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//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

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:

{
  "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"
}

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