Product recognition demo for supermarket shelf images
07de2bd37d
The gates were tuned on image6 alone, where every crop is a facing of a product the shelf actually holds. Wikimedia supermarket photos of milk, soy and snack shelves share nothing with the LOPIA catalog, so every accept there is a false accept by construction - and at the old lines there were 83 of them. Text lane: TEXT_MIN_SHARED 2 -> 4. The vocabulary filter drops bigrams no name contains, so a short garbage read can end up with a perfect ratio on two coincidental pairs. 20 of 26 text-lane answers on those shelves rested on exactly two: '避くて おいしい' named sku_l12 through おい/いし. Local lane: LOCAL_ACCEPT 2.0 -> 4.0. The residual accepts were geometric matches with no text overlap at all, scoring 2.26-4.81. Measured, 2297 distractor crops and 109 image6 facings: accept floor image6 shown/correct precision false accepts 2.0 2 54 / 49 90.7% 83 (3.61%) 2.0 4 52 / 48 92.3% 30 (1.31%) 4.0 4 44 / 42 95.5% 8 (0.35%) 5.0 6 36 / 36 100.0% 0 (0.00%) 4.0/4 is the only region passing both G2 (>=95% precision) and G3 (<=1% far-unknown) with coverage left. On the shipped evaluator: 66% end-to-end at 95% precision, 2 false accepts, against 77%/91%/5 before. The two tests that pinned the old floors now pin the new ones, with the reads that separate them. |
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| recognization | ||
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