feat(backend): scan-product accuracy 66.2% -> 79.7% + frozen validation benchmark

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

backend/docs/feature-list.md

@@ -55,6 +55,7 @@ workflow and have no task numbers; see `git log` for real dates/history.
 - **2.1 (verification pass)** Ran a full browser walkthrough of `/scan-pfm` (classification, top-5, OCR expiry extraction + crop, SKU-master matching, Visual/Spotting Grid, Raw Response — all confirmed working with real data). Found and fixed a real bug: "Save Ground Truth" was returning success but silently writing into the `pfm-web-app` container's ephemeral filesystem instead of the host, because `/sources` wasn't a bind-mounted path in root `docker-compose.yml`. Added `./backend/sources:/sources` to the `pfm-web-app` service, recovered an orphaned entry via `docker cp`, and re-verified the save now persists to `backend/sources/product_manual_labels.json` on the host (confirmed the DO-flow's `manual_labels.json` save was fixed by the same change too) — shipped 2026-07-08.
 - **2.3** Ran the accuracy regression harness and discovered `sources/accuracy_report.md` was badly stale (claimed 75.04%; real current baseline is **95.10% overall, already at/above the 95% target** — added a staleness banner to that file). Root-caused every remaining mismatch by pulling raw OCR text from Postgres (`documents.layout_parsing_result`): the worst field, `plat` (67.6%), is almost entirely the license-plate region being classified as an image/seal by the layout model rather than OCR'd as text — not fixable in `parser.ts`. Found and fixed one genuine parser logic bug along the way: the "global pattern scanning fallback" could duplicate an already-correctly-extracted `noDO` value into a still-missing `noSO` field; fixed by excluding already-assigned values from that fallback's candidate pool (`pfm-web-app/src/utils/parser.ts`). Doesn't change the aggregate score (a wrong value and "Not Found" score the same) but stops a fabricated-looking wrong number from silently reaching the database. All 48 parser unit tests still pass — shipped 2026-07-08.
 - **Ad-hoc** Built custom expiry-date-based auto-rotation algorithm in Python classifier server (`classify_ocr_server.py`). The algorithm calculates the slant angle of the Expiry Date / Batch text line bounding box, automatically rotates the image to make it horizontal, and re-runs YOLO classification + PaddleOCR for maximum accuracy. Enhanced SKU matching database lookup to prioritize exact SKU matches with a score of 1.0, pinning them as the Best Match — shipped 2026-07-09.
+- **2.5** Retrained the Product/SKU scan classifier's model artifacts against the full current dataset, which had grown to 81 SKU classes / 2,493 photos (up from the original 16 classes / 118 photos the deployed model dated 2026-07-08 was actually trained on — the other 65 classes had photos but no trained weights). Rebuilt `models/dinov2_index.pkl` (now 2,493/2,493 photos indexed) and retrained the YOLO classifier 100 epochs on an RTX 2060 (real elapsed time 54m21s), publishing `models/produk-pfm-classifier-26n-100e-2026-07-14.pt`/`.onnx` at **85.8% top-1 / 94.4% top-5** validation accuracy across all 81 classes (up from 83.3%/90% on the old 16-class model). Along the way, fixed a real train/val split bug in `train_classifier.py`: `split_dataset()` previously shuffled and split individual image files, letting an augmented copy (`photo_aug_2.jpeg`) land in validation while its near-duplicate source stayed in training — inflating val accuracy with memorization instead of measuring generalization; now groups by source photo (stripping `_aug_N`) before shuffling and splitting 80/20. Verified via `docker compose up -d pipeline-api` + `docker logs`: "DINOv2 index loaded with 2493 reference images", "Using classifier weights: .../produk-pfm-classifier-26n-100e-2026-07-14.pt", "YOLO model loaded successfully" — the live service is confirmed serving the new 81-class model, not assumed from the newest-file-by-date fallback logic. Remaining gap toward the program's ±230-SKU target is dataset growth, not a pipeline limitation — shipped 2026-07-14.
 
 ## Backend — Postgres Data Layer