The reproduction block still printed the pre-enrollment numbers (catalog 69%,
decision 86%); it now prints what the command actually outputs today, with the
catalog figure's fall from 87% to 83% explained rather than left to look like a
regression.
Adds the three row corrections with their evidence, the near-unknown fix and
its measured effect, and the state of the four unenrolled SKUs - including that
l08's row name and size are themselves unverified, since no retailer listing
for a 30g スパイシーストーム ペリペリミックス exists.
cat_full's *_shelf.jpg views are crops of add/image6.jpg itself - template
matching puts each one back at 0.998-0.999 confidence - so committing them
reintroduced the evaluation photo as a catalog, which is the leak the audit
removed from stock-lopia. cat_pack is the same views without the shelf copies.
Both are regenerated by lfo_setup.py from stock-lopia and the labels, so the
evidence stays reproducible without the repository carrying the leak. Verified
after removal: lfo_setup.py rebuilds all 16 views.
E-3: the text lane adds 31 pp coverage at full resolution and 23 pp at live
while raising precision, against a +5 pp bar. E-2: LocalLane is RootSIFT-only,
so ORB/BRISK have nothing left to drop from the shipped path.
`classify_boxes` returned only the identity, so every accepted still-image
answer reached the client as a bare SKU id with no name or price. Measured
through the real server on add/image6.jpg: 41 of 41 accepted detections had no
`product`. The live path attached the row; the still path did not, which is
the F-02 finding the decision document recorded as still live in production.
The row is attached where the decision is made, so both paths carry it, and
`recognize` passes it through to the wire. Verified through the running server:
41 of 41 accepted answers now carry product_name, price, and stock counts.
Also corrects the decision document's full-resolution claim, which was
measured before the reference-upscaling and text-signature fixes: it said 77%
precision with 11 false accepts and a hub alarm, where the current lane reads
88% with 5 and no alarm. The accept line does not need to differ by input path
- correct facings sit at p25 2.07 (full) and 2.87 (live), wrong ones at p75
1.00 and 1.66, so 2.0 is the boundary in both - and the results table and the
stream G1 numbers are refreshed to the current measurements.
`catalog_hash` hashed the CSV and the view *names*, so replacing
`sku_l09.jpg` with a different product's packshot left the hash unchanged and
a report could claim the same catalog while every l09 decision in it had
moved. It hashes each view's bytes now.
`identity_lanes` carried its own `normalize_text` and `bigrams`, near-copies
of the ones in `rebuild_catalog` that had already drifted (the lane's table
folded ユ to itself, the catalog's did not). The lane imports the catalog's
now, so one read scores the same either way.
`evidence/image6_v2.json` was byte-identical to `eval/labels/image6.json`; the
evidence scripts and the decision document point at the canonical file and the
copy is gone. `score_crop`'s docstring now states the contract its callers
already depend on: the dict's insertion order is the lane's ranking, and
re-sorting by value names a different SKU than the lane chose.
Tests: the lane is shared by the classify pool, so a concurrent-ranking test
pins that one BFMatcher does not cross crops under eight threads.
The pacing paragraph described a defect that no longer exists. Replaced with
the measured before/after, and added the two review findings (the missing
always-argmax baseline lane, the size-token fill-in) with their test counts.
Two review findings, both confirmed by measurement.
1. The evaluator had no always-argmax lane, so every "vs Van" claim compared
against the *gated* matcher, which is not Van's contract - the original demo
answered every crop with its best-scoring SKU and had no accept line. The new
'van' lane is that contract: same scores, no gate. Against it the local lane
wins both axes at both resolutions (full 61%/77% vs 52%/29%; live 59%/95% vs
57%/32%), but the live coverage margin is one facing, not seven, and the 95%
figure is live-resolution only - at full resolution the same lane reads 77%
with 11 false accepts and a hub alarm.
2. A crop reading only "400g" scored 0.667 on three SKUs, above TEXT_ACCEPT, so
the text lane named a product from a size line. The fill-in job now requires
two non-digit bigrams; the veto is unchanged, so "65%" still contradicts a
local answer. The raw OCR read travels with the ranking through
score_crop_lanes for that check.
Tests: 3 for the size gate (policy + live) and the van lane's own row. Removing
the gate fails all three. Suite 276 -> 280.
The live label count is a function of frame pacing, not a fixed property: a
track needs two attempts to settle, so pushing frames faster than the classifier
drains them reports fewer labels (12/8/2/6 at full speed on the same clip, 14
paced, 38 of 30 frames). Precision stays 100% and 0 unknowns are accepted at
every pacing. Also records the local-scale and DecisionPolicy tests.
The record said '2 false accepts of 48 unknowns', which conflated two fields:
the 2 are 0 unknown accepts plus 1 unenrolled in-catalog facing and 1 enrolled
misidentification. Also restores the hybrid-verify line the lane vocabulary
edit dropped, and reframes the l09 note: its hubness ratio is 1.76 full /
1.36 live against G7's limit of 2.0, so the gate passes and the 30% internal
share heuristic is what raises the alarm.
Replaces the DINO gallery as the recognition path with the measured design
from docs/research/2026-09-28-recognition-architecture/DECISION.md: RootSIFT
over a per-SKU reference pyramid, similarity-constrained RANSAC and a hue
check, fused with an OCR text lane that vetoes a match whose package text
names a different catalog row and answers on its own when geometry finds
nothing.
- identity_lanes.py: LocalLane, TextLane, DecisionPolicy. The text lane
builds each row's signature from the catalog name plus its packshot OCR, so
a row with no enrolled image is still answerable.
- engine.py: every backend groups scores by canonical SKU, so no
view-suffixed id reaches the wire and a name and price always resolve.
decide_crop fuses the lanes; score_crop_lanes feeds the live path. Both
lanes are built in warm_up (non-fatally) so the first frame is not the slow
one, and the text lane shares the engine's OCR lock.
- live.py: decides per crop on the lane's own rank order. The lane's order
comes from its rank key and its value is only the gate, so re-sorting by
value named a different SKU than the lane chose; averaging in an empty
observation also diluted a good confidence below the accept line.
- identity.py: a `local` score scale whose gate is one absolute line, because
the value is already a margin over the runner-up.
- app.py, run-demo-server.sh: `--identity local` is the default; the demo
server no longer starts on the leaked `stock-lopia` catalog.
- eval_live.py: the replay vocabulary accepts `local`, so a recording from
the shipped server can be scored.
- README.md: documents the three backends and what each is for.
Measured on the corrected image6 labels at live resolution: 36/61 in-catalog
facings named at 95% precision, 0 of 48 unknowns accepted, 11 labels sourced
from the text lane alone. Through the real server: /v1/recognize 90%
precision, /v1/stream 100% precision, no view-suffixed ids on either.
