Steps to Implement Transfer Learning for Image Classification in PyTorch
Transfer learning for image classification is essentially reusing a pre-trained neural network to improve the result on a different dataset. Follow the steps to implement Transfer Learning for Image Classification.
- Choose a pre-trained model (ResNet, VGG, etc.) based on your task.
- Modify the model by potentially replacing the final classification layer to match the number of classes in your new dataset.
- Freeze the pre-trained layers (make their weights non-trainable) to prevent them from being updated during training on the new dataset. This is especially useful when you have a small dataset.
- Preprocess your data, including resizing images and normalization.
- Optionally, perform data augmentation to increase the size and diversity of your dataset.
- Define the new model architecture by adding the new classifier on top of the pre-trained model.
- Compile the model by specifying the loss function, optimizer, and metrics.
- Train the model on your new dataset. Freezing the pre-trained layers might require fewer training epochs compared to training from scratch.
- Fine-tuning: You can further train the model by unfreezing some or all of the pre-trained layers.
- Evaluate the model’s performance on a validation or test dataset to assess its accuracy and generalization capabilities.