forked from vnguyen/product-recognition-demo

History

kiyo9w 4bdad66bbe docs(decision): record the far-unknown calibration and the gate status The far-unknown claim in the verdict said 0.2-0.7% on 602 crops. That was the audit's variant matcher on the add/ photos; the shipped lane at the shipped lines was 3.61% on the Wikimedia sets. Both are now stated, with the sweep that chose 4.0/4 and the live-endpoint result: 44 named, 42 correct, 95.5% precision against 53/48/90.6% before. Gate status is recorded per gate rather than as a blanket claim: G2 and G3's far half pass offline, G8 passes, and G1/G4/G5/G6/G7 are not yet measured on held-out data because Tier 2 does not exist. The Wikimedia manifests and results are committed; the 59 MB of images are not, because collect_shelf_images.py fetches them from the source URLs the manifests record.		2026-09-29 04:11:00 +09:00
..
evaluations/2026-09-29-session	docs(decision): record the far-unknown calibration and the gate status	2026-09-29 04:11:00 +09:00
external-eval	test(tier3): run the production pipeline over both reusable clips	2026-09-29 03:39:33 +09:00
external-shelves	docs(decision): record the far-unknown calibration and the gate status	2026-09-29 04:11:00 +09:00
packshots	feat(enroll): report views with no recorded source	2026-09-29 03:35:41 +09:00
README.md	Enroll from a repo data dir; read packshots in every orientation	2026-09-29 02:33:47 +09:00

README.md

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