The external evaluation showed the matcher cannot say "unknown": on 20 shelves whose
products are absent it claimed 47.8% of facings as recognised, and its raw inlier
score cannot be thresholded (foreign shelves' median 6.0 overlaps the real shelf's
5.2 p10, and the worst offenders score 50-190). So the gallery becomes a selectable
identity backend:
--identity gallery (or DORAYAKI_IDENTITY=gallery)
Measured on the same 20 foreign shelves: false accepts fall from 47.8% to 13.8% at
the default threshold and to **5%** at 0.60, with a smooth curve to pick an operating
point from. Detection is unchanged (the gallery only names boxes).
What it does NOT fix, measured on a milk shelf where 14 of 107 facings really are
明治 おいしい牛乳 (labelled by eye): precision is 28-45%, no better than the matcher's
39%, and enrolling the shelf from itself (1-3 views per product) is flat at ~25%
top-1. Off-the-shelf DINOv2 is not discriminative enough for fine-grained SKUs - the
same conclusion the literature reaches, now measured here.
The packaging's own text is a real channel and resolution-bound: 51% recall
single-scale, 60% with the tag reader's multi-scale + red-channel trick, 85% where
the text is large (MEGMILK) and 28% where it is small vertical Japanese.
docs/EXTERNAL-EVAL.md records all of it, including the identity architecture it
implies: reject by default, name from the tag when readable, from the packaging text
when legible, from a fine-tuned embedding otherwise, and hand the rest to the
operator.
175 tests pass.
Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>
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