docs(decision): record the far-unknown calibration and the gate status

The far-unknown claim in the verdict said 0.2-0.7% on 602 crops. That was the
audit's variant matcher on the add/ photos; the shipped lane at the shipped
lines was 3.61% on the Wikimedia sets. Both are now stated, with the sweep that
chose 4.0/4 and the live-endpoint result: 44 named, 42 correct, 95.5% precision
against 53/48/90.6% before.

Gate status is recorded per gate rather than as a blanket claim: G2 and G3's
far half pass offline, G8 passes, and G1/G4/G5/G6/G7 are not yet measured on
held-out data because Tier 2 does not exist.

The Wikimedia manifests and results are committed; the 59 MB of images are not,
because collect_shelf_images.py fetches them from the source URLs the manifests
record.
This commit is contained in:
kiyo9w 2026-09-29 04:11:00 +09:00
parent 07de2bd37d
commit 4bdad66bbe
7 changed files with 1264 additions and 2 deletions

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View file

@ -56,8 +56,13 @@ store-scoped, enrolled catalog, with live-video evidence accumulation**.
every enrolled reference can be verified exhaustively. With thousands, it every enrolled reference can be verified exhaustively. With thousands, it
becomes retrieval plus verification (§6). becomes retrieval plus verification (§6).
- **Open set is the dominant difficulty, and it comes in two kinds.** *Far* - **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 unknowns (other categories) are rejected easily **once the accept lines are
602 unrelated crops at a threshold that keeps most correct labels. *Near* 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, 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, Pure Select plain next to Pure Select コクうま) produce genuine,
geometrically consistent evidence and can't be thresholded away. Near 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 | | 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 | | 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. 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: claim about the contents. Measured on the corrected labels, full resolution:
precision 86% -> 91%, false accepts 8 -> 5, coverage unchanged at 79%. 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 ### External benchmark: 19 scored, 0 false accepts
The image6 shelf is one photo with one label set, so every number above is The image6 shelf is one photo with one label set, so every number above is