What is model fine-tuning in PyTorch?

Fine-tuning a PyTorch model refers to adjusting the model parameters through minimal training on a new dataset based on a pre-trained model, in order to adapt the model to specific tasks of the new dataset. Typically, a pre-trained model on a large-scale dataset is used as the initial model, then fine-tuned on our own dataset to better fit the new tasks, which often accelerates the training process and improves the model performance.