The near-argmax fallback is a research construct in analyze.py, not shipped code; its row prices a policy that does not exist on the server yet.",
-  "far-unknown-lines.json's producer is not in the tree, so agreement with it is corroboration, not verification."
- ]
-}
diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/frontier.svg b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/frontier.svg
deleted file mode 100644
index 5e8b84c..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/frontier.svg
+++ /dev/null
@@ -1 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" width="1000" height="480" viewBox="0 0 1000 480"><rect width="1000" height="480" fill="white"/><text x="70" y="30" font-size="18">full: visible coverage / precision</text><path d="M70,50 V405 H460" fill="none" stroke="black"/><text x="60" y="430" font-size="12">0%</text><text x="30" y="405" font-size="12">0%</text><text x="157.5" y="430" font-size="12">25%</text><text x="30" y="317.5" font-size="12">25%</text><text x="255.0" y="430" font-size="12">50%</text><text x="30" y="230.0" font-size="12">50%</text><text x="352.5" y="430" font-size="12">75%</text><text x="30" y="142.5" font-size="12">75%</text><text x="450" y="430" font-size="12">100%</text><text x="30" y="55" font-size="12">100%</text><circle cx="227.43119266055047" cy="70.90909090909088" r="5" fill="white" stroke="#475569"><title>strict/individual: 44/109 shown, 42/64 correct, 2 false</title></circle><circle cx="266.7889908256881" cy="67.72727272727275" r="5" fill="#475569" stroke="#475569"><title>strict/guarded: 55/109 shown, 53/64 correct, 2 false</title></circle><circle cx="256.0550458715596" cy="81.9230769230769" r="5" fill="white" stroke="#2563eb"><title>balanced/individual: 52/109 shown, 48/64 correct, 4 false</title></circle><circle cx="281.10091743119267" cy="78.72881355932202" r="5" fill="#2563eb" stroke="#2563eb"><title>balanced/guarded: 59/109 shown, 55/64 correct, 4 false</title></circle><circle cx="288.25688073394497" cy="106.63934426229508" r="5" fill="white" stroke="#059669"><title>coverage-first/individual: 61/109 shown, 52/64 correct, 9 false</title></circle><circle cx="295.41284403669727" cy="99.44444444444446" r="5" fill="#059669" stroke="#059669"><title>coverage-first/guarded: 63/109 shown, 55/64 correct, 8 false</title></circle><circle cx="306.1467889908257" cy="129.24242424242425" r="5" fill="white" stroke="#dc2626"><title>near-argmax/individual: 66/109 shown, 52/64 correct, 14 false</title></circle><circle cx="313.302752293578" cy="121.91176470588238" r="5" fill="#dc2626" stroke="#dc2626"><title>near-argmax/guarded: 68/109 shown, 55/64 correct, 13 false</title></circle><text x="70" y="458" font-size="12">Any labels / 109; filled = guarded groups, hollow = individual</text><text x="560" y="30" font-size="18">live: visible coverage / precision</text><path d="M560,50 V405 H950" fill="none" stroke="black"/><text x="550" y="430" font-size="12">0%</text><text x="520" y="405" font-size="12">0%</text><text x="647.5" y="430" font-size="12">25%</text><text x="520" y="317.5" font-size="12">25%</text><text x="745.0" y="430" font-size="12">50%</text><text x="520" y="230.0" font-size="12">50%</text><text x="842.5" y="430" font-size="12">75%</text><text x="520" y="142.5" font-size="12">75%</text><text x="940" y="430" font-size="12">100%</text><text x="520" y="55" font-size="12">100%</text><circle cx="692.3853211009175" cy="64.45945945945942" r="5" fill="white" stroke="#475569"><title>strict/individual: 37/109 shown, 36/64 correct, 1 false</title></circle><circle cx="742.4770642201835" cy="61.86274509803923" r="5" fill="#475569" stroke="#475569"><title>strict/guarded: 51/109 shown, 50/64 correct, 1 false</title></circle><circle cx="699.5412844036697" cy="63.974358974359006" r="5" fill="white" stroke="#2563eb"><title>balanced/individual: 39/109 shown, 38/64 correct, 1 false</title></circle><circle cx="746.0550458715596" cy="61.730769230769226" r="5" fill="#2563eb" stroke="#2563eb"><title>balanced/guarded: 52/109 shown, 51/64 correct, 1 false</title></circle><circle cx="753.2110091743119" cy="100.37037037037038" r="5" fill="white" stroke="#059669"><title>coverage-first/individual: 54/109 shown, 47/64 correct, 7 false</title></circle><circle cx="778.256880733945" cy="89.42622950819674" r="5" fill="#059669" stroke="#059669"><title>coverage-first/guarded: 61/109 shown, 55/64 correct, 6 false</title></circle><circle cx="767.5229357798165" cy="115.34482758620692" r="5" fill="white" stroke="#dc2626"><title>near-argmax/individual: 58/109 shown, 48/64 correct, 10 false</title></circle><circle cx="788.9908256880734" cy="104.21875" r="5" fill="#dc2626" stroke="#dc2626"><title>near-argmax/guarded: 64/109 shown, 55/64 correct, 9 false</title></circle><text x="560" y="458" font-size="12">Any labels / 109; filled = guarded groups, hollow = individual</text></svg>
\ No newline at end of file
diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/initial-replays/replay-hanzomon-supermarket-pan-132-152-8fps-0.5x-0.json b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/initial-replays/replay-hanzomon-supermarket-pan-132-152-8fps-0.5x-0.json
deleted file mode 100644
index fa2a669..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/initial-replays/replay-hanzomon-supermarket-pan-132-152-8fps-0.5x-0.json
+++ /dev/null
@@ -1,5301 +0,0 @@
-{
- "summary": {
-  "source": "data/external-eval/tier3-sources/media/hanzomon-supermarket-pan-132-152.mp4",
-  "fps": 8,
-  "rate": 0.5,
-  "trial": 0,
-  "input_frames": 40,
-  "sent": 40,
-  "processed": 40,
-  "not_returned": 0,
-  "elapsed_s": 11.78,
-  "server_cpu_s": 63.94,
-  "server_cpu_pct_one_core": 545.9,
-  "rtt_ms": {
-   "p50": 65.3,
-   "p95": 102.3
-  },
-  "stages_ms": {
-   "decode": {
-    "p50": 2.28,
-    "p95": 2.62
-   },
-   "detect": {
-    "p50": 57.9,
-    "p95": 85.93
-   },
-   "track": {
-    "p50": 1.64,
-    "p95": 14.19
-   },
-   "total": {
-    "p50": 61.09,
-    "p95": 97.65
-   }
-  },
-  "max_tracks": 17,
-  "ever_labelled_track_ids": 0,
-  "first_visible_ms": null,
-  "label_flips": 0,
-  "errors": [],
-  "latest_stats": {
-   "crops": {
-    "requested": 0,
-    "accepted": 0,
-    "rejected": 0
-   },
-   "frames_received": 40,
-   "frames_processed": 40,
-   "frames_dropped": 0,
-   "server_fps": 4.02,
-   "classify_pending": 0,
-   "classify_failures": 0,
-   "classify_ms_p50": null
-  }
- },
- "timelines": [
-  {
-   "frame_id": 1,
-   "received_ms": 95.1,
-   "sent_ms": 74.1,
-   "timing_ms": {
-    "decode": 1.28,
-    "detect": 18.18,
-    "track": 0.37,
-    "total": 19.83
-   },
-   "stats": {
-    "crops": {
-     "requested": 0,
-     "accepted": 0,
-     "rejected": 0
-    },
-    "frames_received": 1,
-    "frames_processed": 1,
-    "frames_dropped": 0,
-    "server_fps": 0.0,
-    "classify_pending": 0,
-    "classify_failures": 0,
-    "classify_ms_p50": null
-   },
-   "tracks": [
-    {
-     "id": 1,
-     "sku": null,
-     "state": "unconfirmed",
-     "box": [
-      424.0,
-      87.0,
-      629.0,
-      329.0
-     ],
-     "source": "frame"
-    },
-    {
-     "id": 2,
-     "sku": null,
-     "state": "unconfirmed",
-     "box": [
-      38.0,
-      168.0,
-      240.0,
-      359.0
-     ],
-     "source": "frame"
-    },
-    {
-     "id": 3,
-     "sku": null,
-     "state": "unconfirmed",
-     "box": [
-      676.0,
-      95.0,
-      886.0,
-      308.0
-     ],
-     "source": "frame"
-    },
-    {
-     "id": 4,
-     "sku": null,
-     "state": "unconfirmed",
-     "box": [
-      1037.0,
-      522.0,
-      1271.0,
-      713.0
-     ],
-     "source": "frame"
-    },
-    {
-     "id": 5,
-     "sku": null,
-     "state": "unconfirmed",
-     "box": [
-      292.0,
-      462.0,
-      577.0,
-      718.0
-     ],
-     "source": "frame"
-    },
-    {
-     "id": 6,
-     "sku": null,
-     "state": "unconfirmed",
-     "box": [
-      939.0,
-      0.0,
-      1267.0,
-      281.0
-     ],
-     "source": "frame"
-    },
-    {
-     "id": 7,
-     "sku": null,
-     "state": "unconfirmed",
-     "box": [
-      300.0,
-      608.0,
-      569.0,
-      713.0
-     ],
-     "source": "frame"
-    },
-    {
-     "id": 8,
-     "sku": null,
-     "state": "unconfirmed",
-     "box": [
-      705.0,
-      434.0,
-      949.0,
-      656.0
-     ],
-     "source": "frame"
-    },
-    {
-     "id": 9,
-     "sku": null,
-     "state": "unconfirmed",
-     "box": [
-      0.0,
-      178.0,
-      31.0,
-      362.0
-     ],
-     "source": "frame"
-    },
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diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/initial-replays/replay-hanzomon-supermarket-pan-132-152-8fps-0.5x-1.json b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/initial-replays/replay-hanzomon-supermarket-pan-132-152-8fps-0.5x-1.json
deleted file mode 100644
index d5b08ce..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/initial-replays/replay-hanzomon-supermarket-pan-132-152-8fps-0.5x-1.json
+++ /dev/null
@@ -1,5301 +0,0 @@
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diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/initial-replays/replay-hanzomon-supermarket-pan-132-152-8fps-1x-0.json b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/initial-replays/replay-hanzomon-supermarket-pan-132-152-8fps-1x-0.json
deleted file mode 100644
index 9361e1c..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/initial-replays/replay-hanzomon-supermarket-pan-132-152-8fps-1x-0.json
+++ /dev/null
@@ -1,5301 +0,0 @@
-{
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diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/initial-replays/replay-hanzomon-supermarket-pan-132-152-8fps-1x-1.json b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/initial-replays/replay-hanzomon-supermarket-pan-132-152-8fps-1x-1.json
deleted file mode 100644
index c4880ed..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/initial-replays/replay-hanzomon-supermarket-pan-132-152-8fps-1x-1.json
+++ /dev/null
@@ -1,5301 +0,0 @@
-{
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diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/initial-replays/replay-image6-15fps-0.5x-0.json b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/initial-replays/replay-image6-15fps-0.5x-0.json
deleted file mode 100644
index 35921b1..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/initial-replays/replay-image6-15fps-0.5x-0.json
+++ /dev/null
@@ -1,69815 +0,0 @@
-{
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-  "rate": 0.5,
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\ No newline at end of file
diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/initial-replays/replay-image6-15fps-1x-0.json b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/initial-replays/replay-image6-15fps-1x-0.json
deleted file mode 100644
index e63f5eb..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/initial-replays/replay-image6-15fps-1x-0.json
+++ /dev/null
@@ -1,36395 +0,0 @@
-{
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-  "rate": 1,
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-    "p95": 220.09
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-  "ever_labelled_track_ids": 40,
-  "first_visible_ms": 645.1856669737026,
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-  "errors": [],
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-   "classify_failures": 0,
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-  }
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-}
\ No newline at end of file
diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/initial-replays/replay-image6-8fps-0.5x-0.json b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/initial-replays/replay-image6-8fps-0.5x-0.json
deleted file mode 100644
index 3cc9ed6..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/initial-replays/replay-image6-8fps-0.5x-0.json
+++ /dev/null
@@ -1,38140 +0,0 @@
-{
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-  "fps": 8,
-  "rate": 0.5,
-  "trial": 0,
-  "input_frames": 40,
-  "sent": 40,
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-    "p95": 10.48
-   },
-   "total": {
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-    "p95": 122.88
-   }
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-  "ever_labelled_track_ids": 57,
-  "first_visible_ms": 623.0825000675395,
-  "label_flips": 0,
-  "errors": [],
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-    "accepted": 0,
-    "rejected": 0
-   },
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-   "frames_processed": 40,
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-   "classify_pending": 3,
-   "classify_failures": 0,
-   "classify_ms_p50": null
-  }
- },
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-    "track": 2.02,
-    "total": 25.95
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-}
\ No newline at end of file
diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/initial-replays/replay-image6-8fps-0.5x-1.json b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/initial-replays/replay-image6-8fps-0.5x-1.json
deleted file mode 100644
index be88bfd..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/initial-replays/replay-image6-8fps-0.5x-1.json
+++ /dev/null
@@ -1,38112 +0,0 @@
-{
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-  "fps": 8,
-  "rate": 0.5,
-  "trial": 1,
-  "input_frames": 40,
-  "sent": 40,
-  "processed": 40,
-  "not_returned": 0,
-  "elapsed_s": 11.9,
-  "server_cpu_s": 69.78,
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-  "rtt_ms": {
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-   "p95": 197.2
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-    "p95": 10.12
-   },
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-    "p95": 91.23
-   },
-   "track": {
-    "p50": 5.53,
-    "p95": 24.17
-   },
-   "total": {
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-    "p95": 158.8
-   }
-  },
-  "max_tracks": 80,
-  "ever_labelled_track_ids": 53,
-  "first_visible_ms": 723.3483329182491,
-  "label_flips": 0,
-  "errors": [],
-  "latest_stats": {
-   "crops": {
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-    "accepted": 0,
-    "rejected": 0
-   },
-   "frames_received": 40,
-   "frames_processed": 40,
-   "frames_dropped": 0,
-   "server_fps": 4.0,
-   "classify_pending": 5,
-   "classify_failures": 0,
-   "classify_ms_p50": null
-  }
- },
- "timelines": [
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-   "sent_ms": 65.7,
-   "timing_ms": {
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-    "detect": 26.06,
-    "track": 2.33,
-    "total": 35.25
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-    "frames_processed": 1,
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-    "classify_ms_p50": null
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\ No newline at end of file
diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/initial-replays/replay-image6-8fps-1x-0.json b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/initial-replays/replay-image6-8fps-1x-0.json
deleted file mode 100644
index 4958894..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/initial-replays/replay-image6-8fps-1x-0.json
+++ /dev/null
@@ -1,37152 +0,0 @@
-{
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-  "errors": [],
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diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/initial-replays/replay-image6-8fps-1x-1.json b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/initial-replays/replay-image6-8fps-1x-1.json
deleted file mode 100644
index 2f6b7bb..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/initial-replays/replay-image6-8fps-1x-1.json
+++ /dev/null
@@ -1,38105 +0,0 @@
-{
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\ No newline at end of file
diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/observations.json b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/observations.json
deleted file mode 100644
index dd21a15..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/observations.json
+++ /dev/null
@@ -1,15353 +0,0 @@
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\ No newline at end of file
diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/ocr-ablation-initial.json b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/ocr-ablation-initial.json
deleted file mode 100644
index b8cc7fb..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/ocr-ablation-initial.json
+++ /dev/null
@@ -1,1306 +0,0 @@
-{
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\ No newline at end of file
diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/ocr-ablation.json b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/ocr-ablation.json
deleted file mode 100644
index 0e2164d..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/ocr-ablation.json
+++ /dev/null
@@ -1,1305 +0,0 @@
-{
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diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/replay-hanzomon-supermarket-pan-132-152-8fps-0.5x-0.json b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/replay-hanzomon-supermarket-pan-132-152-8fps-0.5x-0.json
deleted file mode 100644
index 96ecd25..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/replay-hanzomon-supermarket-pan-132-152-8fps-0.5x-0.json
+++ /dev/null
@@ -1,5997 +0,0 @@
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diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/replay-hanzomon-supermarket-pan-132-152-8fps-0.5x-1.json b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/replay-hanzomon-supermarket-pan-132-152-8fps-0.5x-1.json
deleted file mode 100644
index 19f2c79..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/replay-hanzomon-supermarket-pan-132-152-8fps-0.5x-1.json
+++ /dev/null
@@ -1,5997 +0,0 @@
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diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/replay-hanzomon-supermarket-pan-132-152-8fps-1x-0.json b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/replay-hanzomon-supermarket-pan-132-152-8fps-1x-0.json
deleted file mode 100644
index 3840c43..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/replay-hanzomon-supermarket-pan-132-152-8fps-1x-0.json
+++ /dev/null
@@ -1,6327 +0,0 @@
-{
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diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/replay-hanzomon-supermarket-pan-132-152-8fps-1x-1.json b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/replay-hanzomon-supermarket-pan-132-152-8fps-1x-1.json
deleted file mode 100644
index af4b85f..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/replay-hanzomon-supermarket-pan-132-152-8fps-1x-1.json
+++ /dev/null
@@ -1,5997 +0,0 @@
-{
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\ No newline at end of file
diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/replay-image6-15fps-0.5x-0.json b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/replay-image6-15fps-0.5x-0.json
deleted file mode 100644
index 532443d..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/replay-image6-15fps-0.5x-0.json
+++ /dev/null
@@ -1,58613 +0,0 @@
-{
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-    "p95": 91.85
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-}
\ No newline at end of file
diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/replay-image6-15fps-1x-0.json b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/replay-image6-15fps-1x-0.json
deleted file mode 100644
index 33fbf1a..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/replay-image6-15fps-1x-0.json
+++ /dev/null
@@ -1,31772 +0,0 @@
-{
- "schema_version": 2,
- "coordinate_basis": "displayed box mapped with result frame origin",
- "summary": {
-  "source": "image6",
-  "fps": 15,
-  "rate": 1,
-  "trial": 0,
-  "input_frames": 75,
-  "sent": 75,
-  "processed": 29,
-  "not_returned": 46,
-  "elapsed_s": 7.34,
-  "server_cpu_s": 31.38,
-  "server_cpu_pct_one_core": 430.9,
-  "rtt_ms": {
-   "p50": 211.6,
-   "p95": 376.1
-  },
-  "stages_ms": {
-   "decode": {
-    "p50": 8.23,
-    "p95": 10.78
-   },
-   "detect": {
-    "p50": 107.0,
-    "p95": 198.8
-   },
-   "track": {
-    "p50": 19.4,
-    "p95": 61.28
-   },
-   "total": {
-    "p50": 127.19,
-    "p95": 260.25
-   }
-  },
-  "max_tracks": 81,
-  "ever_labelled_track_ids": 43,
-  "first_visible_ms": 492.2507080482319,
-  "label_flips": 7,
-  "errors": [],
-  "latest_stats": {
-   "crops": {
-    "requested": 0,
-    "accepted": 0,
-    "rejected": 0
-   },
-   "frames_received": 75,
-   "frames_processed": 29,
-   "frames_dropped": 46,
-   "server_fps": 5.49,
-   "classify_pending": 16,
-   "classify_failures": 0,
-   "classify_ms_p50": null
-  }
- },
- "timelines": [
-  {
-   "frame_id": 1,
-   "received_ms": 120.0,
-   "sent_ms": 75.3,
-   "timing_ms": {
-    "decode": 5.36,
-    "detect": 21.64,
-    "track": 1.38,
-    "total": 28.38
-   },
-   "stats": {
-    "crops": {
-     "requested": 0,
-     "accepted": 0,
-     "rejected": 0
-    },
-    "frames_received": 2,
-    "frames_processed": 1,
-    "frames_dropped": 0,
-    "server_fps": 0.0,
-    "classify_pending": 0,
-    "classify_failures": 0,
-    "classify_ms_p50": null
-   },
-   "tracks": [
-    {
-     "id": 1,
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-     "state": "unconfirmed",
-     "box": [
-      682.0,
-      1056.0,
-      783.0,
-      1249.0
-     ],
-     "object_generation": null,
-     "restarts": 0,
-     "source": "frame"
-    },
-    {
-     "id": 2,
-     "sku": null,
-     "state": "unconfirmed",
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-      1334.0,
-      523.0,
-      1550.0
-     ],
-     "object_generation": null,
-     "restarts": 0,
-     "source": "frame"
-    },
-    {
-     "id": 3,
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-     "state": "unconfirmed",
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-      1340.0,
-      296.0,
-      1561.0
-     ],
-     "object_generation": null,
-     "restarts": 0,
-     "source": "frame"
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-     "state": "unconfirmed",
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-      459.0,
-      866.0,
-      675.0
-     ],
-     "object_generation": null,
-     "restarts": 0,
-     "source": "frame"
-    },
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-     ],
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-     "restarts": 0,
-     "source": "frame"
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-      317.0
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-     "object_generation": null,
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-     "source": "frame"
-    },
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-      627.0,
-      1547.0
-     ],
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-      1255.0
-     ],
-     "object_generation": null,
-     "restarts": 0,
-     "source": "frame"
-    },
-    {
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-     ],
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-     ],
-     "object_generation": null,
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-     ],
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-     "restarts": 0,
-     "source": "frame"
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-     "id": 15,
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-     "state": "unconfirmed",
-     "box": [
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-      62.0,
-      376.0,
-      325.0
-     ],
-     "object_generation": null,
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-     ],
-     "object_generation": null,
-     "restarts": 0,
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-      1541.0
-     ],
-     "object_generation": null,
-     "restarts": 0,
-     "source": "frame"
-    },
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-     "state": "unconfirmed",
-     "box": [
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-      314.0,
-      1836.0
-     ],
-     "object_generation": null,
-     "restarts": 0,
-     "source": "frame"
-    },
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-     "id": 19,
-     "sku": null,
-     "state": "unconfirmed",
-     "box": [
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-      1264.0
-     ],
-     "object_generation": null,
-     "restarts": 0,
-     "source": "frame"
-    },
-    {
-     "id": 20,
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\ No newline at end of file
diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/replay-image6-8fps-0.5x-0.json b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/replay-image6-8fps-0.5x-0.json
deleted file mode 100644
index 503c7ac..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/replay-image6-8fps-0.5x-0.json
+++ /dev/null
@@ -1,20065 +0,0 @@
-{
- "schema_version": 2,
- "coordinate_basis": "displayed box mapped with result frame origin",
- "summary": {
-  "source": "image6",
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-  "rate": 0.5,
-  "trial": 0,
-  "input_frames": 40,
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-  "not_returned": 23,
-  "elapsed_s": 9.8,
-  "server_cpu_s": 32.96,
-  "server_cpu_pct_one_core": 338.4,
-  "rtt_ms": {
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-   "p95": 766.8
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-    "p95": 55.8
-   },
-   "detect": {
-    "p50": 76.07,
-    "p95": 278.56
-   },
-   "track": {
-    "p50": 32.83,
-    "p95": 239.39
-   },
-   "total": {
-    "p50": 124.19,
-    "p95": 609.82
-   }
-  },
-  "max_tracks": 79,
-  "ever_labelled_track_ids": 42,
-  "first_visible_ms": 1268.6016659718007,
-  "label_flips": 3,
-  "identity_levels": {
-   "accepted": 203,
-   "provisional": 69
-  },
-  "errors": [],
-  "latest_stats": {
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-    "accepted": 0,
-    "rejected": 0
-   },
-   "frames_received": 23,
-   "frames_processed": 17,
-   "frames_dropped": 6,
-   "server_fps": 2.9,
-   "classify_pending": 16,
-   "classify_failures": 0,
-   "classify_ms_p50": null
-  }
- },
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-    "detect": 51.47,
-    "track": 2.29,
-    "total": 64.99
-   },
-   "stats": {
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-    "frames_dropped": 0,
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-    "classify_failures": 0,
-    "classify_ms_p50": null
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-     "identity_level": null,
-     "box": [
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-      783.0,
-      1249.0
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-     "object_generation": null,
-     "restarts": 0,
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-    {
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-      1550.0
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-     "source": "frame"
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-     "identity_level": null,
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-      459.0,
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-     "source": "frame"
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-     "restarts": 0,
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\ No newline at end of file
diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/replay-image6-8fps-0.5x-1.json b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/replay-image6-8fps-0.5x-1.json
deleted file mode 100644
index 86240a6..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/replay-image6-8fps-0.5x-1.json
+++ /dev/null
@@ -1,22820 +0,0 @@
-{
- "schema_version": 2,
- "coordinate_basis": "displayed box mapped with result frame origin",
- "summary": {
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-  "rate": 0.5,
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-  "not_returned": 19,
-  "elapsed_s": 13.02,
-  "server_cpu_s": 46.73,
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-  "rtt_ms": {
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-    "p95": 18.33
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-    "p95": 700.04
-   },
-   "track": {
-    "p50": 105.52,
-    "p95": 459.65
-   },
-   "total": {
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-    "p95": 910.98
-   }
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-  "max_tracks": 78,
-  "ever_labelled_track_ids": 14,
-  "first_visible_ms": 6475.328208995052,
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-  "errors": [],
-  "latest_stats": {
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-    "accepted": 0,
-    "rejected": 0
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-   "frames_dropped": 19,
-   "server_fps": 1.89,
-   "classify_pending": 16,
-   "classify_failures": 0,
-   "classify_ms_p50": null
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-    "total": 164.95
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diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/replay-image6-8fps-1x-0.json b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/replay-image6-8fps-1x-0.json
deleted file mode 100644
index 884c9b4..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/replay-image6-8fps-1x-0.json
+++ /dev/null
@@ -1,35476 +0,0 @@
-{
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\ No newline at end of file
diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/replay-image6-8fps-1x-1.json b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/replay-image6-8fps-1x-1.json
deleted file mode 100644
index b42b31b..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/replay-image6-8fps-1x-1.json
+++ /dev/null
@@ -1,9189 +0,0 @@
-{
- "schema_version": 2,
- "coordinate_basis": "displayed box mapped with result frame origin",
- "summary": {
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-  "rate": 1,
-  "trial": 1,
-  "input_frames": 40,
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-  "not_returned": 31,
-  "elapsed_s": 8.25,
-  "server_cpu_s": 30.57,
-  "server_cpu_pct_one_core": 380.7,
-  "rtt_ms": {
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-   "p95": 1301.8
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-    "p95": 16.83
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-    "p95": 421.05
-   },
-   "track": {
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-    "p95": 576.98
-   },
-   "total": {
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-    "p95": 958.4
-   }
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-  "max_tracks": 78,
-  "ever_labelled_track_ids": 7,
-  "first_visible_ms": 4194.770915899426,
-  "label_flips": 0,
-  "errors": [],
-  "latest_stats": {
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-    "accepted": 0,
-    "rejected": 0
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-   "frames_received": 40,
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-   "frames_dropped": 31,
-   "server_fps": 1.41,
-   "classify_pending": 16,
-   "classify_failures": 0,
-   "classify_ms_p50": null
-  }
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-    "detect": 240.31,
-    "track": 5.82,
-    "total": 268.23
-   },
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- "input_hash": "bd59b38fb89c59b96c0a53157e27512be7c11754b5188890efa7496b43e5e44f"
-}
\ No newline at end of file
diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/replay-scores-initial.json b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/replay-scores-initial.json
deleted file mode 100644
index dbee5e0..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/replay-scores-initial.json
+++ /dev/null
@@ -1,788 +0,0 @@
-{
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-    "summary": {
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-      "elapsed_s": 11.78,
-      "server_cpu_s": 63.94,
-      "server_cpu_pct_one_core": 545.9,
-      "rtt_ms": {
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-        "p95": 102.3
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-      "stages_ms": {
-        "decode": {
-          "p50": 2.28,
-          "p95": 2.62
-        },
-        "detect": {
-          "p50": 57.9,
-          "p95": 85.93
-        },
-        "track": {
-          "p50": 1.64,
-          "p95": 14.19
-        },
-        "total": {
-          "p50": 61.09,
-          "p95": 97.65
-        }
-      },
-      "max_tracks": 17,
-      "ever_labelled_track_ids": 0,
-      "first_visible_ms": null,
-      "label_flips": 0,
-      "errors": [],
-      "latest_stats": {
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-        "frames_dropped": 0,
-        "server_fps": 4.02,
-        "classify_pending": 0,
-        "classify_failures": 0,
-        "classify_ms_p50": null
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-    },
-    "input_hash": "d5f77b68cb9e7e776f473c8d869e03c4fa23db2c0f6cb8b34c3b218ea12147b8"
-  },
-  "replay-hanzomon-supermarket-pan-132-152-8fps-0.5x-1": {
-    "summary": {
-      "source": "data/external-eval/tier3-sources/media/hanzomon-supermarket-pan-132-152.mp4",
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-        "p95": 90.2
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-      "stages_ms": {
-        "decode": {
-          "p50": 2.28,
-          "p95": 2.73
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-        "detect": {
-          "p50": 56.28,
-          "p95": 82.78
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-        "track": {
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-          "p95": 2.22
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-    "input_hash": "d5f77b68cb9e7e776f473c8d869e03c4fa23db2c0f6cb8b34c3b218ea12147b8"
-  },
-  "replay-hanzomon-supermarket-pan-132-152-8fps-1x-0": {
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-          "p95": 4.58
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-  },
-  "replay-hanzomon-supermarket-pan-132-152-8fps-1x-1": {
-    "summary": {
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-      "source": "image6",
-      "fps": 8,
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-        "p95": 150.2
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-      "stages_ms": {
-        "decode": {
-          "p50": 8.84,
-          "p95": 10.59
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-        "detect": {
-          "p50": 59.15,
-          "p95": 90.7
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-        "track": {
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-          "p95": 14.88
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-          "p95": 107.44
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-      "stable_correct_coverage": 0.783,
-      "blank_ratio": 0.447,
-      "timed": {
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-      "note": "Server receive timestamp as proxy for UI display; per-facing entry from 50% visible source window, not physical entrance. Unmatched track labels excluded. Wrong-label ms is sampled persistence only."
-    }
-  }
-}
\ No newline at end of file
diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/replay-scores.json b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/replay-scores.json
deleted file mode 100644
index ce97bbd..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/replay-scores.json
+++ /dev/null
@@ -1,830 +0,0 @@
-{
-  "replay-hanzomon-supermarket-pan-132-152-8fps-0.5x-0": {
-    "summary": {
-      "source": "data/external-eval/tier3-sources/media/hanzomon-supermarket-pan-132-152.mp4",
-      "fps": 8,
-      "rate": 0.5,
-      "trial": 0,
-      "input_frames": 40,
-      "sent": 40,
-      "processed": 40,
-      "not_returned": 0,
-      "elapsed_s": 11.78,
-      "server_cpu_s": 63.94,
-      "server_cpu_pct_one_core": 545.9,
-      "rtt_ms": {
-        "p50": 65.3,
-        "p95": 102.3
-      },
-      "stages_ms": {
-        "decode": {
-          "p50": 2.28,
-          "p95": 2.62
-        },
-        "detect": {
-          "p50": 57.9,
-          "p95": 85.93
-        },
-        "track": {
-          "p50": 1.64,
-          "p95": 14.19
-        },
-        "total": {
-          "p50": 61.09,
-          "p95": 97.65
-        }
-      },
-      "max_tracks": 17,
-      "ever_labelled_track_ids": 0,
-      "first_visible_ms": null,
-      "label_flips": 0,
-      "errors": [],
-      "latest_stats": {
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-          "accepted": 0,
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-        "frames_received": 40,
-        "frames_processed": 40,
-        "frames_dropped": 0,
-        "server_fps": 4.02,
-        "classify_pending": 0,
-        "classify_failures": 0,
-        "classify_ms_p50": null
-      }
-    },
-    "input_hash": "d5f77b68cb9e7e776f473c8d869e03c4fa23db2c0f6cb8b34c3b218ea12147b8"
-  },
-  "replay-hanzomon-supermarket-pan-132-152-8fps-0.5x-1": {
-    "summary": {
-      "source": "data/external-eval/tier3-sources/media/hanzomon-supermarket-pan-132-152.mp4",
-      "fps": 8,
-      "rate": 0.5,
-      "trial": 1,
-      "input_frames": 40,
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-      "server_cpu_pct_one_core": 552.6,
-      "rtt_ms": {
-        "p50": 63.1,
-        "p95": 90.2
-      },
-      "stages_ms": {
-        "decode": {
-          "p50": 2.28,
-          "p95": 2.73
-        },
-        "detect": {
-          "p50": 56.28,
-          "p95": 82.78
-        },
-        "track": {
-          "p50": 1.43,
-          "p95": 2.22
-        },
-        "total": {
-          "p50": 60.12,
-          "p95": 86.55
-        }
-      },
-      "max_tracks": 17,
-      "ever_labelled_track_ids": 0,
-      "first_visible_ms": null,
-      "label_flips": 0,
-      "errors": [],
-      "latest_stats": {
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-          "accepted": 0,
-          "rejected": 0
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-        "classify_pending": 0,
-        "classify_failures": 0,
-        "classify_ms_p50": null
-      }
-    },
-    "input_hash": "d5f77b68cb9e7e776f473c8d869e03c4fa23db2c0f6cb8b34c3b218ea12147b8"
-  },
-  "replay-hanzomon-supermarket-pan-132-152-8fps-1x-0": {
-    "summary": {
-      "source": "data/external-eval/tier3-sources/media/hanzomon-supermarket-pan-132-152.mp4",
-      "fps": 8,
-      "rate": 1,
-      "trial": 0,
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-          "p50": 49.16,
-          "p95": 92.26
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-        "track": {
-          "p50": 1.4,
-          "p95": 4.58
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-        "total": {
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-          "p95": 95.81
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-      "max_tracks": 17,
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-        "classify_pending": 0,
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-  },
-  "replay-hanzomon-supermarket-pan-132-152-8fps-1x-1": {
-    "summary": {
-      "source": "data/external-eval/tier3-sources/media/hanzomon-supermarket-pan-132-152.mp4",
-      "fps": 8,
-      "rate": 1,
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-      "input_frames": 40,
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-        "p95": 91.6
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-  "replay-image6-8fps-0.5x-1": {
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\ No newline at end of file
diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/representative-initial.json b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/representative-initial.json
deleted file mode 100644
index 006cc07..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/representative-initial.json
+++ /dev/null
@@ -1,616 +0,0 @@
-{
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diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/representative.json b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/representative.json
deleted file mode 100644
index 710f800..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/representative.json
+++ /dev/null
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-          }
-        ]
-      }
-    }
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\ No newline at end of file
diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/scheduler-benchmark-initial.json b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/scheduler-benchmark-initial.json
deleted file mode 100644
index 9e1e349..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/scheduler-benchmark-initial.json
+++ /dev/null
@@ -1,494 +0,0 @@
-{
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-    "ocr_invocations": 218,
-    "ocr_nonempty_reads": 168,
-    "worker_ms_p50": 141.37,
-    "worker_ms_p95": 215.5,
-    "first_facing_ms_p50": 1308.53,
-    "all_first_facings_ms": 2043.2,
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-    "first_results": {
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-      "8": 220.4,
-      "6": 237.5,
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-      "4": 279.1,
-      "3": 391.6,
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-      "16": 569.0,
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-      "98": 1908.4,
-      "99": 1927.5,
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-      "103": 1971.9,
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-      "106": 1996.7,
-      "107": 2013.7,
-      "108": 2021.4,
-      "109": 2035.5,
-      "110": 2043.2
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-    "ocr_nonempty_reads": 84,
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-    "ocr_nonempty_reads": 58,
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-    "first_facing_ms_p50": 561.78,
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-      "10": 19.8,
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-      "33": 65.2,
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-      "109": 713.8,
-      "110": 725.0
-    }
-  }
-}
\ No newline at end of file
diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/scheduler-benchmark.json b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/scheduler-benchmark.json
deleted file mode 100644
index 87cff13..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/scheduler-benchmark.json
+++ /dev/null
@@ -1,494 +0,0 @@
-{
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-    "tasks": 218,
-    "ocr_invocations": 218,
-    "ocr_nonempty_reads": 160,
-    "worker_ms_p50": 147.43,
-    "worker_ms_p95": 282.7,
-    "first_facing_ms_p50": 1177.31,
-    "all_first_facings_ms": 1889.7,
-    "wall_s": 4.324,
-    "throughput_tasks_s": 50.4,
-    "cpu_s": 4.48,
-    "first_results": {
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-      "7": 65.6,
-      "4": 89.7,
-      "8": 119.6,
-      "5": 145.0,
-      "3": 174.0,
-      "1": 197.3,
-      "0": 219.2,
-      "9": 236.5,
-      "10": 259.0,
-      "11": 280.2,
-      "12": 297.7,
-      "13": 327.1,
-      "14": 351.6,
-      "16": 367.7,
-      "17": 384.4,
-      "18": 405.4,
-      "19": 424.3,
-      "20": 448.8,
-      "21": 472.6,
-      "22": 497.3,
-      "23": 521.5,
-      "24": 536.7,
-      "25": 551.8,
-      "26": 569.8,
-      "27": 586.7,
-      "28": 602.2,
-      "29": 618.7,
-      "30": 634.1,
-      "31": 652.5,
-      "32": 666.7,
-      "33": 678.2,
-      "34": 708.7,
-      "35": 738.1,
-      "36": 753.3,
-      "37": 778.6,
-      "38": 794.8,
-      "39": 823.3,
-      "40": 852.2,
-      "41": 860.2,
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-      "59": 1226.4,
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-      "64": 1298.0,
-      "65": 1310.9,
-      "66": 1328.2,
-      "67": 1343.1,
-      "68": 1356.6,
-      "69": 1371.1,
-      "70": 1385.1,
-      "71": 1400.9,
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-      "73": 1433.7,
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-      "75": 1463.3,
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-      "77": 1482.5,
-      "78": 1489.1,
-      "79": 1501.8,
-      "80": 1509.1,
-      "81": 1521.6,
-      "82": 1538.1,
-      "83": 1552.3,
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-      "85": 1576.4,
-      "86": 1583.4,
-      "87": 1596.4,
-      "88": 1615.8,
-      "89": 1628.9,
-      "90": 1654.3,
-      "91": 1670.0,
-      "92": 1684.9,
-      "93": 1702.5,
-      "94": 1718.3,
-      "95": 1738.7,
-      "96": 1751.8,
-      "97": 1768.2,
-      "98": 1777.9,
-      "99": 1793.7,
-      "100": 1801.3,
-      "101": 1809.8,
-      "102": 1816.0,
-      "103": 1822.3,
-      "104": 1829.8,
-      "105": 1836.2,
-      "106": 1845.0,
-      "107": 1863.7,
-      "108": 1870.8,
-      "109": 1882.5,
-      "110": 1889.7
-    }
-  },
-  "once_per_track": {
-    "tasks": 218,
-    "ocr_invocations": 109,
-    "ocr_nonempty_reads": 80,
-    "worker_ms_p50": 19.6,
-    "worker_ms_p95": 301.62,
-    "first_facing_ms_p50": 1234.75,
-    "all_first_facings_ms": 2679.9,
-    "wall_s": 2.773,
-    "throughput_tasks_s": 78.6,
-    "cpu_s": 3.42,
-    "first_results": {
-      "6": 63.4,
-      "4": 91.5,
-      "8": 117.0,
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-      "0": 174.8,
-      "5": 202.5,
-      "3": 234.2,
-      "1": 260.5,
-      "9": 277.4,
-      "10": 299.3,
-      "11": 321.6,
-      "12": 340.8,
-      "13": 368.7,
-      "14": 393.0,
-      "16": 409.4,
-      "17": 426.1,
-      "18": 446.5,
-      "19": 468.5,
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-      "21": 512.9,
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-      "57": 1255.6,
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-    }
-  },
-  "ambiguous_track": {
-    "tasks": 218,
-    "ocr_invocations": 83,
-    "ocr_nonempty_reads": 54,
-    "worker_ms_p50": 15.74,
-    "worker_ms_p95": 221.05,
-    "first_facing_ms_p50": 806.24,
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-    "wall_s": 2.049,
-    "throughput_tasks_s": 106.4,
-    "cpu_s": 2.97,
-    "first_results": {
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-    "first_facing_ms_p50": 453.78,
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-    "first_results": {
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-      "8": 34.5,
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-      "12": 51.7,
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-      "107": 1098.9,
-      "108": 1115.3,
-      "109": 1142.0,
-      "110": 1167.8
-    }
-  }
-}
\ No newline at end of file
diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/temporal-cache.json b/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/temporal-cache.json
deleted file mode 100644
index cc45924..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/evidence/temporal-cache.json
+++ /dev/null
@@ -1,2422 +0,0 @@
-{
-  "purpose": "bounded diagnostic: how much of the geometry-only adjacent-pair work on a replay timeline is redundant between consecutive results",
-  "declared_criterion": {
-    "cache_key": "(track_id, generation) for both members, unordered",
-    "keep_iou": 0.9,
-    "reuse_requires": [
-      "pair still adjacent in this result row ordering",
-      "both member generations unchanged since the entry was written",
-      "both member boxes IoU >= 0.9 with the boxes the entry was written against"
-    ],
-    "cached_value": "the geometry-only edge verdict (analyze.geometric_edge over collect pair_signals geometry)"
-  },
-  "self_check": {
-    "first_lookup": "miss/new",
-    "repeat_lookup": "hit",
-    "generation_invalidations": 2,
-    "adjacency_invalidations": 1,
-    "geometry_invalidations": 1,
-    "entry_survived_declined_lookups": true
-  },
-  "provenance": {
-    "code": "fbf31d74feb12538dc07207cb80722dac38dd438",
-    "sources": [
-      {
-        "group": "arms",
-        "file": "replay-hanzomon-supermarket-pan-132-152-8fps-0.5x-0.json",
-        "sha256": "f66fb69637caccb08fc16e67202fd617be6607290b465e7ba6f55967285c4a1c",
-        "primary": false
-      },
-      {
-        "group": "arms",
-        "file": "replay-hanzomon-supermarket-pan-132-152-8fps-0.5x-1.json",
-        "sha256": "01d04fc7dfc924a41e1b52e1556577cc9cf8976894030b999b343e3ddd8cf7bb",
-        "primary": false
-      },
-      {
-        "group": "arms",
-        "file": "replay-hanzomon-supermarket-pan-132-152-8fps-1x-0.json",
-        "sha256": "5c2b698117648aa3ebf7cf1b008e91dfaf2c85e3300b1387f3ff9a242f71063a",
-        "primary": false
-      },
-      {
-        "group": "arms",
-        "file": "replay-hanzomon-supermarket-pan-132-152-8fps-1x-1.json",
-        "sha256": "5ea13435e9edffff6cfb336fe3985407e0d4144815ae3420806f60770ddd5e89",
-        "primary": false
-      },
-      {
-        "group": "arms",
-        "file": "replay-image6-15fps-0.5x-0.json",
-        "sha256": "e94a3209d4a5ee69689084ad4ca89a5022621bf45d83495187d464b977e03219",
-        "primary": false
-      },
-      {
-        "group": "arms",
-        "file": "replay-image6-15fps-1x-0.json",
-        "sha256": "5ab02a2abcbf5b4917dc0c0c0145865d36ed253475b092e5aec14048b8c7c702",
-        "primary": false
-      },
-      {
-        "group": "arms",
-        "file": "replay-image6-8fps-0.5x-0.json",
-        "sha256": "530d3ef2767f61b16225f4af87869018a8d98ee80e96be0c5830bc1f6d1058a4",
-        "primary": false
-      },
-      {
-        "group": "arms",
-        "file": "replay-image6-8fps-0.5x-1.json",
-        "sha256": "e6ca574dca4d70137283af392d7ed22320faac70192c7d77d04bd7dc24ff93de",
-        "primary": false
-      },
-      {
-        "group": "arms",
-        "file": "replay-image6-8fps-1x-0.json",
-        "sha256": "b1fc9b1e79f4df745d2cfc9eb99ed8d2f5a5ef58fa39a45b93971dbff936a6c8",
-        "primary": false
-      },
-      {
-        "group": "arms",
-        "file": "replay-image6-8fps-1x-1.json",
-        "sha256": "7e7958ec74df1795dde92cd402704863f193aeb3d954100ad8eb5e5d4ee4d5bd",
-        "primary": true
-      },
-      {
-        "group": "legacy_label_anchor_arms",
-        "file": "replay-hanzomon-supermarket-pan-132-152-8fps-0.5x-0.json",
-        "sha256": "2611a0f996ddbfbe08e19c341f88009ba0974284ad0269ca169d66f2bffbb02a",
-        "primary": false
-      },
-      {
-        "group": "legacy_label_anchor_arms",
-        "file": "replay-hanzomon-supermarket-pan-132-152-8fps-0.5x-1.json",
-        "sha256": "3bc526819415c25804e8dde4d1b281e94b55908623041d69a351487c7414c98e",
-        "primary": false
-      },
-      {
-        "group": "legacy_label_anchor_arms",
-        "file": "replay-hanzomon-supermarket-pan-132-152-8fps-1x-0.json",
-        "sha256": "26b4f577159dc3cae1a9960b7e1ee45f38ed5d7db2f92b3a2a3509d37ffc3203",
-        "primary": false
-      },
-      {
-        "group": "legacy_label_anchor_arms",
-        "file": "replay-hanzomon-supermarket-pan-132-152-8fps-1x-1.json",
-        "sha256": "2412c45506cb92cf644c827ec3538731997c32d78afe291dc482619a0892921c",
-        "primary": false
-      },
-      {
-        "group": "legacy_label_anchor_arms",
-        "file": "replay-image6-15fps-0.5x-0.json",
-        "sha256": "81283cd6688d2d409317743cfa77d1b4fa046b02df3fa8332b5ae8e9dfceddf6",
-        "primary": false
-      },
-      {
-        "group": "legacy_label_anchor_arms",
-        "file": "replay-image6-15fps-1x-0.json",
-        "sha256": "4e03aa38649dd209670b01e46408221e7599bc92ab24c816c2bcdc909fbfee73",
-        "primary": false
-      },
-      {
-        "group": "legacy_label_anchor_arms",
-        "file": "replay-image6-8fps-0.5x-0.json",
-        "sha256": "dc1e7e6687902913427a612ca60b316347aaf942f7db43f579ea818abfd4317e",
-        "primary": false
-      },
-      {
-        "group": "legacy_label_anchor_arms",
-        "file": "replay-image6-8fps-0.5x-1.json",
-        "sha256": "bbdd3ae3b5dc69cde51cba8510a1d78cf936e8d76d237a7cfa42dfaff53a32de",
-        "primary": false
-      },
-      {
-        "group": "legacy_label_anchor_arms",
-        "file": "replay-image6-8fps-1x-0.json",
-        "sha256": "b7cfd5627f32542b02e3424357915cd0d33f9baeac511b6abd96cd94d8e15d39",
-        "primary": false
-      },
-      {
-        "group": "legacy_label_anchor_arms",
-        "file": "replay-image6-8fps-1x-1.json",
-        "sha256": "0e6cbab53947846c042ff7a9a446c9974e29df54a19df33c637adfb0d6affab8",
-        "primary": false
-      }
-    ]
-  },
-  "arms": {
-    "replay-hanzomon-supermarket-pan-132-152-8fps-0.5x-0.json": {
-      "results": 40,
-      "schema_version": 2,
-      "coordinate_basis": "displayed box mapped with result frame origin",
-      "source": "data/external-eval/tier3-sources/media/hanzomon-supermarket-pan-132-152.mp4",
-      "fps": 8,
-      "rate": 0.5,
-      "trial": 0,
-      "frames": {
-        "sent": 40,
-        "returned": 40
-      },
-      "declared_keep_iou": 0.9,
-      "rows": {
-        "total": 119,
-        "p50_per_result": 3.0,
-        "max_per_result": 6
-      },
-      "adjacent_pairs": {
-        "per_result_recompute_workload": 228,
-        "p50_per_result": 5.5,
-        "unique_pairs": 51,
-        "adjacency_rebuilds": 61
-      },
-      "tracks": {
-        "unique_ids": 40,
-        "observations": 347,
-        "named_observations": 0,
-        "unnamed_observations": 347,
-        "degenerate_boxes_skipped": 0,
-        "named_tracks": 0,
-        "distinct_skus": 0
-      },
-      "cache": {
-        "lookups": 228,
-        "hits": 5,
-        "misses": 223,
-        "hit_rate": 0.021929824561403508,
-        "pair_computations_with_cache": 223,
-        "pair_computations_avoided": 5,
-        "miss_reasons": {
-          "new": 51,
-          "geometry": 164,
-          "generation": 8
-        },
-        "updates": 172,
-        "entries": 51,
-        "edge_yes_computed_or_reused": 153,
-        "hit_drift_iou_vs_cached_box": {
-          "min": 0.9054797602732721,
-          "p50": 0.9336638552945002,
-          "max": 0.9754209405844784,
-          "samples": 10
-        }
-      },
-      "generations": {
-        "source": "restarts",
-        "wire_generation_events": 13,
-        "tracks_with_generation_change": 11,
-        "gap_proxy_events": 0,
-        "gap_proxy_disagreements": 11,
-        "gap_proxy_disagreeing_tracks": {
-          "1": {
-            "wire": 1,
-            "gap_proxy": 0,
-            "changed_during_run": true
-          },
-          "2": {
-            "wire": 1,
-            "gap_proxy": 0,
-            "changed_during_run": true
-          },
-          "3": {
-            "wire": 1,
-            "gap_proxy": 0,
-            "changed_during_run": true
-          },
-          "4": {
-            "wire": 1,
-            "gap_proxy": 0,
-            "changed_during_run": true
-          },
-          "8": {
-            "wire": 1,
-            "gap_proxy": 0,
-            "changed_during_run": true
-          },
-          "9": {
-            "wire": 3,
-            "gap_proxy": 0,
-            "changed_during_run": true
-          },
-          "80": {
-            "wire": 1,
-            "gap_proxy": 0,
-            "changed_during_run": true
-          },
-          "88": {
-            "wire": 1,
-            "gap_proxy": 0,
-            "changed_during_run": true
-          },
-          "90": {
-            "wire": 1,
-            "gap_proxy": 0,
-            "changed_during_run": true
-          },
-          "135": {
-            "wire": 1,
-            "gap_proxy": 0,
-            "changed_during_run": true
-          },
-          "140": {
-            "wire": 1,
-            "gap_proxy": 0,
-            "changed_during_run": true
-          }
-        },
-        "generation_invalidations": 8
-      },
-      "identity": {
-        "named_transitions": 0,
-        "unchanged": 0,
-        "changed": 0,
-        "note": "consecutive named observations of one track id; a change means the label flipped, repeated observations of an unchanged label count neither way"
-      }
-    },
-    "replay-hanzomon-supermarket-pan-132-152-8fps-0.5x-1.json": {
-      "results": 40,
-      "schema_version": 2,
-      "coordinate_basis": "displayed box mapped with result frame origin",
-      "source": "data/external-eval/tier3-sources/media/hanzomon-supermarket-pan-132-152.mp4",
-      "fps": 8,
-      "rate": 0.5,
-      "trial": 1,
-      "frames": {
-        "sent": 40,
-        "returned": 40
-      },
-      "declared_keep_iou": 0.9,
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-  "limitations": [
-    "appearance: only the geometry-only adjacent edge is cacheable from a replay timeline. The appearance half of analyze.pair_value (hue, pixels, logo_pixels, inliers, ocr_jaccard) needs per-track appearance features a timeline does not carry, so this is the geometry/adjacency reuse ceiling, not a full pair-score cache. The counters bound a cache of one boolean, not of descriptor cost.",
-    "object_generation: replay.py records it, but live.py _track_payload never populates it, so it is null on every track in every arm. live.py increments object_generation and restarts in the same two branches off the same counters, so restarts carries the generation and is what these arms key on. A cache key naming object_generation specifically is therefore not yet constructible from the wire.",
-    "session generation: a method change bumps LiveSession._generation and resets every track in place, leaving per-track object_generation untouched. A per-track generation key does not cover that; a real cache needs the session generation in the key too, and no arm here exercises a method change.",
-    "absence: a gap between consecutive recorded results is not server absence. Arms return 9 of 40 sent frames, so a track can be missing from a recorded result while the server never lost it. gap_proxy_events is reported next to the wire generation and disagrees with it in exactly the arms listed under gap_proxy_disagreeing_tracks; the wire value is the one used.",
-    "box basis differs between the two groups. `arms` is schema 2, coordinate_basis \"displayed box mapped with result frame origin\". `legacy_label_anchor_arms` is the archived schema-1 run, where replay.py took label_box (always non-empty, so raw_box/box never applied) and mapped it with the label frame pan offset; an unlabelled track has label_frame_id == 0, so its box was mapped with frame 0 origin, a pure x error of up to ~197 source px on image6 against a median box width of 89 px. The two groups are not comparable to each other and are reported separately.",
-    "scope: arms differ only in pacing and source, and the still-image arms replay one development photo (add/image6.jpg, 111 labelled facings). No independent generalization, and no native UI or device path was exercised. Arms are also truncated under server load, so the named primary arm is the smallest of the set and its counters are the weakest.",
-    "counters only: no latency saving is claimed, because the timeline carries no cost for the work a hit skips, and the declared IoU is a geometric restatement of \"the box did not move\", not a measurement.",
-    "chained drift: the criterion compares each member against the box the entry was last written against, not against the previous result, so a box that creeps a little every frame can stay under the IoU forever while ending up far from where the verdict was computed. hit_drift_iou_vs_cached_box is the observed bound on that; a production cache that needs a hard staleness bound should compare against the previous result and cap the age of an entry."
-  ]
-}
\ No newline at end of file
diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/far_unknown.py b/recognization/docs/research/2026-09-29-shelf-intelligence/far_unknown.py
deleted file mode 100644
index efcd8c4..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/far_unknown.py
+++ /dev/null
@@ -1,250 +0,0 @@
-"""The four named policies on the far-unknown crops, priced exactly.
-
-`far-unknown-lines.json` sweeps the two open-set gates as a grid - LOCAL_ACCEPT x
-TEXT_MIN_SHARED, one shipped `DecisionPolicy` per cell, no fallback. That table
-says where the gates cross; it cannot say what a *named* policy costs on a shelf
-holding nothing in the catalog. Near-argmax is not a threshold at all - it keeps a
-geometric candidate the gate refused, behind two refusals of its own - and no cell
-in that grid expresses it.
-
-So the four policies go through `analyze.decide`, the same call the frozen
-observation table uses, over the same cached crops `sweep_accept_lines.py` built.
-`gate_accepts` is the shipped `DecisionPolicy` on identical evidence, so the gap
-between the two counts per policy is exactly what the fallback adds. Every accept
-on an unrelated Wikimedia shelf is a false accept by construction.
-
-Run from the recognition root:
-
-    .venv/bin/python docs/research/2026-09-29-shelf-intelligence/far_unknown.py
-"""
-from __future__ import annotations
-
-import argparse
-import hashlib
-import json
-import pickle
-import sys
-from pathlib import Path
-
-DIR = Path(__file__).resolve().parent
-ROOT = Path(__file__).resolve().parents[3]
-
-sys.path.insert(0, str(ROOT / "code"))
-sys.path.insert(0, str(DIR))
-
-import analyze  # noqa: E402
-from eval_identity import _catalog_rows, catalog_hash, code_revision  # noqa: E402
-from identity_lanes import DecisionPolicy, TextLane, _ranked  # noqa: E402
-from sweep_accept_lines import CACHE_VERSION  # noqa: E402
-
-CACHE = "data/evaluations/2026-09-29-session/scratch/accept_line_cache.pkl"
-LINES = "evidence/far-unknown-lines.json"
-OBSERVATIONS = "evidence/observations.json"
-
-
-def main(argv=None):
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("--stock", default="stock-lopia-clean")
-    parser.add_argument("--cache", default=CACHE)
-    parser.add_argument("--out", default=str(DIR / "evidence/far-unknown-policies.json"))
-    args = parser.parse_args(argv)
-
-    # Hash the exact bytes that get unpickled, so the recorded digest and the
-    # measured crops cannot come from two different reads of a rewritten file.
-    blob = Path(args.cache).read_bytes()
-    cache = pickle.loads(blob)
-    if cache.get("version") != CACHE_VERSION:
-        raise SystemExit(
-            f"cache version {cache.get('version')!r} != sweep's {CACHE_VERSION}; "
-            "rebuild it with code/sweep_accept_lines.py --rebuild"
-        )
-
-    # No OCR engine: the reads are already in the cache, so `read()` is never
-    # called and building a recognizer would only add a dependency to fail.
-    lane = TextLane.from_catalog(_catalog_rows(args.stock), args.stock)
-    signatures = lane.signatures
-
-    distractors = cache["distractors"]
-    observations = []
-    for ranked, read in distractors:
-        text = lane.scores_from_text(read)
-        # The cached rank is passed through untouched, and the dict scores are
-        # ordered exactly as the grid sweep ordered them, so the four policies
-        # here and the four columns there read identical evidence.
-        assert _ranked(text) == sorted(text.items(), key=lambda kv: kv[1], reverse=True)
-        observations.append({"rank": ranked, "text": text, "read": read})
-    assert len(observations) == len(distractors)
-
-    results = {}
-    for policy, threshold in analyze.POLICIES.items():
-        gate = DecisionPolicy(local_accept=threshold, signatures=signatures)
-        accepts = fallback_only = with_rank = 0
-        by_source = {"local": 0, "text": 0}
-        for o in observations:
-            if o["rank"]:
-                with_rank += 1
-            decision = gate.decide(o["rank"], o["text"], text_read=o["read"])
-            answer = analyze.decide(o, policy, signatures)
-            if not answer:
-                continue
-            accepts += 1
-            if decision["best_sku"]:
-                # The fallback only runs when the gate refused, so a gated
-                # answer must survive `analyze.decide` unchanged.
-                assert answer == decision["best_sku"]
-                by_source[decision["source"]] += 1
-            else:
-                fallback_only += 1
-        # Accounting: every crop decided once, accepts split exhaustively, and
-        # the fallback belongs to near-argmax alone. A weaker floor accepts
-        # weakly more, so the counts rise as the threshold falls.
-        assert accepts - fallback_only >= 0
-        assert by_source["local"] + by_source["text"] + fallback_only == accepts
-        assert fallback_only == 0 or policy == "near-argmax"
-        results[policy] = {
-            "local_accept": threshold,
-            "fallback": policy == "near-argmax",
-            "accepts": accepts,
-            "gate_accepts": accepts - fallback_only,
-            "fallback_accepts": fallback_only,
-            "by_source": by_source,
-            "rate": accepts / len(observations),
-            "rate_percent": round(accepts / len(observations) * 100, 4),
-            "crops_with_a_local_candidate": with_rank,
-        }
-
-    ordered = [results[name]["accepts"] for name in analyze.POLICIES]
-    assert ordered == sorted(ordered), f"accepts not monotone in the accept floor: {ordered}"
-
-    crops = len(observations)
-    report = {
-        "question": (
-            "False accepts per named analyze policy on unrelated shelf crops, "
-            "analyze.decide on the sweep_accept_lines cache."
-        ),
-        "method": {
-            "decision": "analyze.decide(o, policy, signatures) - the frozen analysis's own call",
-            "policies": {name: analyze.POLICIES[name] for name in analyze.POLICIES},
-            "gate_for_comparison": "identity_lanes.DecisionPolicy(local_accept=threshold, signatures)",
-            "text_scores": "TextLane.scores_from_text on the cached OCR read",
-            "fallback": (
-                "analyze.decide's near-argmax extra fallback only: keep rank[0] when the gate "
-                "refused, unless the read contradicts that row's size or the text prefers another row"
-            ),
-        },
-        "provenance": {
-            "cache": {
-                "path": args.cache,
-                "sha256": hashlib.sha256(blob).hexdigest(),
-                "bytes": len(blob),
-                "version": cache["version"],
-                "detect_conf": cache["conf"],
-                "records": ["version", "conf", "photos", "shelf_full", "shelf_live", "distractors"],
-                "not_recorded": [
-                    "stock_dir",
-                    "catalog_hash",
-                    "code_revision",
-                    "source image paths or hashes",
-                    "per-photo crop counts",
-                ],
-                "note": (
-                    "The cache attests no catalog, so the catalog match below is read off the "
-                    "current worktree, not out of the cache. The read is unpickled, not rebuilt."
-                ),
-            },
-            "catalog": {
-                "dir": args.stock,
-                "catalog_hash": catalog_hash(args.stock),
-            },
-            "code_revision": code_revision(),
-            "signatures": {
-                "rows": len(signatures),
-                "sha256": hashlib.sha256(
-                    json.dumps(signatures, sort_keys=True, ensure_ascii=False).encode()
-                ).hexdigest(),
-            },
-        },
-        "counts": {
-            "photos": cache["photos"],
-            "crops": crops,
-            "crops_with_a_local_candidate": sum(1 for o in observations if o["rank"]),
-            "crops_with_no_local_candidate": sum(1 for o in observations if not o["rank"]),
-            "crops_with_a_raw_read": sum(1 for o in observations if o["read"]),
-            # A read can be non-empty and still score nothing: `scores_from_text`
-            # returns {} under four normalized characters.
-            "crops_scored_by_text": sum(1 for o in observations if o["text"]),
-        },
-        "policies": results,
-    }
-
-    # Context, not an accounting input: the older grid table's same-threshold
-    # cells, whose producer is not in the tree, so its settings stay unverified.
-    lines_path = DIR / LINES
-    lines = json.loads(lines_path.read_text()) if lines_path.is_file() else {}
-    report["cross_check_far_unknown_lines"] = {
-        "source": LINES,
-        "note": "accept-threshold cells only, no fallback; producer script not in the tree",
-        "cells": {
-            name: {
-                "lines_accepts": line["accepts"],
-                "lines_crops": line["crops"],
-                "here_gate_accepts": results[name]["gate_accepts"],
-                "agrees": line["accepts"] == results[name]["gate_accepts"]
-                and line["crops"] == crops,
-            }
-            for name, row in results.items()
-            if (line := lines.get(str(row["local_accept"])))
-        },
-    }
-
-    # What the cache cannot attest, read off the frozen table instead: the lane
-    # built here carries the same 12 names the observation table was scored on.
-    frozen = json.loads((DIR / OBSERVATIONS).read_text())
-    report["provenance"]["catalog"].update({
-        "matches_observations": frozen["signatures"] == signatures,
-        "observations_catalog_hash": frozen["provenance"]["catalog"],
-        "observations_code_revision": frozen["provenance"]["code"],
-    })
-    report["limitations"] = [
-        "The cache records no catalog, code revision or image list, so 'same catalog as the "
-        "frozen observations' is read off the current worktree, not attested by the cache.",
-        f"Only {report['counts']['crops_with_a_local_candidate']} of {crops} crops produced a local "
-        f"candidate; the other {report['counts']['crops_with_no_local_candidate']} are decided by the "
-        "text lane alone or refused before any gate applies, so the accept floor is exercised on a "
-        "small minority of the rows and the rates are dominated by empty evidence.",
-        "The catalog is 12 rows from one store. A false-accept rate is a function of catalog size, so "
-        "these numbers do not transfer to a larger or differently-stocked catalog.",
-        "Crops come from one detection pass at detect_conf "
-        f"{cache['conf']} on 1280-long-side frames, so the crop population is set by the detector, "
-        "not only by the identity gate.",
-        "The cache has no per-photo breakdown, so no photo-level rate and no check that one image is "
-        "not carrying the whole count.",
-        "The near-argmax fallback is a research construct in analyze.py, not shipped code; its row "
-        "prices a policy that does not exist on the server yet.",
-        "far-unknown-lines.json's producer is not in the tree, so agreement with it is corroboration, "
-        "not verification.",
-    ]
-
-    Path(args.out).write_text(json.dumps(report, indent=1, ensure_ascii=False) + "\n")
-
-    print(f"cache {args.cache} sha256 {report['provenance']['cache']['sha256'][:16]}… "
-          f"v{cache['version']} conf {cache['conf']}")
-    print(f"catalog {args.stock}: {len(signatures)} rows, hash "
-          f"{report['provenance']['catalog']['catalog_hash'][:16]}…, matches observations "
-          f"{report['provenance']['catalog']['matches_observations']}")
-    print(f"{cache['photos']} unrelated shelf photos, {crops} crops, "
-          f"{report['counts']['crops_with_a_local_candidate']} with a local candidate")
-    print()
-    print(f"{'policy':>15} {'accept':>7} {'fallb':>6} {'accepts':>8} {'gate':>6} "
-          f"{'local':>6} {'text':>6} {'rate':>8}")
-    for name, row in results.items():
-        print(f"{name:>15} {row['local_accept']:>7.1f} {str(row['fallback']):>6} "
-              f"{row['accepts']:>8} {row['gate_accepts']:>6} {row['by_source']['local']:>6} "
-              f"{row['by_source']['text']:>6} {row['rate_percent']:>7.4f}%")
-    print()
-    print(f"wrote {args.out}")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/group-truth.json b/recognization/docs/research/2026-09-29-shelf-intelligence/group-truth.json
deleted file mode 100644
index dfda12b..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/group-truth.json
+++ /dev/null
@@ -1,16 +0,0 @@
-{
-  "source": "Main visual inspection of add/image6.jpg and evidence/facings.jpg, 2026-09-29; existing catalog labels unchanged",
-  "status": "Single annotator diagnostic repeated-artwork labels; not independently verified JAN ground truth",
-  "rule": "Unknowns receive a distinct visual product key only when repeat artwork is visible. Null omitted IDs are not scored for grouping. No unknown equality shortcut.",
-  "families": {"sku_l01":"kewpie-mayo","sku_l02":"kewpie-mayo","sku_l03":"kewpie-mayo","sku_l04":"yamaya-mayo","sku_l05":"kewpie-mayo","sku_l06":"kewpie-mayo","sku_l07":"lucky-mayo","sku_l08":"peri-mix","sku_l09":"pure-select","sku_l10":"pure-select","sku_l11":"kewpie-mayo","sku_l12":"yuras-sauce"},
-  "unknown_visual_groups": [
-    {"key":"demiglace-pouch","family":"demiglace","members":[31,32]},
-    {"key":"heinz-ketchup","family":"heinz","members":[65,66]},
-    {"key":"black-label-dressing","family":"dressing","members":[76,77,78,79]},
-    {"key":"kewpie-450g","family":"kewpie-mayo","members":[94,95,96,97,81,82,83,84,85,86,87,88]},
-    {"key":"sesame-dressing","family":"dressing","members":[89,90,91]},
-    {"key":"bottom-dark-dressing","family":"dressing","members":[99,100,101]}
-  ],
-  "ambiguous_unknown_ids":[16,60,61,62,63,64,80,92,93,98,102,103,104,105,106,107,108,109,110],
-  "notes":["104 is an overlapping aggregate crop in legacy labels; do not use it for pair calibration", "Bottom partial Kewpie facings may differ by size or overlap; no repeated-SKU truth assigned", "Family definitions are evaluation-only and never supplied to grouping inference"]
-}
diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/ocr_ablation.py b/recognization/docs/research/2026-09-29-shelf-intelligence/ocr_ablation.py
deleted file mode 100644
index 1d82d46..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/ocr_ablation.py
+++ /dev/null
@@ -1,56 +0,0 @@
-"""E: same cached crop evidence, alter only which real OCR reads are available."""
-from pathlib import Path
-import json
-import copy
-import statistics
-from analyze import decide,propagate,metrics,infer_groups
-
-DIR=Path(__file__).resolve().parent
-OUT=DIR/'evidence'
-
-
-def main():
-    data=json.loads((OUT/'observations.json').read_text()); analysis=json.loads((OUT/'analysis.json').read_text())
-    families=json.loads((DIR/'group-truth.json').read_text())['families']
-    report={'provenance':data['provenance'],'arms':{}}
-    for res,d in data['resolutions'].items():
-        obs=d['observations']; sig=data['signatures']; groups=analysis['resolutions'][res]['appearance_groups']
-        lookup={o['id']:o for o in obs}
-        visual={o['id']:decide(o,'strict',sig,use_text=False) for o in obs}
-        # Only uncertainty in the visual evidence schedules expensive text.
-        ambiguous={o['id'] for o in obs if not visual[o['id']] or len(o['rank'])>1 and o['rank'][0][1]<5}
-        arms={
-            'every_attempt':{'ocr_ids':{o['id'] for o in obs},'attempts':2},
-            'once_per_track':{'ocr_ids':{o['id'] for o in obs},'attempts':2},
-            'ambiguous_track':{'ocr_ids':ambiguous,'attempts':2},
-            'group_representative':{'ocr_ids':{max(group,key=lambda i:lookup[i]['sharpness']) for group in groups if any(i in ambiguous for i in group)},'attempts':2},
-        }
-        result={}
-        for name,arm in arms.items():
-            if name=='group_representative':
-                source={i:max(g,key=lambda k:lookup[k]['sharpness']) for g in groups for i in g}
-            else:source={i:i for i in lookup}
-            masked=[]
-            for o in obs:
-                clone=copy.deepcopy(o); origin=source[o['id']]
-                # Representative's read is reusable only when its group
-                # correspondence passed the boundary guard. Read is not a
-                # perfect label; it goes through the unchanged policy.
-                if origin not in arm['ocr_ids']:
-                    clone['text']={};clone['read']=''
-                else:
-                    clone['text']=lookup[origin]['text'];clone['read']=lookup[origin]['read']
-                masked.append(clone)
-            bases={o['id']:decide(o,'coverage-first',sig) for o in masked}
-            available_groups=infer_groups(d,analysis['resolutions'][res]['guarded_edges'],{o['id']:decide(o,'strict',sig) for o in masked})
-            fused,changes=propagate(masked,available_groups,bases,sig,guarded=True)
-            number=len(arm['ocr_ids'])*(arm['attempts'] if name=='every_attempt' else 1)
-            ocr_ms=sum(lookup[i]['ocr_ms'] for i in arm['ocr_ids'])*(arm['attempts'] if name=='every_attempt' else 1)
-            local_ms=sum(o['local_ms'] for o in obs)*arm['attempts']
-            result[name]={'individual':metrics(masked,bases,families),'group':metrics(masked,fused,families,bases),'ocr_calls':number,'ocr_calls_per_facing':number/len(obs),'ocr_ms_serial_projection':round(ocr_ms,1),'matcher_calls':len(obs)*arm['attempts'],'matcher_ms_serial_projection':round(local_ms,1),'read_coverage':sum(bool(lookup[i]['read']) for i in arm['ocr_ids']),'ocr_ids':sorted(arm['ocr_ids']),'rescues':changes}
-        report['arms'][res]=result
-        print(res)
-        for name,r in result.items():print(name,r['ocr_calls'],r['group']['shown'],r['group']['correct'],r['group']['wrong'],r['ocr_ms_serial_projection'])
-    (OUT/'ocr-ablation.json').write_text(json.dumps(report,ensure_ascii=False,indent=2))
-
-if __name__=='__main__':main()
diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/protocol.json b/recognization/docs/research/2026-09-29-shelf-intelligence/protocol.json
deleted file mode 100644
index 01a7fef..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/protocol.json
+++ /dev/null
@@ -1,22 +0,0 @@
-{
-  "scope": "Research prototypes only; production files unchanged",
-  "baseline_revision": "fbf31d74feb12538dc07207cb80722dac38dd438",
-  "primary_data": "eval/labels/image6.json",
-  "catalog": "stock-lopia-clean",
-  "data_status": "Single development photo, previously tuned. No cross-visit generalization claim.",
-  "controls": ["Freeze catalog and labels; hash inputs", "Extract evidence once, replay identical evidence across policies", "Inference receives boxes and observations, never identity ground truth", "Oracle arm alone receives true group membership", "Unknown is not a SKU: never merge unknowns merely because labels equal", "Report labels over all 109 facings and correct labels over all 64 catalog facings separately", "Keep geometry-derived rows fixed across methods", "Compare deterministic runs to chain dynamic programming", "Report group rescue, correction and damage separately", "Separate measured wall time from cached-evidence simulations", "Never call a synthetic pan a recorded walk", "Use paced video timestamps, not result-synchronous sends, for full-rate replay"],
-  "hypotheses": {
-    "A": "Color plus normalized appearance and local correspondences separate repeated facings better than either alone; brand siblings remain difficult.",
-    "B": "Geometry-only rows plus appearance-supported run edges identify repeats without truth labels; leave-one-row-out calibration limits same-row tuning.",
-    "C": "Perfect membership sharing observed strong member evidence recovers at least 5 percentage points of catalog-facing correct coverage. No oracle SKU assignment.",
-    "D": "Inferred group recovery retains useful oracle gain without more than one damaged originally correct facing; measure rather than require a positive result.",
-    "E": "Visual-first scheduling and selective group representative OCR reduce actual OCR calls and make first evidence available sooner; never infer speed from call counts alone.",
-    "F": "Coverage-first has greater product utility than strict even with lower shown-label precision. Near-argmax is a measured stress point, not the presumed winner.",
-    "G": "Independent frame pacing exposes queue churn that the existing send/result client cannot test because both wait for every result. Compare identical source frame sequences at native sample pacing and 0.5x."
-  },
-  "policies": {"strict":4.0,"balanced":2.0,"coverage-first":0.5,"near-argmax":0.0},
-  "confirmation": "Strict production decision is strong evidence, not calibrated probability. All extra identities provisional.",
-  "utility_costs_per_facing_second": {"blank":1.0,"wrong_sibling_provisional":1.5,"wrong_other_provisional":4.0,"wrong_confirmed":8.0,"correct":0.0,"flip_event":0.2},
-  "utility_sensitivity": "Sweep wrong-label cost; no empirical user-cost calibration claimed",
-  "required_outputs": ["raw per-facing and per-pair evidence", "boundary/group confusion counts", "policy frontier plot", "OCR arm costs", "per-frame live timelines", "latency p50/p95 and CPU", "decision A-H with limitations and implementation acceptance"]
-}
diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/replay.py b/recognization/docs/research/2026-09-29-shelf-intelligence/replay.py
deleted file mode 100644
index 9193ba0..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/replay.py
+++ /dev/null
@@ -1,112 +0,0 @@
-"""G: fixed video frames, independently paced sender/receiver, real WebSocket server.
-Run against the already-running local server; no source frame/video is copied.
-"""
-from pathlib import Path
-import sys
-import os
-import json
-import time
-import asyncio
-import statistics
-import subprocess
-import hashlib
-import argparse
-import resource
-from collections import defaultdict
-
-ROOT=Path(__file__).resolve().parents[3]
-sys.path.insert(0,str(ROOT/'code'))
-import cv2
-from websockets.asyncio.client import connect
-from stream_client import FrameSource,encode
-OUT=Path(__file__).resolve().parent/'evidence'
-
-
-def q(values,p):
-    if not values:return None
-    return round(sorted(values)[min(len(values)-1,int((len(values)-1)*p))],2)
-
-
-def input_frames(source, fps, duration=5):
-    if source=='image6':
-        view=FrameSource(str(ROOT/'add/image6.jpg'),width=1280,pan_step=5.6)
-        out=[]
-        for n in range(int(duration*fps)):
-            out.append((encode(view.frame(n),1280,75),view.offset,view.scale))
-        view.release()
-        return out
-    cap=cv2.VideoCapture(str(ROOT/source))
-    sample=[]; frame_count=int(cap.get(cv2.CAP_PROP_FRAME_COUNT)); native_fps=cap.get(cv2.CAP_PROP_FPS)
-    start=max(0,(frame_count-int(duration*native_fps))//2)
-    for n in range(int(duration*fps)):
-        cap.set(cv2.CAP_PROP_POS_FRAMES,start+int(n*native_fps/fps))
-        ok,frame=cap.read()
-        if not ok:break
-        sample.append((encode(frame,1280,75),(0,0),1.))
-    cap.release()
-    return sample
-
-
-async def trial(source,frames,fps,rate,trial_id):
-    # Real-time source sampling is frozen across the two pace arms. At .5x each
-    # source frame is shown longer, rather than processing a different sequence.
-    secret=(ROOT/'.demo-token').read_text().strip()
-    started=time.perf_counter(); results=[]; sent={}; first={}; transitions=defaultdict(list); errors=[]
-    max_tracks=0; pid=int(subprocess.check_output(['lsof','-tiTCP:8099','-sTCP:LISTEN'],text=True).splitlines()[0])
-    from psutil import Process
-    proc=Process(pid); cpu0=proc.cpu_times(); cpu_before=time.perf_counter()
-    async with connect('ws://127.0.0.1:8099/v1/stream',additional_headers={'Authorization':f'Bearer {secret}'},max_size=16*1024*1024) as socket:
-        hello=json.loads(await socket.recv())
-        if hello.get('type')!='hello':raise RuntimeError('server did not greet authenticated session')
-        await socket.send(json.dumps({'type':'config','tags':False,'empty_slots':False}))
-        await socket.recv()
-        async def receive():
-            nonlocal max_tracks
-            while True:
-                try: raw=await asyncio.wait_for(socket.recv(), timeout=2)
-                except asyncio.TimeoutError: break
-                msg=json.loads(raw)
-                if msg.get('type')!='result':
-                    errors.append(msg);continue
-                now=time.perf_counter(); tracks=msg['tracks']; max_tracks=max(max_tracks,len(tracks))
-                visible=[]
-                for tr in tracks:
-                    state=tr['identity']; sku=state.get('sku_id'); tid=tr['track_id']
-                    if sku:
-                        if tid not in first:first[tid]=now
-                        previous=transitions[tid]
-                        if not previous or previous[-1]['sku']!=sku:previous.append({'ms':round((now-started)*1000,1),'sku':sku,'state':state.get('state')})
-                    box=tr.get('box') or tr.get('raw_box')
-                    origin,scale=frames[min(len(frames)-1,max(0,msg['frame_id']-1))][1:]
-                    visible.append({'id':tid,'sku':sku,'state':state.get('state'),'identity_level':state.get('identity_level'),'box':[round(box[j]/scale+origin[j%2],1) for j in range(4)] if box else [],'object_generation':tr.get('object_generation'),'restarts':tr.get('restarts'), 'source':state.get('source')})
-                results.append({'frame_id':msg['frame_id'],'received_ms':round((now-started)*1000,1),'sent_ms':round((sent.get(msg['frame_id'],started)-started)*1000,1),'timing_ms':msg.get('timing_ms'),'stats':msg.get('stats'), 'tracks':visible})
-        receiver=asyncio.create_task(receive())
-        for n,(frame,*_) in enumerate(frames):
-            deadline=started+n/(fps*rate)
-            await asyncio.sleep(max(0,deadline-time.perf_counter()))
-            await socket.send((n+1).to_bytes(4,'big')+frame)
-            sent[n+1]=time.perf_counter()
-        await asyncio.wait_for(receiver, timeout=max(15,len(frames)/fps/rate+6))
-    elapsed=time.perf_counter()-started; cpu1=proc.cpu_times();cpu=(cpu1.user+cpu1.system-cpu0.user-cpu0.system)
-    received={r['frame_id'] for r in results}
-    stages={k:[r['timing_ms'][k] for r in results if r.get('timing_ms') and r['timing_ms'].get(k) is not None] for k in ['decode','detect','track','total']}
-    rtt=[r['received_ms']-r['sent_ms'] for r in results]
-    levels=defaultdict(int)
-    for r in results:
-        for t in r['tracks']:
-            if t['sku']: levels[t.get('identity_level') or 'unset']+=1
-    summary={'source':source,'fps':fps,'rate':rate,'trial':trial_id,'input_frames':len(frames),'sent':len(sent),'processed':len(results),'not_returned':len(sent)-len(received),'elapsed_s':round(elapsed,2),'server_cpu_s':round(cpu,2),'server_cpu_pct_one_core':round(cpu/max(.001,time.perf_counter()-cpu_before)*100,1),'rtt_ms':{'p50':q(rtt,.5),'p95':q(rtt,.95)},'stages_ms':{k:{'p50':q(v,.5),'p95':q(v,.95)} for k,v in stages.items()},'max_tracks':max_tracks,'ever_labelled_track_ids':len(first),'first_visible_ms':min((v-started)*1000 for v in first.values()) if first else None,'label_flips':sum(max(0,len(x)-1) for x in transitions.values()),'identity_levels':dict(levels),'errors':errors,'latest_stats':results[-1]['stats'] if results else None}
-    output={'schema_version':2,'coordinate_basis':'displayed box mapped with result frame origin','summary':summary,'timelines':results,'transitions':dict(transitions),'input_hash':hashlib.sha256(b''.join(x[0] for x in frames)).hexdigest()}
-    path=OUT/f'replay-{Path(source).stem}-{fps}fps-{rate}x-{trial_id}.json';path.write_text(json.dumps(output,ensure_ascii=False,indent=1))
-    print(path.name,summary['processed'],'/',summary['sent'],'first',summary['first_visible_ms'],'detect',summary['stages_ms']['detect'],'cpu',summary['server_cpu_s'],flush=True)
-
-
-async def main():
-    parser=argparse.ArgumentParser();parser.add_argument('--source',default='image6');parser.add_argument('--fps',type=int,default=8);parser.add_argument('--seconds',type=int,default=5);parser.add_argument('--trials',type=int,default=2)
-    args=parser.parse_args();OUT.mkdir(exist_ok=True)
-    frames=input_frames(args.source,args.fps,args.seconds)
-    if not frames:raise SystemExit('source yielded no frames')
-    for i in range(args.trials):
-        for rate in (1,.5):await trial(args.source,frames,args.fps,rate,i)
-
-if __name__=='__main__':asyncio.run(main())
diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/representative.py b/recognization/docs/research/2026-09-29-shelf-intelligence/representative.py
deleted file mode 100644
index 8d462a7..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/representative.py
+++ /dev/null
@@ -1,46 +0,0 @@
-"""Cached-evidence representative ablation; serial cost projections, not wall time.
-Selection uses appearance-only groups; unavailable matcher evidence is masked.
-"""
-from pathlib import Path
-import json
-import sys
-import statistics
-import time
-from collections import Counter
-
-ROOT=Path(__file__).resolve().parents[3];sys.path.insert(0,str(ROOT/'code'))
-from identity_lanes import DecisionPolicy
-from analyze import decide,metrics,propagate,infer_groups
-DIR=Path(__file__).resolve().parent;OUT=DIR/'evidence'
-
-
-def main():
-    observations=json.loads((OUT/'observations.json').read_text());analysis=json.loads((OUT/'analysis.json').read_text());family=json.loads((DIR/'group-truth.json').read_text())['families'];summary={}
-    for res,d in observations['resolutions'].items():
-        rows=d['observations'];lookup={x['id']:x for x in rows}; groups=analysis['resolutions'][res]['appearance_groups']; sig=observations['signatures']
-        baseline={o['id']:decide(o,'coverage-first',sig) for o in rows}
-        arms={}
-        for arm in ('sharpest-one','sharpest-two','all'):
-            selected=set()
-            for group in groups:
-                ordered=sorted(group,key=lambda i:lookup[i]['sharpness'],reverse=True)
-                selected.update(ordered[:len(group) if arm=='all' else 2 if arm=='sharpest-two' else 1])
-            # Unlike the OCR-representative arm, unselected members have no
-            # matcher observation. Their individually read OCR may still speak.
-            masked=[]
-            for o in rows:
-                clone=dict(o)
-                if o['id'] not in selected:clone['rank']=[];clone['local']={}
-                masked.append(clone)
-            individual={o['id']:decide(o,'coverage-first',sig) for o in masked}
-            available_groups=infer_groups(d,analysis['resolutions'][res]['guarded_edges'],{o['id']:decide(o,'strict',sig) for o in masked})
-            inferred,changes=propagate(masked,available_groups,individual,sig,guarded=True)
-            matcher_ms=sum(lookup[i]['local_ms'] for i in selected)
-            descriptor_ms=sum(lookup[i]['pair_features_ms'] for i in lookup) # group prep retains descriptor extraction
-            pair_ms=sum(p['pair_ms'] for p in d['pairs'])
-            arms[arm]={'matcher_calls':len(selected),'matcher_ms_serial_projection':round(matcher_ms,1),'group_descriptor_ms':round(descriptor_ms,1),'pair_match_ms':round(pair_ms,1),'total_identity_and_group_ms_serial_projection':round(matcher_ms+descriptor_ms+pair_ms,1),'individual':metrics(masked,individual,family,baseline),'inferred':metrics(masked,inferred,family,baseline),'changes':changes}
-        summary[res]=arms
-        for arm,v in arms.items():print(res,arm,v['matcher_calls'],v['inferred']['correct'],v['inferred']['wrong'],v['total_identity_and_group_ms_serial_projection'])
-    (OUT/'representative.json').write_text(json.dumps({'provenance':observations['provenance'],'arms':summary},ensure_ascii=False,indent=2))
-
-if __name__=='__main__':main()
diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/score_replay.py b/recognization/docs/research/2026-09-29-shelf-intelligence/score_replay.py
deleted file mode 100644
index a25f4f8..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/score_replay.py
+++ /dev/null
@@ -1,82 +0,0 @@
-"""Sampled WebSocket display proxy; not instrumented phone rendering."""
-from pathlib import Path
-import json
-import sys
-from collections import defaultdict
-from statistics import median
-
-ROOT=Path(__file__).resolve().parents[3]
-sys.path.insert(0,str(ROOT/'code'))
-from stream_client import FrameSource
-from score_stream import iou,visible_fraction
-DIR=Path(__file__).resolve().parent; OUT=DIR/'evidence'
-LABELS=json.loads((ROOT/'eval/labels/image6.json').read_text())['facings']
-
-
-def windows(source,frames):
-    if source!='image6': return {}
-    view=FrameSource(str(ROOT/'add/image6.jpg'),width=1280,pan_step=5.6)
-    result={}
-    for n in range(frames):
-        view.frame(n);result[n+1]=view.windows[-1]
-    view.release();return result
-
-
-def score(run):
-    s=run['summary'];w=windows(s['source'],s['input_frames'])
-    histories=defaultdict(list);entry={};first={};unmatched=0
-    truth={f['index']:f['label'] for f in LABELS if f['label']!='exclude'}
-    for sequence,r in enumerate(run['timelines']):
-        facings=[f for f in LABELS if f['label']!='exclude' and visible_fraction(f['box'],w[r['frame_id']])>=.5]
-        for f in facings:entry.setdefault(f['index'],r['sent_ms'])
-        # Greedy descending IoU, one track per facing and one facing per track.
-        # Match before considering SKU so a correct label never wins a tie by truth.
-        pairs=sorted(((iou(t['box'],f['box']),j,f['index']) for j,t in enumerate(r['tracks']) for f in facings),reverse=True)
-        assigned={};used=set()
-        for overlap,j,key in pairs:
-            if overlap<.2:break
-            if key not in assigned and j not in used:
-                assigned[key]=r['tracks'][j]['sku'];used.add(j)
-        unmatched+=sum(bool(t['sku']) and j not in used for j,t in enumerate(r['tracks']))
-        for f in facings:
-            key=f['index'];sku=assigned.get(key)
-            histories[key].append((r['received_ms'],sku,sequence))
-            if sku:first.setdefault(key,r['received_ms'])
-    timed={}
-    for delay in (250,500,1000):
-        eligible=[k for k,h in histories.items() if h[-1][0]>=entry[k]+delay]
-        values={k:next((sku for at,sku,_ in reversed(histories[k]) if at<=entry[k]+delay),None) for k in eligible}
-        known=[k for k in eligible if truth[k]!='unknown']
-        named=sum(bool(v) for v in values.values());correct=sum(bool(v) and v==truth[k] for k,v in values.items())
-        timed[str(delay)]={'visible_denominator':len(eligible),'catalog_denominator':len(known),'any_label':named,'correct_label':correct,'vrc':named/len(eligible) if eligible else None,'correct_catalog_coverage':correct/len(known) if known else None}
-    corrections=[];flips=0;wrong_ms=0.;stable=[];stable_at=[];last_correct=0
-    for key,h in histories.items():
-        previous=None
-        for (at,sku,sequence),(end,_,next_sequence) in zip(h,h[1:]):
-            if sku and sku!=truth[key] and next_sequence==sequence+1:wrong_ms+=end-at
-        for at,sku,_ in h:
-            if sku and previous and sku!=previous:
-                flips+=1
-                if sku==truth[key]:corrections.append(key)
-            if sku:previous=sku
-        if truth[key]=='unknown' or h[-1][1]!=truth[key]:continue
-        last_correct+=1;start=len(h)-1
-        while start>0 and h[start-1][1]==truth[key] and h[start-1][2]+1==h[start][2]:start-=1
-        if h[-1][0]-h[start][0]>=500:
-            stable.append(key);stable_at.append(h[start][0]-entry[key])
-    known=sum(truth[k]!='unknown' for k in histories)
-    return {'visible_facings':len(histories),'catalog_facings':known,'ever_named':len(first),'never_named_ratio':1-len(first)/len(histories),'last_observed_correct_catalog':last_correct,'last_observed_correct_coverage':last_correct/known if known else None,'stable_correct_catalog_500ms':len(stable),'stable_correct_coverage_500ms':len(stable)/known if known else None,'time_to_trailing_stable_correct_ms_p50':median(stable_at) if stable_at else None,'timed':timed,'first_label_from_entry_ms_p50':median(first[k]-entry[k] for k in first) if first else None,'corrections':sorted(set(corrections)),'identity_flips':flips,'sampled_wrong_label_ms':wrong_ms,'unmatched_label_samples':unmatched,'note':'Receive timestamps proxy display; entry is first processed source window with >=50% visibility. Stable means a trailing correct run spanning >=500 ms of consecutive received results, not future stability. Wrong exposure holds each received state until next result while facing stays visible; final intervals censored. Crop-position matching is diagnostic, not native overlay verification.'}
-
-
-def main():
-    reports={}
-    for path in sorted(OUT.glob('replay-*.json')):
-        if path.name in ('replay-scores.json','replay-scores-initial.json'):continue
-        run=json.loads(path.read_text());name=path.stem
-        reports[name]={'summary':run['summary'],'input_hash':run['input_hash']}
-        if run['summary']['source']=='image6':reports[name]['visible']=score(run)
-    (OUT/'replay-scores.json').write_text(json.dumps(reports,ensure_ascii=False,indent=2))
-    for name,r in reports.items():
-        v=r.get('visible',{});print(name,v.get('last_observed_correct_catalog'),v.get('stable_correct_catalog_500ms'),v.get('timed'))
-
-if __name__=='__main__':main()
diff --git a/recognization/docs/research/2026-09-29-shelf-intelligence/temporal_cache.py b/recognization/docs/research/2026-09-29-shelf-intelligence/temporal_cache.py
deleted file mode 100644
index a2bf499..0000000
--- a/recognization/docs/research/2026-09-29-shelf-intelligence/temporal_cache.py
+++ /dev/null
@@ -1,327 +0,0 @@
-"""I: bounded temporal pair-cache diagnostic over saved replay timelines.
-
-Counters only: hits/misses, unique pairs, updates, absence invalidations, and
-how often a named identity changed between consecutive named observations.
-No latency claim, no SKU propagation, no accuracy claim, no production edits.
-
-Reuses the frozen geometry conventions: `collect.rows_from_boxes` (bottom-aligned
-shelf rows) and `analyze.geometric_edge` (the gap/height/bottom edge rule).
-"""
-from pathlib import Path
-import sys
-import json
-import hashlib
-import statistics
-from collections import Counter, defaultdict
-
-DIR = Path(__file__).resolve().parent
-sys.path.insert(0, str(DIR))
-from analyze import geometric_edge
-from collect import rows_from_boxes
-from eval_identity import code_revision
-
-# Declared before looking at any hit count: a member box is "unchanged" while it
-# keeps IoU >= 0.9 with the box the edge was cached against, i.e. under ~10% of
-# the smaller box's area moved between the frame the edge was measured on and
-# the frame it is consulted.
-DECLARED_KEEP_IOU = 0.9
-SENSITIVITY_IOU = (0.8, 0.9, 0.95, 1.0)
-PRIMARY = 'replay-image6-8fps-1x-1.json'
-GROUPS = {'arms': 'evidence', 'legacy_label_anchor_arms': 'evidence/initial-replays'}
-
-
-def iou(a, b):
-    inter = max(0.0, min(a[2], b[2]) - max(a[0], b[0])) * max(0.0, min(a[3], b[3]) - max(a[1], b[1]))
-    union = (a[2] - a[0]) * (a[3] - a[1]) + (b[2] - b[0]) * (b[3] - b[1]) - inter
-    return inter / union if union > 0 else 0.0
-
-
-def edge_signals(a, b):
-    """collect.pair_signals' geometry half: a is left of b in a rows_from_boxes row."""
-    aw, ah = a[2] - a[0], a[3] - a[1]
-    bw, bh = b[2] - b[0], b[3] - b[1]
-    return {'gap': (b[0] - a[2]) / max(aw, bw), 'height': min(ah, bh) / max(ah, bh), 'bottom': abs(a[3] - b[3]) / max(ah, bh)}
-
-
-class PairCache:
-    """Edge memo keyed by track identities AND object generations.
-
-    A cached edge is reused only when the pair is still adjacent, both members
-    still carry the generation it was cached under, and both boxes are still
-    inside the declared IoU of the boxes it was cached against. Anything else
-    is a miss, and a miss rewrites the entry.
-    """
-    def __init__(self, keep_iou=DECLARED_KEEP_IOU):
-        self.keep_iou = keep_iou
-        self.entries = {}
-        self.lookups = self.hits = self.misses = self.updates = 0
-        self.drift = []
-        self.reasons = Counter()
-
-    @staticmethod
-    def pair(ka, kb):
-        return (ka[0], kb[0]) if ka[0] <= kb[0] else (kb[0], ka[0])
-
-    def get(self, ka, kb, ba, bb, adjacent=True):
-        self.lookups += 1
-        if not adjacent:
-            return self._miss('adjacency')
-        entry = self.entries.get(self.pair(ka, kb))
-        if entry is None:
-            return self._miss('new')
-        if entry['keys'] != (ka, kb):
-            return self._miss('generation')
-        if iou(ba, entry['boxes'][ka]) < self.keep_iou or iou(bb, entry['boxes'][kb]) < self.keep_iou:
-            return self._miss('geometry')
-        self.hits += 1
-        self.drift += [iou(ba, entry['boxes'][ka]), iou(bb, entry['boxes'][kb])]
-        return entry
-
-    def put(self, ka, kb, ba, bb, edge, row):
-        pair = self.pair(ka, kb)
-        if pair in self.entries:
-            self.updates += 1
-        self.entries[pair] = {'keys': (ka, kb), 'boxes': {ka: ba, kb: bb}, 'edge': edge, 'row': row}
-
-    def _miss(self, reason):
-        self.misses += 1
-        self.reasons[reason] += 1
-        return None
-
-
-def self_check():
-    """A cached edge must not survive a generation change or an adjacency change."""
-    a, b = [10, 100, 90, 300], [95, 102, 175, 300]
-    ka, kb = (7, 0), (9, 0)
-    assert geometric_edge({'signals': edge_signals(a, b)}) is True
-    c = PairCache()
-    assert c.get(ka, kb, a, b) is None and c.reasons['new'] == 1
-    c.put(ka, kb, a, b, True, 0)
-    assert c.get(ka, kb, a, b)['edge'] is True
-    assert c.get((7, 1), kb, a, b) is None and c.reasons['generation'] == 1
-    assert c.get((9, 1), ka, a, b) is None and c.reasons['generation'] == 2, 'unordered key must still catch a generation change'
-    assert c.get(ka, kb, a, b, adjacent=False) is None and c.reasons['adjacency'] == 1
-    assert c.get(ka, kb, [10, 100, 90, 420], b) is None and c.reasons['geometry'] == 1
-    assert c.get(ka, kb, a, b)['edge'] is True, 'a declined lookup must not evict a live entry'
-    return {'first_lookup': 'miss/new', 'repeat_lookup': 'hit', 'generation_invalidations': 2,
-            'adjacency_invalidations': 1, 'geometry_invalidations': 1, 'entry_survived_declined_lookups': True}
-
-
-def generations(timelines):
-    """Per-result track_id -> generation, plus where the value came from.
-
-    live.py increments TrackState.object_generation and _restart_count in the
-    same two branches -- a recycled id, and an id absent from the previous
-    frame -- off the same counters, so restarts == object_generation for every
-    track. object_generation is not in _track_payload, so on the wire restarts
-    is the only generation evidence. With neither recorded, the generation falls
-    back to a gap proxy, and the proxy is always reported next to the wire value
-    so the two can be compared instead of assumed equal.
-    """
-    per_result, proxy, recorded, previous = [], {}, set(), {}
-    for index, frame in enumerate(timelines):
-        current = {}
-        for track in frame['tracks']:
-            if track.get('object_generation') is not None:
-                recorded.add('object_generation')
-            if track.get('restarts') is not None:
-                recorded.add('restarts')
-        for track in frame['tracks']:
-            tid = track['id']
-            if tid in previous and index - previous[tid] > 1:
-                proxy[tid] = proxy.get(tid, 0) + 1
-            value = track.get('object_generation')
-            if value is None:
-                value = track.get('restarts')
-            current[tid] = int(value) if value is not None else proxy.get(tid, 0)
-        per_result.append(current)
-        previous = {track['id']: index for track in frame['tracks']}
-    source = next((name for name in ('object_generation', 'restarts') if name in recorded), 'absence_proxy')
-    return per_result, source, proxy
-
-
-def run(path, keep_iou):
-    data = json.loads(path.read_text())
-    timelines = data['timelines']
-    per_result, source, proxy = generations(timelines)
-    cache = PairCache(keep_iou)
-    named = defaultdict(list)
-    unique_ids, unique_pairs, previous_pairs = set(), set(), set()
-    per_result_pairs, rows_per_result, adjacency_rebuilds = [], [], 0
-    named_obs = unnamed_obs = degenerate = edge_yes = 0
-    final_generation, generation_history = {}, {}
-    for index, frame in enumerate(timelines):
-        tracks = []
-        for track in frame['tracks']:
-            box = track.get('box')
-            if not box or box[2] <= box[0] or box[3] <= box[1]:
-                degenerate += 1
-                continue
-            tracks.append({'id': track['id'], 'box': box})
-            unique_ids.add(track['id'])
-            final_generation[track['id']] = per_result[index].get(track['id'], 0)
-            generation_history.setdefault(track['id'], set()).add(final_generation[track['id']])
-            if track.get('sku'):
-                named[track['id']].append(track['sku'])
-                named_obs += 1
-            else:
-                unnamed_obs += 1
-        rows = rows_from_boxes(tracks)
-        current = set()
-        for row_index, row in enumerate(rows):
-            for left, right in zip(row, row[1:]):
-                ka = (left['id'], per_result[index].get(left['id'], 0))
-                kb = (right['id'], per_result[index].get(right['id'], 0))
-                entry = cache.get(ka, kb, left['box'], right['box'])
-                if entry is None:
-                    edge = geometric_edge({'signals': edge_signals(left['box'], right['box'])})
-                    edge_yes += int(edge)
-                    cache.put(ka, kb, left['box'], right['box'], edge, row_index)
-                else:
-                    edge_yes += int(entry['edge'])
-                current.add(cache.pair(ka, kb))
-        rows_per_result.append(len(rows))
-        per_result_pairs.append(len(current))
-        unique_pairs |= current
-        adjacency_rebuilds += len(current - previous_pairs)
-        previous_pairs = current
-    unchanged = changed = 0
-    for sequence in named.values():
-        for a, b in zip(sequence, sequence[1:]):
-            unchanged += int(a == b)
-            changed += int(a != b)
-    disagreeing = {}
-    for tid, values in generation_history.items():
-        wire_value = final_generation.get(tid, 0)
-        if len(values) > 1 or wire_value != proxy.get(tid, 0):
-            disagreeing[str(tid)] = {'wire': wire_value, 'gap_proxy': proxy.get(tid, 0),
-                                     'changed_during_run': len(values) > 1}
-    total = cache.lookups
-    return {
-        'results': len(timelines), 'schema_version': data.get('schema_version'),
-        'coordinate_basis': data.get('coordinate_basis'),
-        'source': data['summary']['source'], 'fps': data['summary']['fps'],
-        'rate': data['summary']['rate'], 'trial': data['summary']['trial'],
-        'frames': {'sent': data['summary']['sent'], 'returned': data['summary']['processed']},
-        'declared_keep_iou': keep_iou,
-        'rows': {'total': sum(rows_per_result), 'p50_per_result': statistics.median(rows_per_result),
-                 'max_per_result': max(rows_per_result, default=0)},
-        'adjacent_pairs': {'per_result_recompute_workload': sum(per_result_pairs),
-                           'p50_per_result': statistics.median(per_result_pairs),
-                           'unique_pairs': len(unique_pairs),
-                           'adjacency_rebuilds': adjacency_rebuilds},
-        'tracks': {'unique_ids': len(unique_ids), 'observations': named_obs + unnamed_obs,
-                   'named_observations': named_obs, 'unnamed_observations': unnamed_obs,
-                   'degenerate_boxes_skipped': degenerate,
-                   'named_tracks': len(named),
-                   'distinct_skus': len({sku for sequence in named.values() for sku in sequence})},
-        'cache': {'lookups': total, 'hits': cache.hits, 'misses': cache.misses,
-                  'hit_rate': cache.hits / total if total else None,
-                  'pair_computations_with_cache': cache.misses,
-                  'pair_computations_avoided': cache.hits,
-                  'miss_reasons': dict(cache.reasons), 'updates': cache.updates,
-                  'entries': len(cache.entries), 'edge_yes_computed_or_reused': edge_yes,
-                  'hit_drift_iou_vs_cached_box': {'min': min(cache.drift, default=None),
-                                                  'p50': statistics.median(cache.drift) if cache.drift else None,
-                                                  'max': max(cache.drift, default=None),
-                                                  'samples': len(cache.drift)}},
-        'generations': {
-            'source': source,
-            'wire_generation_events': sum(value for value in final_generation.values()),
-            'tracks_with_generation_change': sum(1 for values in generation_history.values() if len(values) > 1),
-            'gap_proxy_events': sum(proxy.values()),
-            'gap_proxy_disagreements': len(disagreeing),
-            'gap_proxy_disagreeing_tracks': disagreeing,
-            'generation_invalidations': cache.reasons['generation'],
-        },
-        'identity': {'named_transitions': unchanged + changed, 'unchanged': unchanged, 'changed': changed,
-                     'note': 'consecutive named observations of one track id; a change means the label flipped, '
-                             'repeated observations of an unchanged label count neither way'},
-    }
-
-
-def main():
-    report = {
-        'purpose': 'bounded diagnostic: how much of the geometry-only adjacent-pair work on a replay timeline '
-                   'is redundant between consecutive results',
-        'declared_criterion': {
-            'cache_key': '(track_id, generation) for both members, unordered',
-            'keep_iou': DECLARED_KEEP_IOU,
-            'reuse_requires': ['pair still adjacent in this result row ordering',
-                               'both member generations unchanged since the entry was written',
-                               f'both member boxes IoU >= {DECLARED_KEEP_IOU} with the boxes the entry was written against'],
-            'cached_value': 'the geometry-only edge verdict (analyze.geometric_edge over collect pair_signals geometry)',
-        },
-        'self_check': self_check(),
-        'provenance': {'code': code_revision()},
-        'arms': {},
-        'legacy_label_anchor_arms': {},
-        'sensitivity_hit_rate': {},
-        'limitations': [
-            'appearance: only the geometry-only adjacent edge is cacheable from a replay timeline. The appearance '
-            'half of analyze.pair_value (hue, pixels, logo_pixels, inliers, ocr_jaccard) needs per-track appearance '
-            'features a timeline does not carry, so this is the geometry/adjacency reuse ceiling, not a full '
-            'pair-score cache. The counters bound a cache of one boolean, not of descriptor cost.',
-            'object_generation: replay.py records it, but live.py _track_payload never populates it, so it is null on '
-            'every track in every arm. live.py increments object_generation and restarts in the same two branches '
-            'off the same counters, so restarts carries the generation and is what these arms key on. A cache key '
-            'naming object_generation specifically is therefore not yet constructible from the wire.',
-            'session generation: a method change bumps LiveSession._generation and resets every track in place, '
-            'leaving per-track object_generation untouched. A per-track generation key does not cover that; a real '
-            'cache needs the session generation in the key too, and no arm here exercises a method change.',
-            'absence: a gap between consecutive recorded results is not server absence. Arms return 9 of 40 sent '
-            'frames, so a track can be missing from a recorded result while the server never lost it. gap_proxy_events '
-            'is reported next to the wire generation and disagrees with it in exactly the arms listed under '
-            'gap_proxy_disagreeing_tracks; the wire value is the one used.',
-            'box basis differs between the two groups. `arms` is schema 2, coordinate_basis "displayed box mapped '
-            'with result frame origin". `legacy_label_anchor_arms` is the archived schema-1 run, where replay.py '
-            'took label_box (always non-empty, so raw_box/box never applied) and mapped it with the label frame '
-            'pan offset; an unlabelled track has label_frame_id == 0, so its box was mapped with frame 0 origin, '
-            'a pure x error of up to ~197 source px on image6 against a median box width of 89 px. The two groups '
-            'are not comparable to each other and are reported separately.',
-            'scope: arms differ only in pacing and source, and the still-image arms replay one development photo '
-            '(add/image6.jpg, 111 labelled facings). No independent generalization, and no native UI or device path '
-            'was exercised. Arms are also truncated under server load, so the named primary arm is the smallest of '
-            'the set and its counters are the weakest.',
-            'counters only: no latency saving is claimed, because the timeline carries no cost for the work a hit '
-            'skips, and the declared IoU is a geometric restatement of "the box did not move", not a measurement.',
-            'chained drift: the criterion compares each member against the box the entry was last written '
-            'against, not against the previous result, so a box that creeps a little every frame can stay under '
-            'the IoU forever while ending up far from where the verdict was computed. hit_drift_iou_vs_cached_box '
-            'is the observed bound on that; a production cache that needs a hard staleness bound should compare '
-            'against the previous result and cap the age of an entry.',
-        ],
-    }
-    for group, folder in GROUPS.items():
-        for path in sorted((DIR / folder).glob('replay-*.json')):
-            if 'timelines' not in json.loads(path.read_text()):
-                continue
-            arm = run(path, DECLARED_KEEP_IOU)
-            report[group][path.name] = arm
-            report['provenance'].setdefault('sources', []).append(
-                {'group': group, 'file': path.name, 'sha256': hashlib.sha256(path.read_bytes()).hexdigest(),
-                 'primary': path.name == PRIMARY and group == 'arms'})
-            report['sensitivity_hit_rate'].setdefault(group, {})[path.name] = {
-                str(value): run(path, value)['cache']['hit_rate'] for value in SENSITIVITY_IOU}
-    out = DIR / 'evidence/temporal-cache.json'
-    out.write_text(json.dumps(report, ensure_ascii=False, indent=2))
-    print('self_check', report['self_check'])
-    for group in GROUPS:
-        for name, arm in report[group].items():
-            print(group, name, 'results', arm['results'], 'sent/returned', arm['frames'],
-                  'unique_pairs', arm['adjacent_pairs']['unique_pairs'],
-                  'lookups', arm['cache']['lookups'], 'hits', arm['cache']['hits'],
-                  'misses', arm['cache']['misses'], 'hit_rate', arm['cache']['hit_rate'],
-                  'reasons', arm['cache']['miss_reasons'],
-                  'gen_source', arm['generations']['source'],
-                  'wire_gen_events', arm['generations']['wire_generation_events'],
-                  'proxy_disagree', arm['generations']['gap_proxy_disagreements'],
-                  'identity_unchanged', arm['identity']['unchanged'],
-                  'identity_changed', arm['identity']['changed'])
-    print('wrote', out)
-    if PRIMARY in report['arms']:
-        print('primary', PRIMARY, json.dumps(report['arms'][PRIMARY], indent=1)[:1400])
-
-
-if __name__ == '__main__':
-    main()
diff --git a/recognization/eval/labels/README.md b/recognization/eval/labels/README.md
deleted file mode 100644
index a2bee5d..0000000
--- a/recognization/eval/labels/README.md
+++ /dev/null
@@ -1,54 +0,0 @@
-# Shelf label files
-
-Ground truth for the identity evaluators (`code/eval_identity.py`,
-`code/eval_live.py`) and for the evidence scripts in
-`docs/research/2026-09-28-recognition-architecture/evidence/`.
-
-    {"image": "add/image6.jpg", "version": 2, "facings": [ ... ]}
-
-`image` is the photo the boxes index into. `facings` is one row per detected
-product facing, in that photo's own pixel coordinates.
-
-## Row schema
-
-| Field | Required | Meaning |
-|---|---|---|
-| `index` | yes | Row identity. Stable across label revisions, so a run's per-facing log joins to truth by `index`, never by array position. |
-| `box` | yes | `[x1, y1, x2, y2]` in `image` pixels. |
-| `label` | yes | Canonical SKU id (`sku_l01`), `unknown` for a facing that is not in the catalog, or `exclude` for a facing no lane can be scored on. |
-| `source` | yes | How the label was decided. `v1-confirmed-visual` = the v1 label was checked against the package and kept; `visual-audit-2026-09-28` = the label was re-read from the package in the 2026-09-28 audit. |
-| `v1_label` | yes | The label this row carried in v1. Kept so claims computed on v1 stay reproducible and the 26 corrections stay auditable. |
-| `note` | when `label != v1_label` | Why the label changed. Also on a few unchanged rows that need a remark. |
-| `match` | no | v1 provenance only: the field DECISION.md §3 reads as a match IoU, and as evidence that v1 labels were propagated from tag or box matching rather than read off packages. Values here are 0.0, 0.911, 1.0. No scorer reads it; it is carried through unchanged for traceability. |
-
-`label` takes a canonical SKU id, `unknown`, or `exclude`. This file uses
-`sku_l01`-`sku_l12`. `exclude` rows stay in the file so the facing inventory is
-complete; every scorer drops them before computing a rate. The evaluators read
-`index`, `box`, and `label`; the provenance fields are additive.
-
-`image6.json` is the corrected v2 set: 111 rows, 109 scored (2 `exclude`), 64
-in-catalog across 12 SKUs, 45 `unknown`. 26 labels changed from v1, and three
-more changed on 2026-09-29: facings #46-48 were `unknown` with the note
-"possibly what stock.csv calls l12", and the l12 reconciliation settled it, so
-they now carry `sku_l12`. This file is the single copy: the evidence directory
-used to hold a duplicate (`image6_v2.json`) and it was removed on 2026-09-29
-after `cmp` showed the two byte-identical, because a copy that nothing reads is
-a copy that drifts.
-
-## Schema check
-
-DECISION.md §7 sets the rule for every label file:
-
-> Every label row carries `source`, `confidence`, and `labeler`. A label file
-> without them fails a schema check.
-
-The current file does not meet it. All 111 rows carry `source` and `v1_label`,
-but none carries `confidence` or `labeler`: the file records where each label
-came from and what it replaced, not a per-row certainty or a labeler name.
-Inventing confidence values after the fact would be worse than the gap, so they
-are absent rather than guessed.
-
-Tier-2 labels must add both. `confidence` is the labeler's own certainty for
-that row; `labeler` names the person or pass that produced it, because Tier 2 is
-double-labelled and a disagreement has to be traceable to a pass. Tier 1
-(`image6.json`) is dev-only and its numbers never ship, so it stays as it is.
diff --git a/recognization/eval/labels/image6.json b/recognization/eval/labels/image6.json
deleted file mode 100644
index d35723b..0000000
--- a/recognization/eval/labels/image6.json
+++ /dev/null
@@ -1,1478 +0,0 @@
-{
- "image": "add/image6.jpg",
- "facings": [
-  {
-   "box": [
-    39,
-    56,
-    143,
-    313
-   ],
-   "label": "sku_l01",
-   "match": 1.0,
-   "index": 0,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l01"
-  },
-  {
-   "box": [
-    147,
-    53,
-    264,
-    318
-   ],
-   "label": "sku_l01",
-   "match": 1.0,
-   "index": 1,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l01"
-  },
-  {
-   "box": [
-    1419,
-    0,
-    1500,
-    107
-   ],
-   "label": "exclude",
-   "match": 1.0,
-   "index": 2,
-   "note": "blurred corner of upper shelf; not identifiable",
-   "source": "visual-audit-2026-09-28",
-   "v1_label": "sku_l04"
-  },
-  {
-   "box": [
-    270,
-    64,
-    377,
-    322
-   ],
-   "label": "sku_l01",
-   "match": 1.0,
-   "index": 3,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l01"
-  },
-  {
-   "box": [
-    382,
-    72,
-    467,
-    324
-   ],
-   "label": "sku_l01",
-   "match": 0.0,
-   "index": 4,
-   "note": "Egg Care front, same tag run as #0-3",
-   "source": "visual-audit-2026-09-28",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    472,
-    64,
-    570,
-    328
-   ],
-   "label": "sku_l01",
-   "match": 0.0,
-   "index": 5,
-   "note": "Egg Care front",
-   "source": "visual-audit-2026-09-28",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    575,
-    137,
-    639,
-    330
-   ],
-   "label": "sku_l02",
-   "match": 1.0,
-   "index": 6,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l02"
-  },
-  {
-   "box": [
-    641,
-    126,
-    732,
-    337
-   ],
-   "label": "sku_l02",
-   "match": 1.0,
-   "index": 7,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l02"
-  },
-  {
-   "box": [
-    736,
-    123,
-    817,
-    339
-   ],
-   "label": "sku_l02",
-   "match": 1.0,
-   "index": 8,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l02"
-  },
-  {
-   "box": [
-    822,
-    135,
-    909,
-    345
-   ],
-   "label": "sku_l02",
-   "match": 1.0,
-   "index": 9,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l02"
-  },
-  {
-   "box": [
-    959,
-    160,
-    1044,
-    349
-   ],
-   "label": "sku_l03",
-   "match": 1.0,
-   "index": 10,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l03"
-  },
-  {
-   "box": [
-    1049,
-    158,
-    1138,
-    348
-   ],
-   "label": "sku_l03",
-   "match": 1.0,
-   "index": 11,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l03"
-  },
-  {
-   "box": [
-    1143,
-    144,
-    1228,
-    353
-   ],
-   "label": "sku_l03",
-   "match": 1.0,
-   "index": 12,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l03"
-  },
-  {
-   "box": [
-    1233,
-    147,
-    1352,
-    356
-   ],
-   "label": "sku_l03",
-   "match": 1.0,
-   "index": 13,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l03"
-  },
-  {
-   "box": [
-    1375,
-    152,
-    1475,
-    365
-   ],
-   "label": "sku_l04",
-   "match": 1.0,
-   "index": 14,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l04"
-  },
-  {
-   "box": [
-    1482,
-    155,
-    1500,
-    363
-   ],
-   "label": "exclude",
-   "match": 0.0,
-   "index": 15,
-   "note": "18 px sliver",
-   "source": "visual-audit-2026-09-28",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    0,
-    472,
-    76,
-    662
-   ],
-   "label": "unknown",
-   "match": 1.0,
-   "index": 16,
-   "note": "garlic jar, not mayo",
-   "source": "visual-audit-2026-09-28",
-   "v1_label": "sku_l05"
-  },
-  {
-   "box": [
-    86,
-    420,
-    163,
-    664
-   ],
-   "label": "sku_l05",
-   "match": 1.0,
-   "index": 17,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l05"
-  },
-  {
-   "box": [
-    167,
-    420,
-    265,
-    662
-   ],
-   "label": "sku_l05",
-   "match": 1.0,
-   "index": 18,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l05"
-  },
-  {
-   "box": [
-    270,
-    426,
-    366,
-    664
-   ],
-   "label": "sku_l05",
-   "match": 1.0,
-   "index": 19,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l05"
-  },
-  {
-   "box": [
-    476,
-    446,
-    571,
-    669
-   ],
-   "label": "sku_l06",
-   "match": 1.0,
-   "index": 20,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l06"
-  },
-  {
-   "box": [
-    583,
-    458,
-    678,
-    672
-   ],
-   "label": "sku_l06",
-   "match": 1.0,
-   "index": 21,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l06"
-  },
-  {
-   "box": [
-    683,
-    454,
-    774,
-    672
-   ],
-   "label": "sku_l06",
-   "match": 0.0,
-   "index": 22,
-   "note": "Fitte front",
-   "source": "visual-audit-2026-09-28",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    778,
-    459,
-    865,
-    675
-   ],
-   "label": "sku_l06",
-   "match": 0.0,
-   "index": 23,
-   "note": "Fitte front",
-   "source": "visual-audit-2026-09-28",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    871,
-    466,
-    945,
-    674
-   ],
-   "label": "sku_l07",
-   "match": 1.0,
-   "index": 24,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l07"
-  },
-  {
-   "box": [
-    949,
-    471,
-    1020,
-    674
-   ],
-   "label": "sku_l07",
-   "match": 1.0,
-   "index": 25,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l07"
-  },
-  {
-   "box": [
-    1024,
-    493,
-    1098,
-    676
-   ],
-   "label": "sku_l07",
-   "match": 1.0,
-   "index": 26,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l07"
-  },
-  {
-   "box": [
-    1103,
-    494,
-    1176,
-    676
-   ],
-   "label": "sku_l07",
-   "match": 0.0,
-   "index": 27,
-   "note": "Lucky Mayonnaise front",
-   "source": "visual-audit-2026-09-28",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    1181,
-    499,
-    1245,
-    676
-   ],
-   "label": "sku_l07",
-   "match": 0.0,
-   "index": 28,
-   "note": "Lucky Mayonnaise front",
-   "source": "visual-audit-2026-09-28",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    1249,
-    480,
-    1331,
-    676
-   ],
-   "label": "sku_l07",
-   "match": 1.0,
-   "index": 29,
-   "note": "Lucky Mayonnaise front; was l08",
-   "source": "visual-audit-2026-09-28",
-   "v1_label": "sku_l08"
-  },
-  {
-   "box": [
-    1336,
-    502,
-    1421,
-    682
-   ],
-   "label": "sku_l08",
-   "match": 1.0,
-   "index": 30,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l08"
-  },
-  {
-   "box": [
-    0,
-    751,
-    108,
-    977
-   ],
-   "label": "unknown",
-   "match": 1.0,
-   "index": 31,
-   "note": "demi-glace sauce pouch; was l09",
-   "source": "visual-audit-2026-09-28",
-   "v1_label": "sku_l09"
-  },
-  {
-   "box": [
-    115,
-    758,
-    193,
-    973
-   ],
-   "label": "unknown",
-   "match": 1.0,
-   "index": 32,
-   "note": "demi-glace sauce pouch; was l09",
-   "source": "visual-audit-2026-09-28",
-   "v1_label": "sku_l09"
-  },
-  {
-   "box": [
-    199,
-    758,
-    255,
-    971
-   ],
-   "label": "sku_l09",
-   "match": 1.0,
-   "index": 33,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l09"
-  },
-  {
-   "box": [
-    258,
-    758,
-    352,
-    976
-   ],
-   "label": "sku_l09",
-   "match": 1.0,
-   "index": 34,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l09"
-  },
-  {
-   "box": [
-    357,
-    761,
-    449,
-    973
-   ],
-   "label": "sku_l09",
-   "match": 1.0,
-   "index": 35,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l09"
-  },
-  {
-   "box": [
-    452,
-    755,
-    514,
-    972
-   ],
-   "label": "sku_l09",
-   "match": 1.0,
-   "index": 36,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l09"
-  },
-  {
-   "box": [
-    519,
-    759,
-    608,
-    974
-   ],
-   "label": "sku_l09",
-   "match": 1.0,
-   "index": 37,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l09"
-  },
-  {
-   "box": [
-    614,
-    759,
-    700,
-    973
-   ],
-   "label": "sku_l09",
-   "match": 1.0,
-   "index": 38,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l09"
-  },
-  {
-   "box": [
-    704,
-    763,
-    791,
-    973
-   ],
-   "label": "sku_l09",
-   "match": 1.0,
-   "index": 39,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l09"
-  },
-  {
-   "box": [
-    798,
-    765,
-    916,
-    974
-   ],
-   "label": "sku_l09",
-   "match": 0.0,
-   "index": 40,
-   "note": "Kokuuma 65% front",
-   "source": "visual-audit-2026-09-28",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    919,
-    763,
-    973,
-    974
-   ],
-   "label": "sku_l09",
-   "match": 0.0,
-   "index": 41,
-   "note": "Kokuuma 65% partly occluded",
-   "source": "visual-audit-2026-09-28",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    977,
-    765,
-    1067,
-    974
-   ],
-   "label": "sku_l09",
-   "match": 0.0,
-   "index": 42,
-   "note": "Kokuuma 65% front",
-   "source": "visual-audit-2026-09-28",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    1070,
-    762,
-    1134,
-    975
-   ],
-   "label": "sku_l09",
-   "match": 0.0,
-   "index": 43,
-   "note": "Kokuuma 65% partly occluded",
-   "source": "visual-audit-2026-09-28",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    1138,
-    767,
-    1218,
-    973
-   ],
-   "label": "sku_l09",
-   "match": 0.0,
-   "index": 44,
-   "note": "Kokuuma 65% front",
-   "source": "visual-audit-2026-09-28",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    1221,
-    768,
-    1281,
-    972
-   ],
-   "label": "sku_l09",
-   "match": 0.0,
-   "index": 45,
-   "note": "Kokuuma 65% partly occluded",
-   "source": "visual-audit-2026-09-28",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    1285,
-    794,
-    1359,
-    972
-   ],
-   "label": "sku_l12",
-   "match": 0.0,
-   "index": 46,
-   "note": "ユーラス 野菜をおいしく食べる旨たれ 380g; possibly what stock.csv calls l12 | Relabelled to sku_l12: the shelf tag under these facings reads ユーラス 野菜をおいしく食べる旨タレ (380g) at ¥299, the price the l12 row carries, and the row was renamed to match (data/external-eval/l12-reconciliation/manifest.json).",
-   "source": "l12-reconciliation-2026-09-29",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    1363,
-    794,
-    1440,
-    973
-   ],
-   "label": "sku_l12",
-   "match": 0.0,
-   "index": 47,
-   "note": "same tare bottle | Relabelled to sku_l12: the shelf tag under these facings reads ユーラス 野菜をおいしく食べる旨タレ (380g) at ¥299, the price the l12 row carries, and the row was renamed to match (data/external-eval/l12-reconciliation/manifest.json).",
-   "source": "l12-reconciliation-2026-09-29",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    1443,
-    794,
-    1500,
-    973
-   ],
-   "label": "sku_l12",
-   "match": 0.0,
-   "index": 48,
-   "note": "same tare bottle, partial | Relabelled to sku_l12: the shelf tag under these facings reads ユーラス 野菜をおいしく食べる旨タレ (380g) at ¥299, the price the l12 row carries, and the row was renamed to match (data/external-eval/l12-reconciliation/manifest.json).",
-   "source": "l12-reconciliation-2026-09-29",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    191,
-    1060,
-    297,
-    1266
-   ],
-   "label": "sku_l10",
-   "match": 1.0,
-   "index": 49,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l10"
-  },
-  {
-   "box": [
-    306,
-    1058,
-    436,
-    1260
-   ],
-   "label": "sku_l10",
-   "match": 1.0,
-   "index": 50,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l10"
-  },
-  {
-   "box": [
-    441,
-    1060,
-    538,
-    1256
-   ],
-   "label": "sku_l10",
-   "match": 1.0,
-   "index": 51,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l10"
-  },
-  {
-   "box": [
-    542,
-    1060,
-    628,
-    1251
-   ],
-   "label": "sku_l10",
-   "match": 1.0,
-   "index": 52,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l10"
-  },
-  {
-   "box": [
-    630,
-    1055,
-    680,
-    1249
-   ],
-   "label": "sku_l10",
-   "match": 1.0,
-   "index": 53,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l10"
-  },
-  {
-   "box": [
-    685,
-    1058,
-    783,
-    1251
-   ],
-   "label": "sku_l10",
-   "match": 1.0,
-   "index": 54,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l10"
-  },
-  {
-   "box": [
-    789,
-    1056,
-    876,
-    1246
-   ],
-   "label": "sku_l10",
-   "match": 1.0,
-   "index": 55,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l10"
-  },
-  {
-   "box": [
-    878,
-    1055,
-    968,
-    1246
-   ],
-   "label": "sku_l10",
-   "match": 1.0,
-   "index": 56,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l10"
-  },
-  {
-   "box": [
-    1044,
-    1054,
-    1113,
-    1248
-   ],
-   "label": "sku_l10",
-   "match": 1.0,
-   "index": 57,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l10"
-  },
-  {
-   "box": [
-    1114,
-    1051,
-    1196,
-    1247
-   ],
-   "label": "sku_l10",
-   "match": 1.0,
-   "index": 58,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l10"
-  },
-  {
-   "box": [
-    1199,
-    1050,
-    1280,
-    1247
-   ],
-   "label": "sku_l10",
-   "match": 0.0,
-   "index": 59,
-   "note": "Pure Select mayonnaise front",
-   "source": "visual-audit-2026-09-28",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    1280,
-    1061,
-    1317,
-    1244
-   ],
-   "label": "unknown",
-   "match": 0.0,
-   "index": 60,
-   "source": "v1-confirmed-visual",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    1319,
-    1068,
-    1389,
-    1240
-   ],
-   "label": "unknown",
-   "match": 0.0,
-   "index": 61,
-   "source": "v1-confirmed-visual",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    1393,
-    1067,
-    1467,
-    1241
-   ],
-   "label": "unknown",
-   "match": 0.0,
-   "index": 62,
-   "source": "v1-confirmed-visual",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    1478,
-    1068,
-    1500,
-    1242
-   ],
-   "label": "unknown",
-   "match": 0.0,
-   "index": 63,
-   "source": "v1-confirmed-visual",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    64,
-    1142,
-    150,
-    1264
-   ],
-   "label": "unknown",
-   "match": 1.0,
-   "index": 64,
-   "note": "Heinz can; was l10",
-   "source": "visual-audit-2026-09-28",
-   "v1_label": "sku_l10"
-  },
-  {
-   "box": [
-    0,
-    1379,
-    51,
-    1561
-   ],
-   "label": "unknown",
-   "match": 0.0,
-   "index": 65,
-   "source": "v1-confirmed-visual",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    60,
-    1374,
-    163,
-    1560
-   ],
-   "label": "unknown",
-   "match": 0.0,
-   "index": 66,
-   "source": "v1-confirmed-visual",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    190,
-    1339,
-    296,
-    1562
-   ],
-   "label": "sku_l11",
-   "match": 1.0,
-   "index": 67,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l11"
-  },
-  {
-   "box": [
-    298,
-    1335,
-    404,
-    1558
-   ],
-   "label": "sku_l11",
-   "match": 1.0,
-   "index": 68,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l11"
-  },
-  {
-   "box": [
-    421,
-    1333,
-    524,
-    1549
-   ],
-   "label": "sku_l11",
-   "match": 1.0,
-   "index": 69,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l11"
-  },
-  {
-   "box": [
-    529,
-    1332,
-    626,
-    1546
-   ],
-   "label": "sku_l11",
-   "match": 1.0,
-   "index": 70,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l11"
-  },
-  {
-   "box": [
-    634,
-    1328,
-    729,
-    1541
-   ],
-   "label": "sku_l11",
-   "match": 1.0,
-   "index": 71,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l11"
-  },
-  {
-   "box": [
-    734,
-    1326,
-    831,
-    1538
-   ],
-   "label": "sku_l11",
-   "match": 1.0,
-   "index": 72,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l11"
-  },
-  {
-   "box": [
-    836,
-    1326,
-    924,
-    1535
-   ],
-   "label": "sku_l11",
-   "match": 1.0,
-   "index": 73,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l11"
-  },
-  {
-   "box": [
-    930,
-    1324,
-    1018,
-    1532
-   ],
-   "label": "sku_l11",
-   "match": 1.0,
-   "index": 74,
-   "source": "v1-confirmed-visual",
-   "v1_label": "sku_l11"
-  },
-  {
-   "box": [
-    1022,
-    1320,
-    1123,
-    1530
-   ],
-   "label": "sku_l11",
-   "match": 1.0,
-   "index": 75,
-   "note": "Kewpie Half front; was l12",
-   "source": "visual-audit-2026-09-28",
-   "v1_label": "sku_l12"
-  },
-  {
-   "box": [
-    1190,
-    1331,
-    1262,
-    1521
-   ],
-   "label": "unknown",
-   "match": 1.0,
-   "index": 76,
-   "note": "Vegiglet dressing; was l12",
-   "source": "visual-audit-2026-09-28",
-   "v1_label": "sku_l12"
-  },
-  {
-   "box": [
-    1266,
-    1337,
-    1334,
-    1516
-   ],
-   "label": "unknown",
-   "match": 1.0,
-   "index": 77,
-   "note": "Vegiglet dressing; was l12",
-   "source": "visual-audit-2026-09-28",
-   "v1_label": "sku_l12"
-  },
-  {
-   "box": [
-    1337,
-    1335,
-    1403,
-    1514
-   ],
-   "label": "unknown",
-   "match": 1.0,
-   "index": 78,
-   "note": "Vegiglet dressing; was l12",
-   "source": "visual-audit-2026-09-28",
-   "v1_label": "sku_l12"
-  },
-  {
-   "box": [
-    1409,
-    1336,
-    1475,
-    1511
-   ],
-   "label": "unknown",
-   "match": 1.0,
-   "index": 79,
-   "note": "Vegiglet dressing; was l12",
-   "source": "visual-audit-2026-09-28",
-   "v1_label": "sku_l12"
-  },
-  {
-   "box": [
-    1480,
-    1327,
-    1500,
-    1514
-   ],
-   "label": "unknown",
-   "match": 0.0,
-   "index": 80,
-   "source": "v1-confirmed-visual",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    593,
-    1619,
-    679,
-    1815
-   ],
-   "label": "unknown",
-   "match": 0.0,
-   "index": 81,
-   "source": "v1-confirmed-visual",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    683,
-    1617,
-    762,
-    1809
-   ],
-   "label": "unknown",
-   "match": 0.0,
-   "index": 82,
-   "source": "v1-confirmed-visual",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    766,
-    1612,
-    839,
-    1806
-   ],
-   "label": "unknown",
-   "match": 0.0,
-   "index": 83,
-   "source": "v1-confirmed-visual",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    842,
-    1609,
-    909,
-    1801
-   ],
-   "label": "unknown",
-   "match": 0.0,
-   "index": 84,
-   "source": "v1-confirmed-visual",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    910,
-    1607,
-    995,
-    1796
-   ],
-   "label": "unknown",
-   "match": 0.911,
-   "index": 85,
-   "note": "Kewpie Mayonnaise 450g (not catalog); was l03",
-   "source": "visual-audit-2026-09-28",
-   "v1_label": "sku_l03"
-  },
-  {
-   "box": [
-    999,
-    1602,
-    1078,
-    1793
-   ],
-   "label": "unknown",
-   "match": 0.0,
-   "index": 86,
-   "source": "v1-confirmed-visual",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    1083,
-    1600,
-    1169,
-    1787
-   ],
-   "label": "unknown",
-   "match": 0.0,
-   "index": 87,
-   "source": "v1-confirmed-visual",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    1173,
-    1595,
-    1269,
-    1783
-   ],
-   "label": "unknown",
-   "match": 0.0,
-   "index": 88,
-   "source": "v1-confirmed-visual",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    1306,
-    1586,
-    1388,
-    1771
-   ],
-   "label": "unknown",
-   "match": 0.0,
-   "index": 89,
-   "source": "v1-confirmed-visual",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    1390,
-    1602,
-    1457,
-    1767
-   ],
-   "label": "unknown",
-   "match": 0.0,
-   "index": 90,
-   "source": "v1-confirmed-visual",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    1461,
-    1601,
-    1499,
-    1769
-   ],
-   "label": "unknown",
-   "match": 0.0,
-   "index": 91,
-   "source": "v1-confirmed-visual",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    7,
-    1632,
-    103,
-    1845
-   ],
-   "label": "unknown",
-   "match": 0.0,
-   "index": 92,
-   "source": "v1-confirmed-visual",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    107,
-    1630,
-    199,
-    1844
-   ],
-   "label": "unknown",
-   "match": 0.0,
-   "index": 93,
-   "source": "v1-confirmed-visual",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    213,
-    1631,
-    313,
-    1834
-   ],
-   "label": "unknown",
-   "match": 0.0,
-   "index": 94,
-   "source": "v1-confirmed-visual",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    318,
-    1628,
-    405,
-    1829
-   ],
-   "label": "unknown",
-   "match": 0.0,
-   "index": 95,
-   "source": "v1-confirmed-visual",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    409,
-    1626,
-    491,
-    1825
-   ],
-   "label": "unknown",
-   "match": 0.0,
-   "index": 96,
-   "source": "v1-confirmed-visual",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    494,
-    1622,
-    589,
-    1821
-   ],
-   "label": "unknown",
-   "match": 0.0,
-   "index": 97,
-   "source": "v1-confirmed-visual",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    1059,
-    1855,
-    1197,
-    2000
-   ],
-   "label": "unknown",
-   "match": 0.0,
-   "index": 98,
-   "source": "v1-confirmed-visual",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    1216,
-    1839,
-    1309,
-    2000
-   ],
-   "label": "unknown",
-   "match": 0.0,
-   "index": 99,
-   "source": "v1-confirmed-visual",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    1313,
-    1835,
-    1397,
-    1999
-   ],
-   "label": "unknown",
-   "match": 0.0,
-   "index": 100,
-   "source": "v1-confirmed-visual",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    1404,
-    1837,
-    1498,
-    2000
-   ],
-   "label": "unknown",
-   "match": 0.0,
-   "index": 101,
-   "source": "v1-confirmed-visual",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    0,
-    1918,
-    75,
-    1998
-   ],
-   "label": "unknown",
-   "match": 0.0,
-   "index": 102,
-   "source": "v1-confirmed-visual",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    82,
-    1910,
-    187,
-    1999
-   ],
-   "label": "unknown",
-   "match": 0.0,
-   "index": 103,
-   "source": "v1-confirmed-visual",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    214,
-    1897,
-    506,
-    1998
-   ],
-   "label": "unknown",
-   "match": 0.0,
-   "index": 104,
-   "source": "v1-confirmed-visual",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    219,
-    1906,
-    362,
-    2000
-   ],
-   "label": "unknown",
-   "match": 0.0,
-   "index": 105,
-   "source": "v1-confirmed-visual",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    363,
-    1911,
-    500,
-    1999
-   ],
-   "label": "unknown",
-   "match": 0.0,
-   "index": 106,
-   "source": "v1-confirmed-visual",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    516,
-    1882,
-    591,
-    2000
-   ],
-   "label": "unknown",
-   "match": 0.0,
-   "index": 107,
-   "source": "v1-confirmed-visual",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    646,
-    1886,
-    786,
-    2000
-   ],
-   "label": "unknown",
-   "match": 0.0,
-   "index": 108,
-   "source": "v1-confirmed-visual",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    790,
-    1882,
-    887,
-    2000
-   ],
-   "label": "unknown",
-   "match": 0.0,
-   "index": 109,
-   "source": "v1-confirmed-visual",
-   "v1_label": "unknown"
-  },
-  {
-   "box": [
-    895,
-    1862,
-    1044,
-    2000
-   ],
-   "label": "unknown",
-   "match": 0.0,
-   "index": 110,
-   "source": "v1-confirmed-visual",
-   "v1_label": "unknown"
-  }
- ],
- "version": 2
-}
\ No newline at end of file
diff --git a/recognization/pyproject.toml b/recognization/pyproject.toml
deleted file mode 100644
index a97a54a..0000000
--- a/recognization/pyproject.toml
+++ /dev/null
@@ -1,33 +0,0 @@
-[project]
-name = "dorayaki-recognition"
-version = "0.1.0"
-description = "Dorayaki shelf recognition server"
-requires-python = ">=3.10,<3.13"
-readme = "README.md"
-dependencies = [
-    "torch>=2.0.0",
-    "torchvision>=0.15.0",
-    "ultralytics==8.4.153",
-    "lap>=0.5.12",
-    "websockets>=13",
-    "opencv-python>=4.8.0",
-    "numpy>=1.23.0,<2.0.0",
-    "fastapi>=0.100.0",
-    "uvicorn>=0.20.0",
-    "python-multipart>=0.0.6",
-    "requests>=2.28.0",
-    "pyobjc-framework-Vision>=10; sys_platform == 'darwin'",
-    "pyobjc-framework-Quartz>=10; sys_platform == 'darwin'",
-]
-
-[project.optional-dependencies]
-dev = ["httpx>=0.27"]
-
-[tool.pytest.ini_options]
-testpaths = ["code"]
-python_files = ["test_*.py"]
-python_functions = ["test_*"]
-addopts = ["-q"]
-
-[tool.setuptools]
-py-modules = []
diff --git a/recognization/requirements-dev.txt b/recognization/requirements-dev.txt
deleted file mode 100644
index 0d10685..0000000
--- a/recognization/requirements-dev.txt
+++ /dev/null
@@ -1,3 +0,0 @@
--r requirements.txt
-# FastAPI TestClient
-httpx>=0.27
diff --git a/recognization/requirements.lock b/recognization/requirements.lock
deleted file mode 100644
index 06fa8eb..0000000
--- a/recognization/requirements.lock
+++ /dev/null
@@ -1,79 +0,0 @@
-# This file was autogenerated by uv via the following command:
-#    uv pip compile requirements.txt requirements-dev.txt --universal --python-version 3.12 -o requirements.lock --no-annotate
-annotated-doc==0.0.5
-annotated-types==0.8.0
-anyio==4.15.1
-certifi==2026.7.22
-charset-normalizer==3.5.1
-click==8.5.0
-cloudpickle==3.1.2
-contourpy==1.3.3
-cuda-bindings==13.4.3 ; python_full_version < '3.15' and sys_platform == 'linux'
-cuda-pathfinder==1.8.2 ; python_full_version < '3.15' and sys_platform == 'linux'
-cuda-toolkit==13.0.3.0 ; sys_platform == 'linux'
-cycler==0.12.1
-fastapi==0.141.1
-filelock==4.0.4
-fonttools==4.66.0
-fsspec==2026.9.0
-h11==0.16.0
-httpcore==1.0.9
-httpx==0.28.1
-idna==3.20
-jinja2==3.1.6
-kiwisolver==1.5.1
-lap==0.5.13
-markupsafe==3.0.3
-matplotlib==3.11.2
-mpmath==1.3.0
-networkx==3.7
-numpy==1.26.4
-nvidia-cublas==13.1.1.3 ; sys_platform == 'linux'
-nvidia-cuda-cupti==13.0.85 ; (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')
-nvidia-cuda-nvrtc==13.0.88 ; sys_platform == 'linux'
-nvidia-cuda-runtime==13.0.96 ; (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')
-nvidia-cudnn-cu13==9.24.0.43 ; sys_platform == 'linux'
-nvidia-cufft==12.0.0.61 ; (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')
-nvidia-cufile==1.15.1.6 ; (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')
-nvidia-curand==10.4.0.35 ; (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')
-nvidia-cusolver==12.0.4.66 ; (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')
-nvidia-cusparse==12.6.3.3 ; (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')
-nvidia-cusparselt-cu13==0.8.1 ; sys_platform == 'linux'
-nvidia-ml-py==13.615.71
-nvidia-nccl-cu13==2.30.7 ; sys_platform == 'linux'
-nvidia-nvjitlink==13.4.92 ; (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')
-nvidia-nvshmem-cu13==3.4.5 ; sys_platform == 'linux'
-nvidia-nvtx==13.0.85 ; (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')
-opencv-python==4.11.0.86
-packaging==26.3
-pillow==12.3.0
-polars==1.44.2
-polars-runtime-32==1.44.2
-psutil==7.2.2
-pydantic==2.13.5
-pydantic-core==2.46.5
-pyobjc-core==12.2.2 ; sys_platform == 'darwin'
-pyobjc-framework-cocoa==12.2.2 ; sys_platform == 'darwin'
-pyobjc-framework-coreml==12.2.2 ; sys_platform == 'darwin'
-pyobjc-framework-quartz==12.2.2 ; sys_platform == 'darwin'
-pyobjc-framework-vision==12.2.2 ; sys_platform == 'darwin'
-pyparsing==3.3.3
-python-dateutil==2.9.0.post0
-python-multipart==0.0.32
-pyyaml==6.0.3
-requests==2.34.2
-setuptools==84.0.0
-six==1.17.0
-starlette==1.7.0
-sympy==1.14.0
-torch==2.14.0
-torchvision==0.29.0
-triton==3.8.0 ; python_full_version < '3.15' and sys_platform == 'linux'
-typing-extensions==4.16.0
-typing-inspection==0.4.4
-ultralytics==8.4.153
-ultralytics-platform==0.1.65
-ultralytics-thop==2.2.1
-urllib3==2.8.0
-uvicorn==0.54.0
-websockets==17.1
diff --git a/recognization/requirements.txt b/recognization/requirements.txt
index 76ecafb..32e7a3a 100644
--- a/recognization/requirements.txt
+++ b/recognization/requirements.txt
@@ -1,18 +1,9 @@
 torch>=2.0.0
 torchvision>=0.15.0
-# Exact: tracking.py relies on BYTETracker internals; the trained checkpoint was written by 8.4.152
-ultralytics==8.4.153
-# ByteTrack linear assignment; pinned so ultralytics never pip-installs it at runtime
-lap>=0.5.12
-# uvicorn WebSocket support for /v1/stream
-websockets>=13
+ultralytics>=8.0.0
 opencv-python>=4.8.0
 numpy>=1.23.0,<2.0.0
 fastapi>=0.100.0
 uvicorn>=0.20.0
 python-multipart>=0.0.6
 requests>=2.28.0
-# macOS OCR backend (price tags); other platforms run without OCR until a
-# cross-platform backend is added - see docs/design-price-tags-and-compliance.md
-pyobjc-framework-Vision>=10; sys_platform == "darwin"
-pyobjc-framework-Quartz>=10; sys_platform == "darwin"
diff --git a/recognization/run-demo-server.sh b/recognization/run-demo-server.sh
deleted file mode 100755
index c87ed63..0000000
--- a/recognization/run-demo-server.sh
+++ /dev/null
@@ -1,71 +0,0 @@
-#!/usr/bin/env bash
-# Start the recognition server and write ignored local app endpoints.
-#
-#   ./run-demo-server.sh
-#   ./run-demo-server.sh stock-milk150
-set -euo pipefail
-
-ROOT="$(cd "$(dirname "$0")/../.." && pwd)"
-cd "$(dirname "$0")"
-
-STOCK="${1:-stock-lopia-clean}"
-PORT="${PORT:-8099}"
-TOKEN_FILE=".demo-token"
-
-if [ ! -d "$STOCK" ]; then
-  echo "no such store: $STOCK" >&2
-  exit 1
-fi
-
-IP="$(ipconfig getifaddr en0 2>/dev/null || ipconfig getifaddr en1 2>/dev/null || true)"
-if [ -z "$IP" ]; then
-  echo "this Mac has no LAN address; the phone cannot reach it" >&2
-  exit 1
-fi
-
-if [ ! -s "$TOKEN_FILE" ]; then
-  umask 077
-  openssl rand -hex 32 > "$TOKEN_FILE"
-fi
-TOKEN="$(tr -d '[:space:]' < "$TOKEN_FILE")"
-
-umask 077
-cat > "$ROOT/iosApp/Configuration/Config.local.xcconfig" <<EOF
-APP_RECOGNITION_BASE_URL = http:/\$()/$IP:$PORT
-APP_RECOGNITION_API_TOKEN = $TOKEN
-EOF
-
-mkdir -p config
-cat > config/local.env <<EOF
-DORAYAKI_API_TOKEN=$TOKEN
-EOF
-
-PROPERTIES="$ROOT/androidApp/local.properties"
-touch "$PROPERTIES"
-TMP="$(mktemp)"
-awk -v url="app.recognitionBaseUrl=http://$IP:$PORT" -v token="app.recognitionApiToken=$TOKEN" '
-  BEGIN { url_found = 0; token_found = 0 }
-  $0 ~ /^app\.recognitionBaseUrl=/ { print url; url_found = 1; next }
-  $0 ~ /^app\.recognitionApiToken=/ { print token; token_found = 1; next }
-  { print }
-  END {
-    if (!url_found) print url
-    if (!token_found) print token
-  }
-' "$PROPERTIES" > "$TMP"
-mv "$TMP" "$PROPERTIES"
-
-cat <<EOF
-Dorayaki demo server
-  store      $STOCK
-  listening  http://0.0.0.0:$PORT
-  phone URL  http://$IP:$PORT
-
-Local app configuration was updated. Rebuild both apps.
-The API token is in $TOKEN_FILE and is not printed.
-EOF
-
-exec env YOLO_OFFLINE=1 DORAYAKI_STOCK_DIR="$STOCK" DORAYAKI_API_TOKEN="$TOKEN" \
-  .venv/bin/python -u code/app.py \
-  --port "$PORT" --host 0.0.0.0 --identity local \
-  --live-conf 0.15 --classify-workers 8
diff --git a/recognization/run_app.bat b/recognization/run_app.bat
index 160be62..c072e9a 100644
--- a/recognization/run_app.bat
+++ b/recognization/run_app.bat
@@ -3,7 +3,7 @@ echo ==========================================================
 echo       DORAYAKI - SMART SHELF RECOGNITION AI APP
 echo ==========================================================
 echo [1/2] Installing / Checking dependencies...
-python -m pip install -r requirements-dev.txt
-echo [2/2] Starting Dorayaki recognition server on port 8080...
-python code/app.py --port 8080 %*
+python -m pip install -r requirements.txt
+echo [2/2] Starting Dorayaki Server on port 8080...
+python code/app.py --port 8080
 pause
diff --git a/recognization/run_app.sh b/recognization/run_app.sh
old mode 100755
new mode 100644
index bf503df..bc15075
--- a/recognization/run_app.sh
+++ b/recognization/run_app.sh
@@ -3,6 +3,6 @@ echo '=========================================================='
 echo '      DORAYAKI - SMART SHELF RECOGNITION AI APP'
 echo '=========================================================='
 echo '[1/2] Installing / Checking dependencies...'
-pip install -r requirements-dev.txt
-echo '[2/2] Starting Dorayaki recognition server on port 8080...'
-python3 code/app.py --port 8080 "$@"
+pip install -r requirements.txt
+echo '[2/2] Starting Dorayaki Server on port 8080...'
+python3 code/app.py --port 8080
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--- a/recognization/stock-all/stock.csv
+++ /dev/null
@@ -1,61 +0,0 @@
-SKU_ID,product_name,receipt_date,expiration_date,number_in,number_out,number_on,image_link,price
-sku_001,明治 おいしい牛乳,2026-09-12,2026-09-25,120,47,73,'/Users/nguyenhongvan/app/recognization/stock/sku_001.jpeg',258
-sku_002,野菜生活100 グリーンサラダ,2026-09-11,2027-03-11,130,45,85,'/Users/nguyenhongvan/app/recognization/stock/sku_002.jpeg,179
-sku_003,Tropicana エッセンシャルズ マルチビタミン,2026-09-12,2027-03-12,140,52,88,'/Users/nguyenhongvan/app/recognization/stock/sku_003.jpg',168
-sku_004,伊藤園 1日分の野菜,2026-09-09,2027-03-09,200,96,104,'/Users/nguyenhongvan/app/recognization/stock/sku_004.jpeg',198
-sku_005,野菜生活100 マンゴーサラダ,2026-09-11,2027-03-11,140,58,82,'/Users/nguyenhongvan/app/recognization/stock/sku_005.jpeg',179
-sku_007,野菜生活100 オリジナル,2026-09-10,2027-03-10,180,82,98,'/Users/nguyenhongvan/app/recognization/stock/sku_007.jpeg',179
-sku_008,明治プロビオヨーグルト R-1 ドリンクタイプ,2026-09-14,2026-09-28,120,45,75,'/Users/nguyenhongvan/app/recognization/stock/sku_008.png',250
-sku_009,野菜生活100 ベリーサラダ,2026-09-11,2027-03-11,150,63,87,'/Users/nguyenhongvan/app/recognization/stock/sku_009.jpeg',179
-sku_010,明治プロビオヨーグルト R-1 ドリンクタイプ 低糖・低カロリー,2026-09-14,2026-09-28,100,39,61,'/Users/nguyenhongvan/app/recognization/stock/sku_010.jpeg',250
-sku_011,野菜生活100 アップルサラダ,2026-09-15,2027-03-15,150,65,85,'/Users/nguyenhongvan/app/recognization/stock/sku_011.jpeg',179
-sku_012,ハッピーターン,2026-09-15,2027-03-15,180,72,108,'/Users/nguyenhongvan/app/recognization/stock/sku_012.jpeg',198
-sku_018,手塩屋 8枚,2026-09-15,2027-03-15,150,65,85,'/Users/nguyenhongvan/app/recognization/stock/sku_018.jpeg',168
-sku_016,新潟仕込み こだわりのり塩味 24枚,2026-09-15,2027-03-15,160,58,102,'/Users/nguyenhongvan/app/recognization/stock/sku_016.jpeg',178
-sku_014,おばあちゃんのぽたぽた焼 20枚,2026-09-15,2027-03-15,170,82,88,'/Users/nguyenhongvan/app/recognization/stock/sku_014.jpeg',188
-sku_013,ソフトサラダ 20枚,2026-09-15,2027-03-15,140,55,85,'/Users/nguyenhongvan/app/recognization/stock/sku_013.jpeg',188
-sku_015,ばかうけ 青のりしょうゆ味,2026-09-15,2027-03-15,155,67,88,'/Users/nguyenhongvan/app/recognization/stock/sku_015.jpeg',168
-sku_020,亀田のまがりせんべい 16枚,2026-09-15,2027-03-15,145,61,84,'/Users/nguyenhongvan/app/recognization/stock/sku_020.jpeg',188
-sku_017,味しらべ,2026-09-15,2027-03-15,125,43,82,'/Users/nguyenhongvan/app/recognization/stock/sku_017.jpeg',188
-sku_019,歌舞伎揚,2026-09-15,2027-03-15,135,54,81,'/Users/nguyenhongvan/rapp/recognization/stock/sku_019.jpeg',218
-sku_021,岩塚の黒豆せんべい,2026-09-15,2027-03-15,120,46,74,'/Users/nguyenhongvan/app/recognization/stock/sku_021.jpeg',198
-sku_022,持ちのこ,2026-09-15,2027-03-15,120,46,74,'/Users/nguyenhongvan/app/recognization/stock/sku_022.jpg',210
-sku_l01,キューピー エッグケア 305g,2026-09-18,2026-12-20,36,12,24,'',349
-sku_l02,キューピー アマニ油マヨネーズ 200g,2026-09-18,2026-12-20,24,9,15,'',329
-sku_l03,キューピー からしマヨネーズ 200g,2026-09-18,2026-12-20,30,14,16,'',219
-sku_l04,やまや めんたいマヨネーズタイプ 200g,2026-09-18,2026-11-30,18,6,12,'',399
-sku_l05,キューピー ゼロ ノンコレステロール 310g,2026-09-20,2026-12-25,24,11,13,'',269
-sku_l06,キューピー フィッテ 210g,2026-09-20,2026-12-25,18,7,11,'',379
-sku_l07,丸和油脂 ラッキーマヨネーズ 200g,2026-09-20,2026-12-25,24,10,14,'',199
-sku_l08,スパイシーストーム ペリペリミックス 30g,2026-09-15,2027-03-15,12,4,8,'',699
-sku_l09,ピュアセレクト コクうま カロリーカット65% 360g,2026-09-22,2026-12-28,30,13,17,'',209
-sku_l10,ピュアセレクト マヨネーズ 400g,2026-09-22,2026-12-28,36,15,21,'',222
-sku_l11,キューピー ハーフマヨネーズ 400g,2026-09-19,2026-12-22,48,22,26,'',269
-sku_l12,ユーラス 野菜をおいしく食べる旨たれ 380g,2026-09-10,2027-02-10,24,8,16,'',299
-sku_m01,小岩井 低脂肪牛乳,2026-09-25,2026-10-20,24,8,16,'',186
-sku_m02,荻山酪農 カフェ・オ・レ,2026-09-25,2026-10-20,24,8,16,'',397
-sku_m03,丹那 3.6牛乳,2026-09-25,2026-10-20,24,8,16,'',214
-sku_m04,木次 ミルクコーヒー,2026-09-25,2026-10-20,24,8,16,'',195
-sku_m05,木次 パスチャライズ牛乳,2026-09-25,2026-10-20,24,8,16,'',240
-sku_m06,キスキ ブラウン・スイス牛乳,2026-09-25,2026-10-20,24,8,16,'',254
-sku_m07,丹那 飲んでやさしいヨーグルト,2026-09-25,2026-10-20,24,8,16,'',225
-sku_m08,農場3.7牛乳 特選,2026-09-25,2026-10-20,24,8,16,'',216
-sku_m09,MINOR FIGURES OAT,2026-09-25,2026-10-20,24,8,16,'',300
-sku_m10,北海道 低脂肪乳,2026-09-25,2026-10-20,24,8,16,'',214
-sku_m11,タカナシ 低温殺菌牛乳,2026-09-25,2026-10-20,24,8,16,'',396
-sku_m12,RUDE HEALTH Oat Organic,2026-09-25,2026-10-20,24,8,16,'',400
-sku_m13,小岩井 無脂肪牛乳,2026-09-25,2026-10-20,24,8,16,'',178
-sku_m14,きすき ノンホモ牛乳,2026-09-25,2026-10-20,24,8,16,'',198
-sku_k01,明治 おいしい牛乳,2026-09-25,2026-10-15,48,20,28,'',198
-sku_k02,ゆうパック 優良牛乳,2026-09-25,2026-10-15,48,20,28,'',130
-sku_k03,酪農家限定 低温殺菌牛乳,2026-09-25,2026-10-15,48,20,28,'',248
-sku_k04,北海道牛乳,2026-09-25,2026-10-15,48,20,28,'',178
-sku_k05,MEGMILK,2026-09-25,2026-10-15,48,20,28,'',158
-sku_k06,毎日牛乳,2026-09-25,2026-10-15,48,20,28,'',168
-sku_k07,タカナシ 低温殺菌牛乳,2026-09-25,2026-10-15,48,20,28,'',258
-sku_k08,タカナシ 殺菌低脂肪牛乳,2026-09-25,2026-10-15,48,20,28,'',198
-sku_k09,毎日骨太,2026-09-25,2026-10-15,48,20,28,'',148
-sku_k10,カルシウム入り低脂肪乳,2026-09-25,2026-10-15,48,20,28,'',145
-sku_k11,無脂肪牛乳,2026-09-25,2026-10-15,48,20,28,'',138
-sku_k12,低脂肪牛乳,2026-09-25,2026-10-15,48,20,28,'',120
-sku_k13,ミルク,2026-09-25,2026-10-15,48,20,28,'',260
diff --git a/recognization/stock-demo/identity_head.pt b/recognization/stock-demo/identity_head.pt
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diff --git a/recognization/stock-demo/stock.csv b/recognization/stock-demo/stock.csv
deleted file mode 100644
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--- a/recognization/stock-demo/stock.csv
+++ /dev/null
@@ -1,28 +0,0 @@
-SKU_ID,product_name,receipt_date,expiration_date,number_in,number_out,number_on,image_link,price
-sku_m01,小岩井 低脂肪牛乳,2026-09-25,2026-10-20,24,8,16,'',186
-sku_m02,荻山酪農 カフェ・オ・レ,2026-09-25,2026-10-20,24,8,16,'',397
-sku_m03,丹那 3.6牛乳,2026-09-25,2026-10-20,24,8,16,'',214
-sku_m04,木次 ミルクコーヒー,2026-09-25,2026-10-20,24,8,16,'',195
-sku_m05,木次 パスチャライズ牛乳,2026-09-25,2026-10-20,24,8,16,'',240
-sku_m06,キスキ ブラウン・スイス牛乳,2026-09-25,2026-10-20,24,8,16,'',254
-sku_m07,丹那 飲んでやさしいヨーグルト,2026-09-25,2026-10-20,24,8,16,'',225
-sku_m08,農場3.7牛乳 特選,2026-09-25,2026-10-20,24,8,16,'',216
-sku_m09,MINOR FIGURES OAT,2026-09-25,2026-10-20,24,8,16,'',300
-sku_m10,北海道 低脂肪乳,2026-09-25,2026-10-20,24,8,16,'',214
-sku_m11,タカナシ 低温殺菌牛乳,2026-09-25,2026-10-20,24,8,16,'',396
-sku_m12,RUDE HEALTH Oat Organic,2026-09-25,2026-10-20,24,8,16,'',400
-sku_m13,小岩井 無脂肪牛乳,2026-09-25,2026-10-20,24,8,16,'',178
-sku_m14,きすき ノンホモ牛乳,2026-09-25,2026-10-20,24,8,16,'',198
-sku_k01,明治 おいしい牛乳,2026-09-25,2026-10-15,48,20,28,'',198
-sku_k02,ゆうパック 優良牛乳,2026-09-25,2026-10-15,48,20,28,'',130
-sku_k03,酪農家限定 低温殺菌牛乳,2026-09-25,2026-10-15,48,20,28,'',248
-sku_k04,北海道牛乳,2026-09-25,2026-10-15,48,20,28,'',178
-sku_k05,MEGMILK,2026-09-25,2026-10-15,48,20,28,'',158
-sku_k06,毎日牛乳,2026-09-25,2026-10-15,48,20,28,'',168
-sku_k07,タカナシ 低温殺菌牛乳,2026-09-25,2026-10-15,48,20,28,'',258
-sku_k08,タカナシ 殺菌低脂肪牛乳,2026-09-25,2026-10-15,48,20,28,'',198
-sku_k09,毎日骨太,2026-09-25,2026-10-15,48,20,28,'',148
-sku_k10,カルシウム入り低脂肪乳,2026-09-25,2026-10-15,48,20,28,'',145
-sku_k11,無脂肪牛乳,2026-09-25,2026-10-15,48,20,28,'',138
-sku_k12,低脂肪牛乳,2026-09-25,2026-10-15,48,20,28,'',120
-sku_k13,ミルク,2026-09-25,2026-10-15,48,20,28,'',260
diff --git a/recognization/stock-demo/store.json b/recognization/stock-demo/store.json
deleted file mode 100644
index 4a257fa..0000000
--- a/recognization/stock-demo/store.json
+++ /dev/null
@@ -1,6 +0,0 @@
-{
-  "store_id": "demo",
-  "tax_ratios": [1.08, 1.1],
-  "min_price": 50,
-  "rail_hsv": {"hue_low": 14, "hue_high": 166, "sat_min": 70, "val_min": 70}
-}
diff --git a/recognization/stock-lopia-clean/README.md b/recognization/stock-lopia-clean/README.md
deleted file mode 100644
index 2a57a5a..0000000
--- a/recognization/stock-lopia-clean/README.md
+++ /dev/null
@@ -1,152 +0,0 @@
-# LOPIA clean catalog
-
-Official manufacturer pack shots, one front view per SKU. This replaces the
-views in `../stock-lopia/`, which were 71-119 px crops cut from the test shelf
-`add/image6.jpg` itself: one 18 px sliver, duplicate crops, a wrong product on
-`sku_l09`, and four SKUs with no view at all. Enrolling from the test shelf is
-why every dark package collapsed onto one SKU and why the identity head was
-trained on its own exam.
-
-Eight of the twelve rows are enrolled; `sku_l04`, `sku_l07`, `sku_l08` and
-`sku_l12` stay text-only (see *Known gaps*). Every enrolled view is at least
-1000 px tall.
-
-Rebuild from the staged packshots with:
-
-    python code/rebuild_catalog.py --source data/packshots/source \
-        --output stock-lopia-clean --csv stock-lopia/stock.csv \
-        --replace --min-views 1
-
-`--min-views 1` because official pages publish a single front view per JAN. The
-default floor of 2 exists to catch accidental single-view enrollment from shelf
-crops, where several views are available.
-
-To re-run the audit without touching the catalog, point `--output` at a
-scratch path inside the repo and keep the JSON:
-
-    python code/rebuild_catalog.py --source data/packshots/source \
-        --output data/packshots/audit-check --csv stock-lopia/stock.csv \
-        --min-views 1 --json data/packshots/audit-check.json
-
-## Provenance
-
-Product names are the catalog's, verbatim from `stock.csv`; sizes are measured.
-
-| SKU | Catalog name | View | Recorded source |
-|---|---|---|---|
-| sku_l01 | キューピー エッグケア 305g | 455x1000 | not recorded |
-| sku_l02 | キューピー アマニ油マヨネーズ 200g | 468x1000 | not recorded |
-| sku_l03 | キューピー からしマヨネーズ 200g | 450x1000 | not recorded |
-| sku_l05 | キューピー ゼロ ノンコレステロール 310g | 461x1000 | not recorded |
-| sku_l06 | キューピー フィッテ 210g | 654x1500 | [amazon.co.jp](https://www.amazon.co.jp/dp/B08V2676LK) |
-| sku_l09 | ピュアセレクト コクうま カロリーカット65% 360g | 1000x1000 | not recorded |
-| sku_l10 | ピュアセレクト マヨネーズ 400g | 1000x1000 | [rakuten.co.jp](https://item.rakuten.co.jp/matsukiyo/4901001114030/) (JAN 4901001114030) |
-| sku_l11 | キューピー ハーフマヨネーズ 400g | 482x1000 | not recorded |
-
-`data/packshots/source/ATTRIBUTION.json` records a URL for `sku_l06` and
-`sku_l10` only. Its `_provenance_gap` note names the other six - l01, l02, l03,
-l05, l09, l11 - as enrolled before any provenance was tracked, and the audit
-reports them under `unattributed_skus` on every run. What *is* verified about
-them, per that note: each is independent of the evaluation photo (template
-match against `add/image6.jpg` peaks at 0.166, against 0.95 for a crop of it),
-each is 1000 px or taller, and each one's own OCR matches its catalog row (see
-*Name/OCR gate*). That is a traceability gap, not a correctness one.
-
-`sku_l06` is the current package (`内臓脂肪を減らす`), which replaced the
-superseded `内臓脂肪ケア` view on 2026-09-29. The catalog name was shortened to
-match; the shelf tags were not part of that change.
-
-## Name/OCR gate
-
-A packshot's OCR has to explain its own catalog row's name, or it is refused.
-Measured over the shipped packshots - every image read in all four rotations,
-every read scored against all 12 catalog names:
-
-| Class | n | Score |
-|---|---|---|
-| Correct packshot vs its own row | 8 | 0.9167 - 1.0000 |
-| Wrong product vs any row | 1 image x 12 names | 0.0476 - 0.5833 |
-
-`MIN_NAME_OCR = 0.75` is the midpoint of those two bands: 0.167 of headroom
-above the worst correct packshot (l11, whose read omits `マヨネーズ`) and 0.167
-below the wrong-product ceiling. The wrong-product image is
-`references/rejected/kewpie-mayonnaise-450g.jpg`; its best score anywhere is
-0.5833, tied against `キューピー からしマヨネーズ 200g` and
-`キューピー ハーフマヨネーズ 400g`, and it bottoms out at 0.0476 against
-`ピュアセレクト コクうま カロリーカット65% 360g`.
-
-Reproduce the band with (run from the recognition root; unindented so it runs
-verbatim):
-
-```python
-import csv, os, sys, cv2
-sys.path.insert(0, 'code')
-from ocr import build_ocr_engine
-from rebuild_catalog import best_orientation_text, name_ocr_similarity
-
-clean = 'stock-lopia-clean'
-names = {r['SKU_ID']: r['product_name'] for r in csv.DictReader(open(clean + '/stock.csv'))}
-engine = build_ocr_engine(None)
-for n in sorted(os.listdir(clean)):
-    if n.endswith('.jpg'):
-        sku = n[:-4]
-        read = best_orientation_text(engine, cv2.imread(os.path.join(clean, n)), names[sku])
-        print(sku, round(name_ocr_similarity(names[sku], read), 4))
-
-rej = 'references/rejected/kewpie-mayonnaise-450g.jpg'
-read = best_orientation_text(engine, cv2.imread(os.path.join(clean, rej)), names['sku_l01'])
-print('rejected max', max((round(name_ocr_similarity(v, read), 4), k) for k, v in names.items()))
-```
-
-The gate cannot separate siblings, and does not try: the `sku_l09` packshot
-scores 0.8462 against the `sku_l10` name, because both are Ajinomoto
-ピュアセレクト mayonnaise. That case is what `text_discriminated_siblings` flags
-(l09 ~ l10, cosine 0.9817) - it is a cross-SKU geometry problem, not a gap a
-threshold closes.
-
-## Pyramid floor
-
-Every enrolled view must be at least 800 px tall, or it is refused. This is a
-hard gate, not a report. It exists because of `sku_l09`: the view enrolled at
-305 px could not be stored as a 200/400/800 px pyramid, so its local lane
-upscaled 305->400 px and scored 85-129 inliers against 10-40 for every other
-reference, winning facings it should not. It was re-sourced at 1000x1000 on
-2026-09-29. Reporting the defect instead of refusing it is what let it ship.
-
-`data/packshots/source/sku_l07.jpg` is 600x600 and is refused on this rule
-before OCR even runs.
-
-## Known gaps
-
-Four rows in `../stock-lopia/stock.csv` have no enrolled image, so no image
-lane can match them. They stay in `stock.csv` as text-only rows: the text lane
-builds a signature from the printed name, which is how it can name or veto a
-near-unknown the matcher cannot see. They are the current image-coverage
-ceiling, not a recognition failure.
-
-| SKU | Catalog name | Why there is no view |
-|---|---|---|
-| sku_l04 | やまや めんたいマヨネーズタイプ 200g | A packshot is staged and refused at name/OCR 0.00: the image reads only `明太手屋さんが つくつ た コ`, brand text that runs vertically. A correctly oriented or higher-resolution shot is what would clear it. |
-| sku_l07 | 丸和油脂 ラッキーマヨネーズ 200g | A packshot is staged and refused twice: it is 600x600, under the pyramid floor, and scores name/OCR 0.5714. Every product bigram reads (`ラッキー マヨネーズ`); the six it misses are the maker prefix `丸和`/`和油`/`油脂` and the size `20`/`0G`/`00`, which the package does not print. |
-| sku_l08 | スパイシーストーム ペリペリミックス 30g | No official 30 g Peri-Peri Mix found |
-| sku_l12 | ユーラス 野菜をおいしく食べる旨たれ 380g | No pack shot sourced |
-
-`sku_l10` left this table on 2026-09-29. Its catalog name said 450 g, which
-Ajinomoto does not make; the shelf tag in `add/image6.jpg` and the
-manufacturer's lineup both say 400 g, so `stock.csv` was corrected and a
-1000x1000 packshot (JAN 4901001114030) enrolled. See
-`data/packshots/source/ATTRIBUTION.json`.
-
-Keep the text-only rows when rebuilding: the CSV carry-over must preserve every
-source row, not just the SKUs with a kept view. Dropping these four costs the
-text lane the signatures that cover them.
-
-## Rejected
-
-`references/rejected/kewpie-mayonnaise-450g.jpg` is Kewpie Mayonnaise 450g (JAN
-4901577042072). It was filed as `sku_l09` and then as `sku_l10` because the
-catalog's names pointed there, but it is neither: l09 is Ajinomoto Pure Select
-コクうま カロリーカット 65% 360g and l10 is Pure Select マヨネーズ 400g. Both now
-hold their own correct views. The rejected image is kept as provenance for that
-mix-up and is never enrolled; the name/OCR gate scores it at most 0.5833
-against any catalog name, below the 0.75 threshold.
diff --git a/recognization/stock-lopia-clean/references/rejected/kewpie-mayonnaise-450g.jpg b/recognization/stock-lopia-clean/references/rejected/kewpie-mayonnaise-450g.jpg
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diff --git a/recognization/stock-lopia-clean/stock.csv b/recognization/stock-lopia-clean/stock.csv
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+++ /dev/null
@@ -1,13 +0,0 @@
-SKU_ID,product_name,receipt_date,expiration_date,number_in,number_out,number_on,image_link,price
-sku_l01,キューピー エッグケア 305g,2026-09-18,2026-12-20,36,12,24,'',349
-sku_l02,キューピー アマニ油マヨネーズ 200g,2026-09-18,2026-12-20,24,9,15,'',329
-sku_l03,キューピー からしマヨネーズ 200g,2026-09-18,2026-12-20,30,14,16,'',219
-sku_l04,やまや めんたいマヨネーズタイプ 200g,2026-09-18,2026-11-30,18,6,12,'',399
-sku_l05,キューピー ゼロ ノンコレステロール 310g,2026-09-20,2026-12-25,24,11,13,'',269
-sku_l06,キューピー フィッテ 210g,2026-09-20,2026-12-25,18,7,11,'',379
-sku_l07,丸和油脂 ラッキーマヨネーズ 200g,2026-09-20,2026-12-25,24,10,14,'',199
-sku_l08,スパイシーストーム ペリペリミックス 30g,2026-09-15,2027-03-15,12,4,8,'',699
-sku_l09,ピュアセレクト コクうま カロリーカット65% 360g,2026-09-22,2026-12-28,30,13,17,'',209
-sku_l10,ピュアセレクト マヨネーズ 400g,2026-09-22,2026-12-28,36,15,21,'',222
-sku_l11,キューピー ハーフマヨネーズ 400g,2026-09-19,2026-12-22,48,22,26,'',269
-sku_l12,ユーラス 野菜をおいしく食べる旨たれ 380g,2026-09-10,2027-02-10,24,8,16,'',299
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diff --git a/recognization/stock-lopia/stock.csv b/recognization/stock-lopia/stock.csv
deleted file mode 100644
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--- a/recognization/stock-lopia/stock.csv
+++ /dev/null
@@ -1,13 +0,0 @@
-SKU_ID,product_name,receipt_date,expiration_date,number_in,number_out,number_on,image_link,price
-sku_l01,キューピー エッグケア 305g,2026-09-18,2026-12-20,36,12,24,'',349
-sku_l02,キューピー アマニ油マヨネーズ 200g,2026-09-18,2026-12-20,24,9,15,'',329
-sku_l03,キューピー からしマヨネーズ 200g,2026-09-18,2026-12-20,30,14,16,'',219
-sku_l04,やまや めんたいマヨネーズタイプ 200g,2026-09-18,2026-11-30,18,6,12,'',399
-sku_l05,キューピー ゼロ ノンコレステロール 310g,2026-09-20,2026-12-25,24,11,13,'',269
-sku_l06,キューピー フィッテ 210g,2026-09-20,2026-12-25,18,7,11,'',379
-sku_l07,丸和油脂 ラッキーマヨネーズ 200g,2026-09-20,2026-12-25,24,10,14,'',199
-sku_l08,スパイシーストーム ペリペリミックス 30g,2026-09-15,2027-03-15,12,4,8,'',699
-sku_l09,ピュアセレクト コクうま カロリーカット65% 360g,2026-09-22,2026-12-28,30,13,17,'',209
-sku_l10,ピュアセレクト マヨネーズ 400g,2026-09-22,2026-12-28,36,15,21,'',222
-sku_l11,キューピー ハーフマヨネーズ 400g,2026-09-19,2026-12-22,48,22,26,'',269
-sku_l12,ユーラス 野菜をおいしく食べる旨たれ 380g,2026-09-10,2027-02-10,24,8,16,'',299
diff --git a/recognization/stock-lopia/store.json b/recognization/stock-lopia/store.json
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--- a/recognization/stock-lopia/store.json
+++ /dev/null
@@ -1,6 +0,0 @@
-{
-  "store_id": "lopia",
-  "tax_ratios": [1.08, 1.1],
-  "min_price": 50,
-  "rail_hsv": {"hue_low": 14, "hue_high": 166, "sat_min": 70, "val_min": 70}
-}
diff --git a/recognization/stock-mediya/identity_head.pt b/recognization/stock-mediya/identity_head.pt
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diff --git a/recognization/stock-mediya/stock.csv b/recognization/stock-mediya/stock.csv
deleted file mode 100644
index 994662d..0000000
--- a/recognization/stock-mediya/stock.csv
+++ /dev/null
@@ -1,15 +0,0 @@
-SKU_ID,product_name,receipt_date,expiration_date,number_in,number_out,number_on,image_link,price
-sku_m01,小岩井 低脂肪牛乳,2026-09-25,2026-10-20,24,8,16,'',186
-sku_m02,荻山酪農 カフェ・オ・レ,2026-09-25,2026-10-20,24,8,16,'',397
-sku_m03,丹那 3.6牛乳,2026-09-25,2026-10-20,24,8,16,'',214
-sku_m04,木次 ミルクコーヒー,2026-09-25,2026-10-20,24,8,16,'',195
-sku_m05,木次 パスチャライズ牛乳,2026-09-25,2026-10-20,24,8,16,'',240
-sku_m06,キスキ ブラウン・スイス牛乳,2026-09-25,2026-10-20,24,8,16,'',254
-sku_m07,丹那 飲んでやさしいヨーグルト,2026-09-25,2026-10-20,24,8,16,'',225
-sku_m08,農場3.7牛乳 特選,2026-09-25,2026-10-20,24,8,16,'',216
-sku_m09,MINOR FIGURES OAT,2026-09-25,2026-10-20,24,8,16,'',300
-sku_m10,北海道 低脂肪乳,2026-09-25,2026-10-20,24,8,16,'',214
-sku_m11,タカナシ 低温殺菌牛乳,2026-09-25,2026-10-20,24,8,16,'',396
-sku_m12,RUDE HEALTH Oat Organic,2026-09-25,2026-10-20,24,8,16,'',400
-sku_m13,小岩井 無脂肪牛乳,2026-09-25,2026-10-20,24,8,16,'',178
-sku_m14,きすき ノンホモ牛乳,2026-09-25,2026-10-20,24,8,16,'',198
diff --git a/recognization/stock-mediya/store.json b/recognization/stock-mediya/store.json
deleted file mode 100644
index fcd8942..0000000
--- a/recognization/stock-mediya/store.json
+++ /dev/null
@@ -1,6 +0,0 @@
-{
-  "store_id": "mediya",
-  "tax_ratios": [1.08, 1.1],
-  "min_price": 50,
-  "rail_hsv": {"hue_low": 14, "hue_high": 166, "sat_min": 70, "val_min": 70}
-}
diff --git a/recognization/stock-milk150/identity_head.pt b/recognization/stock-milk150/identity_head.pt
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deleted file mode 100644
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diff --git a/recognization/stock-milk150/stock.csv b/recognization/stock-milk150/stock.csv
deleted file mode 100644
index b447869..0000000
--- a/recognization/stock-milk150/stock.csv
+++ /dev/null
@@ -1,14 +0,0 @@
-SKU_ID,product_name,receipt_date,expiration_date,number_in,number_out,number_on,image_link,price
-sku_k01,明治 おいしい牛乳,2026-09-25,2026-10-15,48,20,28,'',198
-sku_k02,ゆうパック 優良牛乳,2026-09-25,2026-10-15,48,20,28,'',130
-sku_k03,酪農家限定 低温殺菌牛乳,2026-09-25,2026-10-15,48,20,28,'',248
-sku_k04,北海道牛乳,2026-09-25,2026-10-15,48,20,28,'',178
-sku_k05,MEGMILK,2026-09-25,2026-10-15,48,20,28,'',158
-sku_k06,毎日牛乳,2026-09-25,2026-10-15,48,20,28,'',168
-sku_k07,タカナシ 低温殺菌牛乳,2026-09-25,2026-10-15,48,20,28,'',258
-sku_k08,タカナシ 殺菌低脂肪牛乳,2026-09-25,2026-10-15,48,20,28,'',198
-sku_k09,毎日骨太,2026-09-25,2026-10-15,48,20,28,'',148
-sku_k10,カルシウム入り低脂肪乳,2026-09-25,2026-10-15,48,20,28,'',145
-sku_k11,無脂肪牛乳,2026-09-25,2026-10-15,48,20,28,'',138
-sku_k12,低脂肪牛乳,2026-09-25,2026-10-15,48,20,28,'',120
-sku_k13,ミルク,2026-09-25,2026-10-15,48,20,28,'',260
diff --git a/recognization/stock-milk150/store.json b/recognization/stock-milk150/store.json
deleted file mode 100644
index 27c5789..0000000
--- a/recognization/stock-milk150/store.json
+++ /dev/null
@@ -1,6 +0,0 @@
-{
-  "store_id": "milk150",
-  "tax_ratios": [1.08, 1.1],
-  "min_price": 50,
-  "rail_hsv": {"hue_low": 14, "hue_high": 166, "sat_min": 70, "val_min": 70}
-}
diff --git a/recognization/stock/store.json b/recognization/stock/store.json
deleted file mode 100644
index bf2994c..0000000
--- a/recognization/stock/store.json
+++ /dev/null
@@ -1,6 +0,0 @@
-{
-  "store_id": "stock",
-  "tax_ratios": [1.08, 1.1],
-  "min_price": 50,
-  "rail_hsv": {"hue_low": 14, "hue_high": 166, "sat_min": 70, "val_min": 70}
-}
diff --git a/recognization/test_empty_slots.jpg b/recognization/test_empty_slots.jpg
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index 0000000..207d8d7
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diff --git a/recognization/uv.lock b/recognization/uv.lock
deleted file mode 100644
index 1e6df0f..0000000
--- a/recognization/uv.lock
+++ /dev/null
@@ -1,1737 +0,0 @@
-version = 1
-revision = 3
-requires-python = ">=3.10, <3.13"
-resolution-markers = [
-    "python_full_version >= '3.12' and sys_platform == 'win32'",
-    "python_full_version == '3.11.*' and sys_platform == 'win32'",
-    "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'",
-    "(python_full_version >= '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')",
-    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
-    "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')",
-    "python_full_version >= '3.12' and sys_platform == 'darwin'",
-    "python_full_version == '3.11.*' and sys_platform == 'darwin'",
-    "python_full_version < '3.11' and sys_platform == 'win32'",
-    "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux'",
-    "(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')",
-    "python_full_version < '3.11' and sys_platform == 'darwin'",
-]
-
-[[package]]
-name = "annotated-doc"
-version = "0.0.5"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/5a/8e/38aa427ed5402449e226975b649c5dc73ccadfefeb95e6aecb8f8ea4b6b6/annotated_doc-0.0.5.tar.gz", hash = "sha256:c7e58ce09192557605d8bbd92836d7e1d520ac9580096042c0bfd197efacf1bb", size = 10758, upload-time = "2026-07-28T13:50:58.129Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/3e/30/e900b21425a860e195f32e37657aa1f7c7f2b1bfb26f03ca209b90933c06/annotated_doc-0.0.5-py3-none-any.whl", hash = "sha256:117bac03a25ede5df5440e855b32d556049ca169ead221505badf432fed4b101", size = 5302, upload-time = "2026-07-28T13:50:57.239Z" },
-]
-
-[[package]]
-name = "annotated-types"
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