Fine-tuning is a more flexible transfer learning technique that involves unfreezing some or all of a pre-trained model’s layers and retraining them on a new dataset. Unlike feature extraction, which keeps most of the model unchanged, fine-tuning allows the model to adapt more specifically to a new task by updating weight parameters.
Fine-tuning is particularly beneficial when the target dataset differs significantly from the original dataset used for pre-training. By adjusting specific layers, fine-tuning enables the model to learn new feature representations while still leveraging its previously acquired knowledge.
Fine-Tuning Workflow
Here is a rundown of the general steps for performing fine-tuning.
- Select a Pre-Trained Model: Choose a suitable pre-trained model such as ResNet, ViT, or BLIP, depending on the task. As above, choose something close to your target.
- Load the Pre-Trained Model: Use a deep learning framework to load the model with pre-trained weights.
- Determine Layers to Unfreeze: Identify and selectively unfreeze layers that should be retrained based on the similarity between the original and new tasks.
- Modify the Output Layers: Adjust the model’s output layers to match the prediction target of the new dataset.
- Adjust Learning Rates: Apply a lower learning rate to pre-trained layers and a higher learning rate to newly initialized layers to prevent overwriting learned representations.
- Preprocess the Input Data: Ensure images are sized, normalized, and formatted to match model requirements.
- Train the Model: Perform training with backpropagation, monitoring validation loss to prevent overfitting.
- Evaluate and Optimize: Use performance metrics such as accuracy and F1 score, and apply hyperparameter tuning if needed.
