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The stricter gates trade 9 names for 3 wrong labels." +} \ No newline at end of file diff --git a/recognization/data/evaluations/2026-09-29-session/server-smoke/recognize-gate-calibrated.json b/recognization/data/evaluations/2026-09-29-session/server-smoke/recognize-gate-calibrated.json new file mode 100644 index 0000000..b47cbaa --- /dev/null +++ b/recognization/data/evaluations/2026-09-29-session/server-smoke/recognize-gate-calibrated.json @@ -0,0 +1 @@ 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With thousands, it becomes retrieval plus verification (§6). - **Open set is the dominant difficulty, and it comes in two kinds.** *Far* - unknowns (other categories) are rejected easily: 0.2–0.7 % false accepts on - 602 unrelated crops at a threshold that keeps most correct labels. *Near* + unknowns (other categories) are rejected easily **once the accept lines are + calibrated against them**: 0.37 % and 0.24 % false accepts on 2297 crops from + 56 unrelated Wikimedia supermarket photos, at a threshold that keeps 42 of 48 + correct image6 labels. The audit's 0.2-0.7 % was measured on 602 crops from + the `add/` photos with the audit's own variant matcher; the shipped lane at + the shipped lines was 3.61 % until 2026-09-29 (§ Re-measured, far unknowns). + *Near* unknowns (siblings of enrolled products: Kewpie 450 g next to Kewpie からし, Pure Select plain next to Pure Select コクうま) produce genuine, geometrically consistent evidence and can't be thresholded away. Near @@ -389,6 +394,14 @@ with always-argmax, run on that same catalog. | G7 hubness | max predicted-SKU share ÷ that SKU's true share | ≤ 2.0 | | G8 runtime | server identity latency p95 per crop; server memory | ≤ 60 ms; within the demo host's budget | +Status 2026-09-29, offline on image6 plus the Wikimedia distractor sets: +**G2 passes** (95 % precision, full resolution). **G3's far-unknown half +passes** (0.37 % / 0.24 % on 2297 unrelated crops); its near-unknown half is +measured on image6 only, where the size gate rejects the documented 450 g case. +**G8 passes** (identity p95 well inside 60 ms). **G1, G4, G5, G6, G7 are not +yet measured on held-out data** - Tier 2 does not exist, and G4/G5 need a +recorded walk. Nothing here is a device-path result. + Not complete until G1–G7 pass on the device path (Tier 3), not just offline. --- @@ -838,6 +851,46 @@ out of "210g"), and percentages are excluded because "カロリー 50% カット claim about the contents. Measured on the corrected labels, full resolution: precision 86% -> 91%, false accepts 8 -> 5, coverage unchanged at 79%. + +### Far unknowns: the accept lines were tuned on a shelf that has no unknowns + +Every threshold in this document was fitted on image6, where each crop is a +facing of a product the shelf actually holds. That is a closed-set tuning set, +and it hid the far-unknown rate completely: the shipped lane accepted 83 of +2297 crops from 56 Wikimedia Commons supermarket photos of milk, soy and snack +shelves - 3.61 %, against G3's 1 %. + +Those photos share nothing with the LOPIA catalog, so every accept is a false +accept by construction. Two mechanisms produced them: + +- **The text lane answered on two coincidental bigrams.** The vocabulary filter + drops bigrams no catalog name contains, so a short garbage read can end up + with a perfect ratio on two pairs. 20 of 26 text-lane answers rested on + exactly two: `避くて おいしい` named `sku_l12` through おい/いし. +- **The local lane accepted pure geometry.** The residual 25 were matches with + no text overlap at all, scoring 2.26-4.81 against an accept line of 2.0. + +Both lines moved: `TEXT_MIN_SHARED` 2 -> 4, `LOCAL_ACCEPT` 2.0 -> 4.0. Measured +on 2297 distractor crops and 109 image6 facings: + +| `LOCAL_ACCEPT` | `TEXT_MIN_SHARED` | 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 that is 66 % +end-to-end at 95 % precision with 2 false accepts, against 77 % / 91 % / 5 +before - the coverage is what the precision costs, and G2 is the gate that says +to pay it. + +Reproduce: `.venv/bin/python code/eval_distractors.py --stock stock-lopia-clean +--write data/external-shelves/wikimedia-commons data/external-shelves/wikimedia-commons-2`. +The images are fetched by `code/collect_shelf_images.py` and are not committed; +the manifests (source URL, licence, artist) and the results are. + ### External benchmark: 19 scored, 0 false accepts The image6 shelf is one photo with one label set, so every number above is