feat(backend): diff-vs-previous-run reporting for product-scan accuracy harness

Ports the DO-harness's auto-diff-vs-previous-run reporting into
accuracy-check-scan.mts: prints a per-field, per-split (Training/
Validation) delta against the last product_accuracy_history.jsonl entry
and calls out regressions/improvements explicitly, plus classifier
method distribution and average confidence as informational context.

Also adds the real held-out validation photo set into
sources/product-test-images/ (75 photos, one per current SKU class) with
its README documenting the drop-photo -> label -> re-run workflow, so the
harness's Validation Set split actually has images to score against.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Xsxk4ZkDQVVaLUcixDcqb5

backend/sources/product-test-images/README.md

@@ -0,0 +1,21 @@
+# Product-scan validation images
+
+This folder is the **validation/test set** for the product-scan accuracy
+harness (`backend/scripts/accuracy-check-scan.mts`) — real-world photos that
+are *not* part of the classifier's reference dataset, so scoring against them
+measures actual accuracy instead of memorization.
+
+**Workflow:**
+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. Run `node scripts/accuracy-check-scan.mts` from `backend/` — the photo now
+   scores under "Validation Set", separate from "Training Set".
+
+**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.