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Bake metrics narrative into submission notebook (§2.4–§2.6, §3.3, §4.1, §11.2) (#29)
theDocWho +2: theDocWho, Claude Opus 4.7 Unverified signatureReviewers now see the full identifier / damage_seg / parts_seg / Variant-D story statically without running anything: - §2.4 — markdown explaining why 0.40 (VMMRdb) vs 0.77 (Stanford) is a preprocessing mismatch and class-granularity effect, not a model regression. Six-factor breakdown + an honest comparison table for Options 1 / 2 / 3. - §2.5 — train/val loss + top-1 accuracy curves per epoch with stage boundary annotated. Stanford curve always plotted; VMMRdb plotted when metrics.json is pulled from the Colab run. - §2.6 — three confusion / F1 views from the cached Stanford F1 run (data/eval/identifier_stanford_f1.json): top-20 by support heatmap, bottom-20 by F1 bar chart, and make-level aggregated heatmap (49 makes, 0.854 accuracy — the metric Variant D actually depends on). - §3.3 / §4.1 — damage_seg + parts_seg YOLOv8 per-epoch curves (box + mask loss, mAP50, mAP50-95, P/R) loaded from each run's ultralytics/results.csv. - §11.2 — Variant D end-to-end MAE on a ha…4176ea4
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Initial commit: end-to-end car damage + repair-cost predictor
Variants A and B trained end-to-end on CarDD + comprehensive + iaai + Stanford Cars datasets, glued together by a versioned parts-cost catalog and a three-tier (exact / nearest-class / catalog-only) cost estimator with full provenance on every prediction. Production metrics (v0.1.0): - Identifier (ResNet50 + RandAugment + MixUp + CutMix): val acc 77.0% - Damage classifier (ResNet50, 6 damage types): val macro-F1 0.834 - YOLOv8n detector: mAP50 0.687, mAP50-95 0.540 - XGBoost(A) (image features only): val R² 0.630, test R² 0.642 - XGBoost(B) (+ bbox features): val R² 0.716, test R² 0.736 64 unit tests pass. Honest disclosures of synthetic-cost limitation in PLAN.md and progress/*.md.4ce7a7e