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v3: deep clean via per-source cross-val + manual curation
- Per-source iterative cross-val cleaning (3 rounds each)
- racing-original: 99.6% OOF, removed all mislabeled 4s (were actually 1s)
- sebring: 82% OOF, removed 91 noisy transition frames
- mnist: 99% OOF, removed 30 ambiguous digits
- Fixed Paul Ricard gear_4 label CSV (was pointing to a 3)
- Fixed gear_4 example image
- Per-label verification composites in composites/{train,validation}/
- 5,391 train / 903 val -
add per-label verification composites and cleaning scripts
- composites/train/label_N.png — racing-only training samples per digit
- composites/validation/label_N.png — racing-only val samples per digit
- scripts/make_composites.py — regenerate composites from parquet
- scripts/iterative_clean.py — multi-round cross-val + ensemble cleaning
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cleanup: remove temp files, fix find_label_issues.py guard
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fix: build_dataset.py writes HF Image feature type
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v2: add new sources, extraction pipeline, better train/val split
- Added Sebring Q Tobi Lap 6 and Paul Ricard Alpine LMPh video sources
- Proper stratified 85/15 train/val split (was nearly empty val before)
- Augmented minority racing classes (gears 4, 7) to 200 samples
- 5,964 train / 1,003 val images (up from 5,321 / 318)
- Added scripts/extract.py and scripts/build_dataset.py for pipeline
- Added scripts/find_label_issues.py (cleanlab-based QA)
- Added examples/ with reference crops per gear per source
- Added labels/ CSVs for frame-range labeling
- Added AGENTS.md with domain context and crop coordinates
