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/CLAUDE.md

@@ -26,7 +26,7 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
 
 ## Product/SKU scanning flow — status
 
-**How it works end-to-end** (architecture, endpoints, classification/OCR internals, retraining): [`docs/scan-product.md`](docs/scan-product.md). See [`plans/next-enhancements.md`](plans/next-enhancements.md) §2 (task 2.1) for full detail — kept there instead of a separate doc so status stays traceable against the rest of the `e`/`n` backlog. **Feature-complete as of 2026-07-08**: the backend (`config/classify_ocr_server.py` with DINOv2 similarity search + YOLO classifier fallback, `api/scan-pfm/route.ts`, `api/produk-pfm/route.ts`, DB schema), the reference photo dataset (`pfm-web-app/public/produk-pfm/foto-kemasan-v2/`, 16 SKU subfolders), the desktop frontend page (`scan-pfm/page.tsx`, full feature parity), and the trained model artifacts (`models/dinov2_index.pkl` — 118/118 photos indexed; `models/produk-pfm-classifier-26n-100e-2026-07-08.pt` — 83.3% top-1 val accuracy on the current thin dataset) all now exist and load cleanly on `pipeline-api` startup. **No mobile web page is planned**: `scan-pfm/page.tsx` is desktop-only, used to test the pipeline; real mobile product scanning goes through the Flutter app instead, so `m-scan-pfm/page.tsx` and its `nginx.conf` route are intentionally left unbuilt/dead (see plan task 2.2, cancelled 2026-07-08). Not yet done: an actual browser pass uploading a photo through `/scan-pfm` end-to-end (verified via container logs/model-loading so far, not a UI test).
+**How it works end-to-end** (architecture, endpoints, classification/OCR internals, retraining): [`docs/scan-product.md`](docs/scan-product.md). See [`plans/next-enhancements.md`](plans/next-enhancements.md) §2 (task 2.1) for full detail — kept there instead of a separate doc so status stays traceable against the rest of the `e`/`n` backlog. **Feature-complete as of 2026-07-08**: the backend (`config/classify_ocr_server.py` with DINOv2 similarity search + YOLO classifier fallback, `api/scan-pfm/route.ts`, `api/produk-pfm/route.ts`, DB schema), the reference photo dataset (`pfm-web-app/public/produk-pfm/foto-kemasan-v2/`, 81 SKU subfolders as of 2026-07-14, up from the original 16 — target ~230), the desktop frontend page (`scan-pfm/page.tsx`, full feature parity), and the trained model artifacts (`models/dinov2_index.pkl` — 2,493/2,493 photos indexed as of 2026-07-14; `models/produk-pfm-classifier-26n-100e-2026-07-14.pt` — 85.8% top-1 / 94.4% top-5 val accuracy across all 81 classes, retrained 2026-07-14 in 54m21s on an RTX 2060) all now exist and load cleanly on `pipeline-api` startup. **No mobile web page is planned**: `scan-pfm/page.tsx` is desktop-only, used to test the pipeline; real mobile product scanning goes through the Flutter app instead, so `m-scan-pfm/page.tsx` and its `nginx.conf` route are intentionally left unbuilt/dead (see plan task 2.2, cancelled 2026-07-08). Not yet done: an actual browser pass uploading a photo through `/scan-pfm` end-to-end (verified via container logs/model-loading so far, not a UI test).
 
 ## Confidentiality