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
26 lines
1.4 KiB
Markdown
26 lines
1.4 KiB
Markdown
# Product-scan validation images — live intake (staging)
|
|
|
|
This folder is the **live-intake / staging area** for the product-scan
|
|
validation set. It's still where the `/manual-label-scan` page saves new
|
|
photo drops, and still what real-world photos get dropped into by hand — but
|
|
it is **no longer what the accuracy harness scores**. That's
|
|
`../product-test-images-fixed/` (a frozen, sequentially-renamed snapshot) —
|
|
see that folder's README for why the split exists.
|
|
|
|
**Workflow (adding a new SKU or photo):**
|
|
1. Drop a photo here directly (flat, no subfolders — a filename with no `/`
|
|
is what marks an image as "validation" instead of "training").
|
|
2. Label it via the `/manual-label-scan` page (correct `no_sku`, `nama_item`,
|
|
`expiry_date` by hand — don't just accept the AI-scan prefill, that would
|
|
make the ground truth equal to the model's own prediction).
|
|
3. Re-run `node scripts/freeze-validation-set.mjs` from `backend/` to promote
|
|
the new photo into `product-test-images-fixed/` (renamed to
|
|
`<index> <no_sku>.<ext>`) so it actually gets scored on the next
|
|
`accuracy-check-scan.mts` run.
|
|
|
|
**This is not where new training photos go.** To improve the classifier
|
|
itself (DINOv2 index / YOLO fine-tune), add photos to
|
|
`pfm-web-app/public/produk-pfm/foto-kemasan-v2/<SKU folder>/` instead, then
|
|
reindex/retrain per `docs/scan-product.md`'s "Model artifacts & retraining"
|
|
section.
|