Product recognition demo for supermarket shelf images

kiyo9w 4bdad66bbe docs(decision): record the far-unknown calibration and the gate status ... 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.		2026-09-29 04:11:00 +09:00
.github/workflows	chore: run the Python suite from the repository root	2026-09-28 17:26:58 +09:00
recognization	docs(decision): record the far-unknown calibration and the gate status	2026-09-29 04:11:00 +09:00
.gitignore	Initial commit for push folder	2026-09-18 09:13:53 +09:00