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/scan-product.md

@@ -119,9 +119,9 @@ pipeline call with `promptLabel: "spotting"`, no layout detection).
 
 | File (`pfm-web-app/public/produk-pfm/`) | What |
 |---|---|
-| `foto-kemasan-v2/<SKU or class>/…` | Reference photo dataset — 16 classes, 118 photos (2–16 each) |
-| `models/dinov2_index.pkl` | DINOv2 embeddings + metadata (rebuild after adding photos) |
-| `models/produk-pfm-classifier-26n-100e-2026-07-08.pt` / `.onnx` | Fine-tuned YOLO classifier (83.3% top-1 / 90% top-5 val on the thin dataset) |
+| `foto-kemasan-v2/<SKU or class>/…` | Reference photo dataset — 81 classes, 2,493 photos (target ~230 SKU) |
+| `models/dinov2_index.pkl` | DINOv2 embeddings + metadata (rebuild after adding photos) — currently indexes all 2,493 photos across 81 classes |
+| `models/produk-pfm-classifier-26n-100e-2026-07-14.pt` / `.onnx` | Fine-tuned YOLO classifier (85.8% top-1 / 94.4% top-5 val across all 81 classes; retrained 2026-07-14, 54m21s on an RTX 2060, up from the prior 2026-07-08 model's 83.3%/90% on only 16 classes) |
 | `index_dinov2.py` | Rebuilds the pickle index from `foto-kemasan-v2/` |
 | `train_classifier.py` | Splits 80/20 into `yolo_dataset/`, fine-tunes `yolo26n-cls.pt` (default 100 epochs, `--imgsz 224`), writes a dated checkpoint |
 
@@ -144,10 +144,16 @@ every labeled image in `sources/product_manual_labels.json`, checks 3 fields
 (`no_sku`, `nama_item`, `expiry_date`) against ground truth, and splits into:
 - **Training Set** — gallery photos under `foto-kemasan-v2/` (the classifier's
   own reference images; scores here measure memorization, not generalization).
-- **Validation Set** — flat filenames dropped into
-  `sources/product-test-images/` (a real held-out set; see that folder's
-  `README.md` for the drop-photo → label → re-run workflow via
-  `/manual-label-scan`).
+- **Validation Set** — flat filenames, scored from the frozen
+  `sources/product-test-images-fixed/` snapshot (renamed `<index> <no_sku>.<ext>`,
+  built by `scripts/freeze-validation-set.mjs`) so a rerun always grades the
+  same 79 images regardless of what's since been dropped into the live-intake
+  `sources/product-test-images/` folder. See each folder's `README.md` — the
+  live folder documents the drop-photo → label → re-run-freeze-script workflow
+  via `/manual-label-scan`; the fixed folder documents the freeze/promote step
+  and flags 5 SKUs (12010801, 12012504, 12130504, 13050101, 15040102) whose
+  only available photo was already used to train the classifier, so their
+  scores aren't a clean held-out result.
 
 Every run appends to `sources/product_accuracy_history.jsonl` and **auto-diffs
 against the previous run**: the printed summary shows a Δ column per field per
@@ -185,10 +191,9 @@ Tracked ones (see `plans/next-enhancements.md`):
 - **Dataset thinness**: 2–16 photos/class caps both classifiers; every new real
   photo (especially non-studio, in-warehouse shots) matters. The harness above
   already reports gallery (training) vs. held-out (validation) accuracy
-  separately — but as of this writing `sources/product-test-images/` is empty,
-  so the Validation Set is still 0 images and every published number so far is
-  a training/memorization score. Dropping real photos there is the next step,
-  not yet done.
+  separately, and as of 2026-07-14 the Validation Set has 79 labeled images
+  (74 genuinely held out, 5 flagged trained-on — see above) — the first real
+  (non-zero) Validation Set numbers.
 
 Additional recommendations (not yet tasks — promote via `e`/`n` when wanted):
 1. ~~Use `extracted_sku` in match ranking.~~ **Done** — `product-scan.ts`'s