Steps in Transfer Learning for Computer Vision
- Select a Pre-trained Model: Choose a model pre-trained on a large dataset. Common choices include ResNet, VGG, and Inception due to their proven performance and availability in popular deep-learning libraries.
- Modify the Model: Replace the final classification layer of the pre-trained model with one that matches the number of classes in the target task. This often involves adding new fully connected layers followed by a softmax or sigmoid activation function.
- Freeze Layers: Optionally freeze the weights of the earlier layers to retain their learned features. This helps in leveraging the general patterns and structures learned from the large dataset.
- Train the Model: Train the modified model on the target dataset. This involves fine-tuning the new layers and possibly the later layers of the pre-trained model. Fine-tuning is typically done with a lower learning rate to avoid drastic changes to the pre-trained weights.
Transfer Learning for Computer Vision
Transfer learning is a powerful technique in the field of computer vision, where a pre-trained model on a large dataset is fine-tuned for a different but related task. This approach leverages the knowledge gained from the initial training to improve performance and reduce training time for the new task. Here’s an overview of transfer learning for computer vision: