Five blocks still printed pre-correction figures. Each was re-run through the
evaluator and replaced with what it actually outputs now:
- The lane comparison: local 75% at 91% against Van 64% at 38% (full), and
59% at 93% against 66% at 39% (live). At live resolution Van now answers more
facings correctly and is wrong on 67 of them to the local lane's 3 - stated
plainly rather than left as a win on both axes.
- The accept sweep: 75/67/67/62% end-to-end at 91/91/93/95% precision, with the
evaluator's own column definitions spelled out, since my first re-derivation
used the wrong denominator.
- The G1 stream claim: four pan replays give 63-70% coverage at 76-84%
precision, not 75% at 100%.
- The upscale paragraph: the guard is intact, but sku_l09.jpg is now a
1000x1000 packshot, so the current run no longer exercises it.
- catalog_hash and the pyramid floor: the hash covers view bytes, not just
names, and every view is now above the 800 px floor.
Also refreshes the server smoke against the renamed catalog: 53 named in 2.58 s,
no view-suffixed ids, no missing product info. l12 is not among them - it has
no enrolled view, which is the documented gap.
/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.
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.
