Rafhan Mazaya FathurrahmanandClaude Sonnet 5
3a17c28758
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
22 lines
1.1 KiB
Markdown
22 lines
1.1 KiB
Markdown
# Product-scan validation images
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This folder is the **validation/test set** for the product-scan accuracy
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harness (`backend/scripts/accuracy-check-scan.mts`) — real-world photos that
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are *not* part of the classifier's reference dataset, so scoring against them
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measures actual accuracy instead of memorization.
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**Workflow:**
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1. Drop a photo here directly (flat, no subfolders — a filename with no `/`
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is what marks an image as "validation" instead of "training").
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2. Label it via the `/manual-label-scan` page (correct `no_sku`, `nama_item`,
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`expiry_date` by hand — don't just accept the AI-scan prefill, that would
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make the ground truth equal to the model's own prediction).
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3. Run `node scripts/accuracy-check-scan.mts` from `backend/` — the photo now
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scores under "Validation Set", separate from "Training Set".
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**This is not where new training photos go.** To improve the classifier
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itself (DINOv2 index / YOLO fine-tune), add photos to
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`pfm-web-app/public/produk-pfm/foto-kemasan-v2/<SKU folder>/` instead, then
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reindex/retrain per `docs/scan-product.md`'s "Model artifacts & retraining"
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section.
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