feat: a multi-view embedding gallery, because one template per SKU cannot work

The user's objection was right and it changed the design: the tag channel is sparse in the real world (measured: 0 tags readable at ~2 m on the main shelf), so packaging recognition has to carry identity. Measured on a 12-class, 57-facing evaluation set read off the demo shelf, the packaging channel as shipped cannot: views per SKU embedding top-1 shipped matcher top-1 1 57 % 33 % 2 72 % 27 % 3 75 % 20 % 4 76 % 5 % Two things follow. First, two views per SKU is where most of the gain is - that is the enrollment spec (a first scan already produces several facings per product). Second, the matcher gets *worse* with more views, because its raw inlier score is not calibrated to be aggregated and it cannot express two views of one SKU at all: it keys templates by filename. So identity becomes retrieval: `gallery.py` embeds every enrolled view with DINOv2 ViT-S/14, scores a SKU by its best view, and rejects when nothing is close enough (an unknown product must come back unknown, not as the nearest stranger). Measured with held-out views - the gallery never contains its own queries - 2 views per SKU over 12 SKUs gives 69 % top-1 with rejection working, and the threshold moves the rejection line. It is affordable in the live loop only on the GPU: 159 ms per crop on CPU versus 3.6 ms on the M5's MPS, and a live frame only needs its new tracks embedded. 157 tests pass; test_gallery.py covers the retrieval contract (best view wins, several views are one answer, strangers are rejected) with injected embeddings so it needs no torch. Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>
2026-09-24 23:43:27 +09:00 · 2026-09-24 23:43:27 +09:00 · e854aea23d
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