What is transfer learning in PyTorch?

What is transfer learning in PyTorch?

Transfer learning is a technique where a model developed for a specific task is reused as the starting point for a model on a second task.

How do I implement transfer learning in PyTorch?

You can implement transfer learning in PyTorch by using pre-trained models from torchvision and fine-tuning them on your dataset.

Can you give me an example of code for transfer learning?

Sure! Here's a simple example using ResNet18:

import torch
import torchvision.models as models

model = models.resnet18(pretrained=True)
model.fc = torch.nn.Linear(model.fc.in_features, num_classes)

# Now you can train the model on your dataset.

What are the benefits of using transfer learning?

The benefits include reduced training time, improved performance on small datasets, and leveraging knowledge from large datasets.