How do you implement a custom data loader for large image datasets in PyTorch

Question

With the help of code, can you implement a custom data loader for large image datasets in PyTorch?

Answer 1

You can implement a custom data loader for large image datasets in PyTorch by referring to the code snippet below:

In the above code, we are using Custom Dataset to handle large datasets efficiently, override __len__ and __getitem__, and transforms, which uses torchvision. Transforms for resizing, normalization, etc, and DataLoader, which supports batch loading, shuffling, and parallelism (num_workers).

Hence, this approach efficiently handles large datasets in PyTorch.