Transfer Learning for Computer Vision

Implement transfer learning for my computer vision project.

Project details:
- Task: [CLASSIFICATION/DETECTION/SEGMENTATION]
- Target domain: [DESCRIBE YOUR IMAGES]
- Dataset size: [NUMBER OF IMAGES]
- Classes: [NUMBER AND DESCRIPTION]

Transfer learning requirements:
1. Pretrained model selection:
   - ResNet, EfficientNet, ViT options
   - Model size vs. accuracy tradeoffs
2. Feature extraction approach:
   - Freeze backbone, train classifier
   - Intermediate layer features
3. Fine-tuning approach:
   - Gradual unfreezing
   - Layer-wise learning rates
4. Domain adaptation techniques:
   - Data augmentation for domain shift
   - Feature alignment methods
5. Training strategies:
   - Two-stage training
   - Warm restarts
6. Performance comparison:
   - Pretrained vs. scratch
   - Different backbone architectures
7. Model ensembling

Use [PyTorch/TensorFlow] with timm or TensorFlow Hub.