The far-unknown claim in the verdict said 0.2-0.7% on 602 crops. That was the audit's variant matcher on the add/ photos; the shipped lane at the shipped lines was 3.61% on the Wikimedia sets. Both are now stated, with the sweep that chose 4.0/4 and the live-endpoint result: 44 named, 42 correct, 95.5% precision against 53/48/90.6% before. Gate status is recorded per gate rather than as a blanket claim: G2 and G3's far half pass offline, G8 passes, and G1/G4/G5/G6/G7 are not yet measured on held-out data because Tier 2 does not exist. The Wikimedia manifests and results are committed; the 59 MB of images are not, because collect_shelf_images.py fetches them from the source URLs the manifests record. |
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|---|---|---|
| .. | ||
| evaluations/2026-09-29-session | ||
| external-eval | ||
| external-shelves | ||
| packshots | ||
| 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