Rafhan Mazaya FathurrahmanandClaude Fable 5
e76ccb60a6
Accuracy work on the 79-image product-scan validation set (user goal: 90%): - classify_ocr_server.py: 0/90/180/270-degree expiry-date search (stops at first hit, 0-degree fallback); classification decoupled onto the upright image (rotated frames regressed DINOv2 -6pts until this); cross-line date stitching; tiled full-res OCR pass (defeats the 4000px downscale that killed small inkjet dates); VL-pipeline expiry fallback with keyword-anchored anti-hallucination guard; VL text lines merged into text_lines + VL SKU retry. Visualization endpoints removed entirely (Visual/Spotting grids - unused by frontend, 3x per-scan GPU cost). - product-scan.ts: coverage-normalized OCR-evidence re-ranking of DINOv2 top-K (tuned offline: +8/-0 on top-1 misses), re-ranked class mapped to sku_master by SKU prefix; classifier timeout 90s->240s for fallback paths. - Frozen benchmark: product-test-images-fixed/ (79 renamed images) + freeze/seed/build-undetected/capture/experiment scripts; labels trimmed to the 79 validation entries (training rows kept in .bak-with-training); 5 TRAINED-ON SKUs replaced with fresh held-out photos. - manual-label-scan page: shows last batch-test AI prediction under every field by default (new /api/product-scan-results); serves the fixed folder; fixed total hydration failure via allowedDevOrigins 127.0.0.1. - Measured (all-79, zero failures): sku/name 87.3%, expiry 64.6%, overall 79.7%. Tiles/VL-evidence/VL-SKU deployed but not yet batch-measured. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Gr6HH7JrdsXX8AARejQboM
Product-scan validation images — frozen benchmark set
This is the actual Validation Set the accuracy harness scores
(accuracy-check-scan.mts's getImagePath() points here, not at
../product-test-images/). It exists so re-running the harness always grades
the exact same images — the live-intake folder can keep growing from new
/manual-label-scan drops without silently shifting the benchmark underfoot.
Naming: each file is <index> <no_sku>.<ext> (e.g. 1 11110059.jpeg),
where <index> is just this file's stable position in the set — it carries
no other meaning. product_manual_labels.json's ground-truth entries for
these images use this same filename.
Do not hand-edit this folder. It's fully generated by
node scripts/freeze-validation-set.mjs (run from backend/), which copies
every flat (non-training) entry out of product_manual_labels.json from
../product-test-images/, renames it, and rewrites those entries'
filename fields to match. To add a new SKU/photo to the benchmark:
label it in the live-intake folder first (see that folder's README), then
re-run the freeze script.
79 images as of 2026-07-14. 5 of them (SKUs 12010801, 12012504, 12130504,
13050101, 15040102) are flagged TRAINED-ON in product_manual_labels.json's
notes field — their only available source photo (from an external
research-sam3 segmentation project) was already used to train the
classifier (as a SAM3 crop + augmentations), so they are not a clean
held-out test. Their per-image scores will read as memorization, not real
generalization, until a fresh, never-trained-on photo is dropped for those
SKUs. The other 74 are genuinely held out.