Both CSVs called sku_l11 キューピー ハーフマヨネーズ 450g. The shelf tag under
the l11 row in add/image6.jpg reads キユーピー ハーフマヨネーズ〈400g〉, and the
enrolled packshot's own OCR reads 400g, so the catalog shipped a size the
product does not have - the same defect class as sku_l10.
Measured on the corrected image6 labels, local lane: full 86% precision (was
84%), live 95% (was 91%), false accepts 8 and 2. 303 tests pass.
The previous commit corrected stock-lopia-clean/stock.csv to reference
sku_l10.jpg but left the view itself untracked, so a fresh checkout would
have loaded a catalog whose CSV names an image that is not there.
Ajinomoto ピュアセレクト マヨネーズ has no 450g size: the official lineup is
250g/400g/600g/1kg, the shelf tag in add/image6.jpg reads 400g, and DECISION.md
already recorded the mismatch. Both CSVs carried 450g, so every l10 answer
shipped a wrong size.
The packshot is the exact 400g front (JAN 4901001114030, 1000x1000) with its
source URL and the 450g rejection recorded in
data/packshots/research/sku_l10/manifest.json.
Enrolling it moves the catalog ceiling from 69% to 87% of in-catalog facings
answerable by image (l10 was 11 unenrolled facings). Measured on the corrected
image6 labels: local lane 92% identity at 84% precision full, 85% at 91% live.
Also points external-eval defaults at the repository-local data path; the
licensed Wikimedia corpus remains uncommitted pending manifest review.
Two enrollment defects, both found by trying to enroll real packshots.
`rebuild_catalog.py` crashed on every successful run: the report referenced
`small` but the call to `below_pyramid_floor` was missing, so the pyramid-floor
check the enrollment protocol asks for never ran and the tool died with a
NameError after writing the catalog.
The name/OCR check read one orientation. A packshot whose identifying text runs
vertically - the やまや めんたいマヨネーズ image reads 明太手屋さんが only when
rotated - was refused as a mismatch, which is the worst failure this check has:
it rejects good data while claiming to validate it. `best_orientation_text`
tries all four right-angle rotations and picks the one that explains the most of
the catalog name, not the one that reads the most characters, because the
upright pass returns stray kana that concatenate longer than the real read.
The regression test goes through `audit`, not the helper: the bug was that
`audit` called a single-orientation read, and a helper unit test would have
passed on the broken code. Verified to fail before the fix and pass after.
Data now lives in the repo, not /tmp:
data/packshots/source/ packshots as downloaded, with ATTRIBUTION.json
data/evaluations/ per-session run artifacts, so a number in
DECISION.md traces to the run that produced it
sku_l09 is re-sourced at 1000x1000 from Matsukiyo's Rakuten image, replacing
the 305px view that the README documents as the reason l09 wins facings it
should not.
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.
