The row named a product that is not on the shelf. The tag under facings #46-48
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 already carried - and the package reads お子さまOK 甘めテイスト, the
opposite of 中辛. Category, heat level and brand were all wrong.
This one is worse than a wrong size: 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.
The 2023 green-label predecessor (380ml, JAN 4571233473887, checksum verified)
is recorded in the manifest as predecessor-only evidence; no current JAN is
claimed, because the current white package's rear label has not been
photographed. One in-store rear-label photo would close that.
Both CSVs called sku_l11 キューピー ハーフマヨネーズ 450g. The shelf tag under
the l11 row in add/image6.jpg reads キユーピー ハーフマヨネーズ〈400g〉, and the
enrolled packshot's own OCR reads 400g, so the catalog shipped a size the
product does not have - the same defect class as sku_l10.
Measured on the corrected image6 labels, local lane: full 86% precision (was
84%), live 95% (was 91%), false accepts 8 and 2. 303 tests pass.
Ajinomoto ピュアセレクト マヨネーズ has no 450g size: the official lineup is
250g/400g/600g/1kg, the shelf tag in add/image6.jpg reads 400g, and DECISION.md
already recorded the mismatch. Both CSVs carried 450g, so every l10 answer
shipped a wrong size.
The packshot is the exact 400g front (JAN 4901001114030, 1000x1000) with its
source URL and the 450g rejection recorded in
data/packshots/research/sku_l10/manifest.json.
Enrolling it moves the catalog ceiling from 69% to 87% of in-catalog facings
answerable by image (l10 was 11 unenrolled facings). Measured on the corrected
image6 labels: local lane 92% identity at 84% precision full, 85% at 91% live.
Also points external-eval defaults at the repository-local data path; the
licensed Wikimedia corpus remains uncommitted pending manifest review.
Replaces the DINO gallery as the recognition path with the measured design
from docs/research/2026-09-28-recognition-architecture/DECISION.md: RootSIFT
over a per-SKU reference pyramid, similarity-constrained RANSAC and a hue
check, fused with an OCR text lane that vetoes a match whose package text
names a different catalog row and answers on its own when geometry finds
nothing.
- identity_lanes.py: LocalLane, TextLane, DecisionPolicy. The text lane
builds each row's signature from the catalog name plus its packshot OCR, so
a row with no enrolled image is still answerable.
- engine.py: every backend groups scores by canonical SKU, so no
view-suffixed id reaches the wire and a name and price always resolve.
decide_crop fuses the lanes; score_crop_lanes feeds the live path. Both
lanes are built in warm_up (non-fatally) so the first frame is not the slow
one, and the text lane shares the engine's OCR lock.
- live.py: decides per crop on the lane's own rank order. The lane's order
comes from its rank key and its value is only the gate, so re-sorting by
value named a different SKU than the lane chose; averaging in an empty
observation also diluted a good confidence below the accept line.
- identity.py: a `local` score scale whose gate is one absolute line, because
the value is already a margin over the runner-up.
- app.py, run-demo-server.sh: `--identity local` is the default; the demo
server no longer starts on the leaked `stock-lopia` catalog.
- eval_live.py: the replay vocabulary accepts `local`, so a recording from
the shipped server can be scored.
- README.md: documents the three backends and what each is for.
Measured on the corrected image6 labels at live resolution: 36/61 in-catalog
facings named at 95% precision, 0 of 48 unknowns accepted, 11 labels sourced
from the text lane alone. Through the real server: /v1/recognize 90%
precision, /v1/stream 100% precision, no view-suffixed ids on either.
