History

kiyo9w 962cdaab5f test(tier3): run the production pipeline over both reusable clips The two clips were staged but never exercised. One frame per second through the production detector and the shipped local lane: - The 1080p supermarket pan: 227 crops, 0 accepted. Correct open-set behaviour - the frame is German pickle jars, not catalog products. - The 360p mayonnaise bay: 1623 crops, 24 accepted (1.5%), across seven SKUs. The clip visibly contains Kewpie products, so those are not automatically false accepts, but at 640x360 the labels are too soft for per-SKU ground truth. Recorded as an accept-rate probe with false_accepts and accuracy left null rather than estimated. Tier 3 still needs an operator-recorded walk.		2026-09-29 03:39:33 +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	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

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