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README.md

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+---
+license: mit
+tags:
+  - computer-vision
+  - image-classification
+  - pytorch
+  - insurance
+  - claims-triage
+  - grad-cam
+pipeline_tag: image-classification
+---
+
+# ClaimSight — Vehicle Damage Triage Classifier (resnet50)
+
+Binary image classifier that flags whether a submitted vehicle photo
+shows visible damage (`00-damage`) or an intact vehicle (`01-whole`),
+built as a **claims-triage decision-support tool** — not an autonomous
+adjuster. Every flagged prediction is intended to route to a human
+reviewer.
+
+Full project, training code, and API: https://github.com/<your-username>/claimsight
+
+## Intended use
+
+- First-pass triage of policyholder-submitted claim photos, to route
+  obviously-intact vehicles away from a manual review queue.
+- **Not** a final claims-adjudication system. **Not** a repair-cost
+  estimator. Every prediction should be reviewed by a human before any
+  claim decision is made.
+
+## How to use
+
+```python
+import torch
+from huggingface_hub import hf_hub_download
+
+# clone github.com/<your-username>/claimsight for src/model.py, src/preprocessing.py
+from src.model import build_model
+from src.preprocessing import preprocess
+from src.dataset import eval_transform
+
+checkpoint_path = hf_hub_download(
+    repo_id="<your-username>/claimsight-damage-detection",
+    filename="best_resnet50.pt",
+)
+model = build_model("resnet50", pretrained=False)
+model.load_state_dict(torch.load(checkpoint_path, map_location="cpu"))
+model.eval()
+
+import cv2
+image_bgr = cv2.imread("claim_photo.jpg")
+image_rgb = preprocess(image_bgr)                      # same function used in training
+tensor = eval_transform(image=image_rgb)["image"].unsqueeze(0)
+probs = torch.softmax(model(tensor), dim=1).squeeze()
+print({"00-damage": probs[1].item(), "01-whole": probs[0].item()})
+```
+
+## Training data
+
+Car Damage Detection (Kaggle, `anujms/car-damage-detection`) — 2,300
+images, binary folder labels only (no masks/bounding boxes), split
+1,840 train / 460 validation, balanced within each split.
+
+## Architecture & training
+
+Transfer learning (resnet50, ImageNet-pretrained) in two phases: frozen
+backbone with a fresh head first, then fine-tuning of the last block at
+a reduced learning rate. `ReduceLROnPlateau` + early stopping on
+validation loss. Full details, augmentation policy and reproducibility
+notes: see the project README.
+
+## Evaluation (real, on the held-out validation split)
+
+| Metric | Value |
+|---|---|
+| Validation accuracy | 0.9435 |
+| ROC-AUC (damage class) | 0.9858 |
+| Recall — damage class (@ threshold 0.5) | 0.9565 |
+| Precision — damage class (@ threshold 0.5) | 0.9322 |
+| Confusion matrix (TN/FP/FN/TP) | 214/16/10/220 |
+
+Recall on the `damage` class is the priority metric: a false negative
+(damaged vehicle classified as intact) can wrongly close a legitimate
+claim, while a false positive only costs one extra human review. A
+recall-priority operating point was chosen by sweeping the decision
+threshold: at threshold **0.25**, damage recall is
+**0.9870** at precision **0.9080**
+(vs. 0.9565 recall / 0.9322 precision at the default 0.5).
+
+| Architecture | Val. accuracy | ROC-AUC |
+|---|---|---|
+| **resnet50** (this checkpoint) | 0.9435 | 0.9858 |
+| efficientnet_b0 | 0.8870 | 0.9620 |
+
+
+## Explainability
+
+Grad-CAM (`pytorch-grad-cam`) on the last convolutional layer is used
+as a **weak-localization** signal — a coarse heatmap of the region that
+most influenced the decision. It is **not** pixel-level segmentation:
+there is no mask ground truth in this dataset, so there is no IoU/Dice.
+See the project repo's `outputs/gradcam/` for overlays on both correct
+and incorrect predictions, including a documented shortcut-learning
+check.
+
+## Limitations
+
+- Binary output only — no severity or damaged-part classification.
+- Grad-CAM is weak localization, not segmentation.
+- Modest dataset size (2,300 images, one source) — real domain-shift
+  risk against a real insurer's photo distribution (different brands,
+  angles, lighting, phone cameras).
+- Not validated as a repair-cost estimator — triage signal only.
+- Requires human review in every deployment path.
+
+## Reproducibility
+
+Fixed seed (`torch.manual_seed(42)`), deterministic cuDNN settings,
+pinned `requirements.txt`. Metadata for this exact run:
+
+```json
+{
+  "arch": "resnet50",
+  "seed": 42,
+  "device": "cuda",
+  "phase1_epochs_ran": 8,
+  "phase2_epochs_ran": 15,
+  "best_val_loss": 0.14994667431582576,
+  "checkpoint": "models\\best_resnet50.pt",
+  "torch_version": "2.6.0+cu124",
+  "trained_at_utc": "2026-08-01T22:51:16Z"
+}
+```