Transfer Learning with MobileNetV2 on CIFAR-10
This project demonstrates Transfer Learning using a pretrained MobileNetV2 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 MobileNetV2
- Replace the final classifier
- Freeze the backbone
- Train only the classifier
Fine-Tuning
- Load the best feature extraction model
- Unfreeze features[18] and the classifier
- Fine-tune using different learning rates
Results
Feature Extraction
Fine-Tuning
Technologies
- Python
- PyTorch
- TorchVision
- NumPy
- Matplotlib
- tqdm
Author
Ankit Bari
