product-recognition-demo/recognization/data
kiyo9w d13ce94123 test(server): final smoke with every catalog correction live
/v1/recognize: 111 detections, 53 named in 2.38 s, no view-suffixed ids, no
missing product info. l06 now names all 4 of its facings - it was 1/4 at live
resolution before the current package was enrolled, which is the F-12 fix
visible in the product.

/v1/stream, 20 s pan: 82 tracks labelled, 0 label changes, 0 unexplained. The
label-memory fixes hold under a pan.
2026-09-29 03:41:05 +09:00
..
evaluations/2026-09-29-session test(server): final smoke with every catalog correction live 2026-09-29 03:41:05 +09:00
external-eval test(tier3): run the production pipeline over both reusable clips 2026-09-29 03:39:33 +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

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