product-recognition-demo/recognization/data/external-eval
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
..
kewpie feat(eval): report the external benchmark per SKU 2026-09-29 03:37:04 +09:00
l12-reconciliation test(external): score 8 independent-source images, 0 false accepts 2026-09-29 03:13:16 +09:00
other-mayo feat(eval): report the external benchmark per SKU 2026-09-29 03:37:04 +09:00
tier3-sources test(external): score 8 independent-source images, 0 false accepts 2026-09-29 03:13:16 +09:00
README.md docs(decision): report the external benchmark on the corrected basis 2026-09-29 03:30:16 +09:00

External evidence (2026-09-29)

Independent-source media used to stress the identity pipeline. None of this is Tier 2. DECISION.md's Tier 2 is a second labelled store visit; these are external_proxy artifacts, and every manifest here says so.

Directory What it holds
other-mayo/ 8 accepted images for sku_l04, sku_l07, sku_l09 from 6 source domains, plus the scored result
l12-reconciliation/ What sku_l12 actually is, from the shelf tag and package pixels
kewpie/ 12 accepted images for six SKUs from 6 source domains, plus the scored result. One is now enrolled, so it is excluded from scoring.
tier3-sources/ Two reusable pan clips and 11 rejected candidates for a future Tier-3 replay

Leak checks

Two, both in code/eval_external.py, both run before any number is reported:

  • Against the evaluation photo. Every image is template-matched against add/image6.jpg. The highest score across all 20 is 0.675 (sku_l07_1), and the 0.95+ band that identifies a crop of the evaluation photo is empty.
  • Against the catalog. An image that has since been enrolled is refused rather than scored. kewpie/sku_l06_1.jpg is one: it is now the enrolled l06 view, so scoring it would be training accuracy wearing a held-out label.

Result: 19 scored, 0 false accepts

.venv/bin/python code/eval_external.py --stock stock-lopia-clean \
    data/external-eval/kewpie data/external-eval/other-mayo
Set Scored Correct Abstain False accepts Leaked
kewpie/ 11 8 3 0 1
other-mayo/ 8 4 4 0 0
Total 19 12 7 0 1

Two things this establishes that the image6 shelf cannot:

  • sku_l07 is answered with no enrolled image. Both l07 images accept through the text lane alone: sku_l07_1 reads ラッキー フヨネーズ, and sku_l07_2 reads the rear label's 製造者:丸和油脂株式会社 with 内容量:200g. The catalog row is 丸和油脂 ラッキーマヨネーズ 200g, so the manufacturer and the size together name it. This is the text lane's coverage claim - that a product with no reference image can still be named - holding on data that was not used to build it.
  • The l04 abstentions are correct behaviour, not a miss. l04 has no enrolled image either, and its reads are weak: sku_l04_2 reads only コと旨 タ 味 (all rows score 0.0), and sku_l04_4 reads the rear label with やまや garbled to まや, scoring 0.379 - below the accept line. Abstaining on a weak read is the designed outcome.

sku_l07_2 accepts with a negative local score (-0.55) on text evidence alone. That is the lane doing its job, but it is worth naming: the accept rests on two matched tokens (manufacturer + size), not on geometry.

What is still missing

No external source gives per-SKU identity for the whole catalog, and no reusable clip combines the target category with shelf-distance text resolution. The Tier-2 and Tier-3 gates in DECISION.md still need the operator's own recorded walks.