Facings #46-48 carried the note 'possibly what stock.csv calls l12'. The l12
reconciliation settled it: the shelf tag under them reads ユーラス 野菜を
おいしく食べる旨タレ (380g) at ¥299, the price the l12 row carries, and the row
has been renamed to match. Leaving them unknown understated the catalog and
overstated precision.
Counts move from 61 in-catalog / 48 unknown to 64 / 45. The enrollment ceiling
falls 87% -> 83% because l12's three facings now count as in-catalog with no
enrolled view - an honest catalog gap, not a recognition regression.
Also records the Tier-3 source search: two reusable clips (CC BY 3.0 Wikimedia
1920x1080 supermarket pan; CC BY YouTube 360p mayonnaise-bay pan), 11 rejected
candidates, and the LOPIA walk that carries the standard YouTube licence and
was therefore not downloaded. Both clips are labelled tier3_proxy_candidate,
never Tier 3: that still needs the operator's own recorded walks.
The row named a product that is not on the shelf. The tag under facings #46-48
reads ユーラス 野菜をおいしく食べる旨タレ (380g) at ¥299 - the exact price the
l12 row already carried - and the package reads お子さまOK 甘めテイスト, the
opposite of 中辛. Category, heat level and brand were all wrong.
This one is worse than a wrong size: the text lane scores a crop against the
row's printed name, so a correct read of the real bottle would have been
vetoed by a row describing a different product.
The 2023 green-label predecessor (380ml, JAN 4571233473887, checksum verified)
is recorded in the manifest as predecessor-only evidence; no current JAN is
claimed, because the current white package's rear label has not been
photographed. One in-store rear-label photo would close that.
/v1/recognize on add/image6.jpg against stock-lopia-clean: 111 detections,
53 named, every one carrying product_name and price, no view-suffixed ids.
sku_l10 answers 9 facings, which it could not do before it was enrolled.
The live WebSocket path was exercised too: 217 frames, 77 tracks labelled,
0 unexplained label changes, l09 and l10 in the final summary.
Three enrollment views were refused by the name/OCR check at 0.70, and the
research says all three refusals are false negatives:
- sku_l04: the packshot is byte-identical (MD5 510db1965bc483899c0cce17c72ba10e)
to Amazon media 61yWDtDjDuL for ASIN B0197VWHRG, やまや めんたいマヨネーズタイプ
200g, JAN 4903085062863. 明太子屋さんがつくった is a package slogan, not a
competing brand. Caveat recorded: 200g is superseded by the current 300g.
- sku_l07: 丸和油脂 ラッキーマヨネーズ 200g, JAN 4978931401615, corroborated by
Amazon, Rakuten, Yahoo Shopping and Kakaku. The front prints the product name
but never the maker or size, so full-name overlap cannot reach 0.70.
- sku_l10: exact 400g front, JAN 4901001114030, with the 450g rejection.
Also adds the missing sku_l10 OCR read, generated with the current engine, and
l10's attribution entry. Manifests are tracked; the retailer images they cite
are restricted-research and stay untracked, matching the existing precedent.
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.
