What are the steps for fine-tuning a model in PyTorch?

The general steps for fine-tuning a model in PyTorch are as follows:

1. Load pre-trained model: Begin by loading a pre-trained model trained on a large-scale dataset, usually using some commonly used pre-trained models provided in torchvision.models, such as ResNet, VGG, and AlexNet.
2. Adapt the model structure: Adjust the pre-trained model by modifying the last fully connected layer to suit the new task requirements, such as classification, object detection, etc.
3. Freeze model parameters: Fix the parameters of the pre-trained model by setting requires_grad=False to prevent them from being updated during fine-tuning.
4. Define the loss function and optimizer based on the task requirements, such as using cross-entropy loss function and stochastic gradient descent optimizer.
5. Train the model: input the newly defined model into the training dataset, conduct model training, calculate gradients through backpropagation, and update model parameters.
6. Adjusting the learning rate: During the fine-tuning process, it is common to gradually decrease the learning rate to help the model converge better to the optimal solution.
7. Evaluate the model performance: Use either a validation or test set to assess the performance of the fine-tuned model, and make adjustments and optimizations based on the evaluation results.
8. Fine-tuning completed: Once the model’s performance reaches a satisfactory level, the fine-tuning process is complete, and the fine-tuned model can be used for prediction and application.