History

kiyo9w 22db40eb93 fix(labels): the tare facings are sku_l12, not unknown Facings #46-48 carried the note 'possibly what stock.csv calls l12'. The l12 reconciliation settled it: the shelf tag under them reads ユーラス野菜をおいしく食べる旨タレ (380g) at ¥299, the price the l12 row carries, and the row has been renamed to match. Leaving them unknown understated the catalog and overstated precision. Counts move from 61 in-catalog / 48 unknown to 64 / 45. The enrollment ceiling falls 87% -> 83% because l12's three facings now count as in-catalog with no enrolled view - an honest catalog gap, not a recognition regression. Also records the Tier-3 source search: two reusable clips (CC BY 3.0 Wikimedia 1920x1080 supermarket pan; CC BY YouTube 360p mayonnaise-bay pan), 11 rejected candidates, and the LOPIA walk that carries the standard YouTube licence and was therefore not downloaded. Both clips are labelled tier3_proxy_candidate, never Tier 3: that still needs the operator's own recorded walks.		2026-09-29 03:09:15 +09:00
..
evaluations/2026-09-29-session	test(server): record the real-endpoint smoke after the size gate	2026-09-29 03:06:28 +09:00
external-eval	fix(labels): the tare facings are sku_l12, not unknown	2026-09-29 03:09:15 +09:00
packshots	docs(packshots): record l04/l07/l10 identity provenance	2026-09-29 02:51:55 +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