therealestcoder/paint_defect_detector

Paint Defect Detector

A binary image classifier that detects paint defects on car body panels using transfer learning with EfficientNetV2-S backbone (via imm).

Model Architecture

• Backbone: EfficientNetV2-S (pretrained, from imm)
• Head: Dropout → Linear(feat_dim, 256) → GELU → Dropout → Linear(256, 2)
• Task: Binary classification — clean vs defect

Training

• Optimizer: AdamW with cosine annealing LR scheduler
• Loss: CrossEntropyLoss with label smoothing
• Augmentations: Albumentations pipeline
• Metrics: AUC-ROC, F1, Accuracy

Inference

The project includes a FastAPI REST API (src/api.py) for serving predictions, and a Grad-CAM visualisation layer for model explainability.

Project Structure

src/ config.py # Hyperparameters and paths dataset.py # Dataset and data loaders model.py # DefectClassifier model train.py # Training loop infer.py # Inference utilities api.py # FastAPI inference server prepare_data.py # Data preparation script requirements.txt

Requirements

See equirements.txt. Key dependencies: orch, imm, lbumentations, astapi, grad-cam.