product-recognition-demo/recognization/data
kiyo9w 11826cc43c feat(eval): report the external benchmark per SKU
A set total hides which products the lane can and cannot name. Every SKU in
these sets has images from two independent source domains, so a SKU that scores
the same on both is not one lucky photograph.

The breakdown is what makes the two interesting results visible: l01, l02, l03,
l07 and l09 are 2/2 correct on both their domains, l07 with no enrolled image
at all, while l11 abstains on both and l04 abstains on all four of its images.
2026-09-29 03:37:04 +09:00
..
evaluations/2026-09-29-session docs(decision): refresh every number the corrected labels moved 2026-09-29 03:18:30 +09:00
external-eval feat(eval): report the external benchmark per SKU 2026-09-29 03:37:04 +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