forked from vnguyen/product-recognition-demo

History

kiyo9w 01d9a5e614 docs(decision): re-measure the stream and live rows after the calibration The stream row was recorded before the accept lines moved. Three fresh 20 s pan replays through the shipped lane: 70-74% coverage at 77-85% precision, 0 wrong labels in all three (0-1 before), 8-14 unknown false accepts - the near-unknown case, which the size gate and catalog completeness own rather than the accept line. Live resolution on the shipped evaluator: 56% end-to-end at 97% precision with 1 false accept, so the calibration is not a full-resolution artifact. The recordings are committed under stream-gated/ so the row is reproducible.		2026-09-29 04:22:46 +09:00
..
evaluations/2026-09-29-session	docs(decision): re-measure the stream and live rows after the calibration	2026-09-29 04:22:46 +09:00
external-eval	test(tier3): run the production pipeline over both reusable clips	2026-09-29 03:39:33 +09:00
external-shelves	chore(eval): reconcile the distractor sets and refresh the sweep numbers	2026-09-29 04:17:54 +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