How to create custom image Datasets and Dataloaders in PyTorch for training models
In this video I discuss about how to create custom image datasets and data loaders in the PyTorch framework for training models. Using two Kaggle image datasets I explain how you can create custom datasets for training models in PyTorch.
Code for processing data samples can get messy and hard to maintain; we ideally want our dataset code to be decoupled from our model training code for better readability and modularity. PyTorch provides two data primitives: torch.utils.data.DataLoader and torch.utils.data.Dataset that allow you to use pre-loaded datasets as well as your own data. Dataset stores the samples and their corresponding labels, and DataLoader wraps an iterable around the Dataset to enable easy access to the samples.
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