# Transfer Learning with EfficientNet-B0 on CIFAR-10

This project demonstrates **Transfer Learning** using a pretrained **EfficientNet-B0** model on the **CIFAR-10** dataset with PyTorch.

Two transfer learning approaches are implemented:

- **Feature Extraction**
- **Fine-Tuning**

## Dataset

- **Dataset:** CIFAR-10
- **Training Images:** 45,000
- **Validation Images:** 5,000
- **Test Images:** 10,000
- **Classes:** 10

## Training Pipeline

### Feature Extraction

- Load pretrained EfficientNet-B0
- Replace the final classifier
- Freeze the backbone
- Train only the classifier

### Fine-Tuning

- Load the best feature extraction model
- Unfreeze features[8] and the classifier
- Fine-tune using different learning rates

## Results

### Feature Extraction

| Loss | Accuracy |
|------|----------|
| ![](feature_extraction_loss.png) | ![](feature_extraction_accuracy.png) |

### Fine-Tuning

| Loss | Accuracy |
|------|----------|
| ![](fine_tuning_loss.png) | ![](fine_tuning_accuracy.png) |

## Technologies

- Python
- PyTorch
- TorchVision
- NumPy
- Matplotlib
- tqdm

## Author

**Ankit Bari**