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The deep review's first recommendation was real data, because every accuracy number so far comes from one or two photographs - the identity benchmark even takes its gallery and its queries from the same photo. This is the mechanism for replacing that. `--record-frames DIR` writes each processed frame, sampled every Nth, with a JSON line describing what the server saw in it: the boxes, the identity channels and the tag reading. The lines are the labelling input - `labelling_candidates()` returns the facings the tag channel named with a corroborated price, which are seeds for the confirmation screen rather than guesses. Nothing here is a training set on its own. Sampling keeps each frame's own number in the index, so a sampled recording says so instead of hiding it, and `/v1/health` reports how much has been written. Measured on a 163-frame walk: 32 frames and their lines. `docs/DATA.md` is the runbook: what to capture (a slow walk per rail, a close pass for the barcode, and the same shelf on a second visit), how to turn it into labels, and how to freeze a benchmark with a cross-visit split. 167 tests pass. Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>
92 lines
3.4 KiB
Markdown
92 lines
3.4 KiB
Markdown
# Capturing real data
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Everything measured in this project so far comes from one or two photographs. That
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is its largest weakness: the identity benchmark takes its gallery and its queries
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from the same photo, so every accuracy number is an in-distribution number.
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This is the procedure for replacing that with data from a real store, recorded on
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the device that will do the scanning.
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## 1. Record a walk
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Start the server with recording on. Frames are written every Nth processed frame,
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and each one is written with a line describing what the server saw in it.
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```bash
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cd recognization
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YOLO_OFFLINE=1 .venv/bin/python code/app.py \
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--port 8090 --host 0.0.0.0 \
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--classify-workers 8 \
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--record-frames ~/dorayaki-data/2026-09-25-storeA \
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--record-every 3
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```
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Then run the app and scan. Each scan session becomes its own directory:
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```
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~/dorayaki-data/2026-09-25-storeA/<session>/
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000000.jpg the frame, exactly as the client sent it
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000001.jpg
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index.jsonl one line per recorded frame:
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{"frame": 123, "path": "000001.jpg", "at": 1699...,
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"summary": {...},
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"tracks": [{"box": [...], "identity": {...},
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"tag_identity": {...}, "tag": {...}}]}
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```
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Frames are sampled, not continuous, and the index keeps each frame's own number so
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the sampling is visible rather than silent. `/v1/health` reports how many frames
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have been written.
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## 2. What to capture, and why
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Two visits to the same shelf are worth more than one long visit, because the
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benchmark needs a **cross-visit split**: the gallery comes from visit 1 and the
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test set from visit 2. That is the difference between "it works on this photo" and
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"it works on this shelf".
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Per shelf, capture:
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- a **slow walk along each rail** at working distance (about 1 m) — the live loop's
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real input;
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- a **close pass** (about 30 cm) over a few tags — the only distance where the
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barcode is decodable (measured: 0.63 px per module at 1 m needs ~3× more pixels);
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- the same shelf again **on a second visit**, ideally with something changed: a gap,
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a moved facing, a new product.
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## 3. Turn a recording into labels
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The tag channel seeds the labels. A facing whose tag named a product, with a price
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the tag's own arithmetic corroborates, is a candidate label rather than a guess:
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```bash
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python -c "
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import json, sys
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sys.path.insert(0, 'code')
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from record import labelling_candidates
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seeds = labelling_candidates('~/dorayaki-data/2026-09-25-storeA/<session>/index.jsonl')
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print(len(seeds), 'candidate labels')
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print(json.dumps(seeds[:3], ensure_ascii=False, indent=1))
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"
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```
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A human then confirms or corrects them in the app's confirmation screen — the same
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screen that closes the flywheel. Nothing here is a training set on its own; it is
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the raw material for one.
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## 4. Freeze a benchmark
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Write the confirmed labels into a manifest that the evaluation reads, and keep it in
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the repository so results are reproducible:
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```
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benchmarks/<store>-<date>/
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visit1/<sku_id>/<view>.jpg the gallery: 2-3 views per SKU
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visit2/*.jpg the test frames
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labels.json {"visit2/0007.jpg": [{"box": [...], "sku_id": "..."}]}
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```
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Then the identity benchmark (see `code/bench_identity.py`) can be run with a
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cross-visit split, and every number it reports is a number worth acting on. Until
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that exists, treat the current figures as what they are: measurements on a
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photograph.
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