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license: mit
tags:
- image-classification
- computer-vision
- defect-detection
- automotive
- pytorch
- timm
- efficientnet
language:
- ru
pipeline_tag: image-classification
---
# 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.
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