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Certainly! Loading a custom image dataset in PyTorch involves creating a custom dataset class, preprocessing the data, and using PyTorch's DataLoader to efficiently load the data in batches. Here's a step-by-step tutorial with code examples:
You'll create a custom dataset class that extends PyTorch's Dataset class. This class will handle loading your custom image dataset.
Replace extract_label_from_filename_or_path() with your logic to extract labels if your dataset has labels encoded in the filenames or directory structure.
Transformations are applied to the images to preprocess them before feeding them into the model. Common transformations include resizing, normalization, and converting to tensors.
Instantiate your custom dataset by providing the path to your dataset directory and the defined transformations.
Now, use PyTorch's DataLoader to efficiently load the data in batches.
Finally, iterate through the DataLoader to access batches of images and process them as needed.
Make sure to replace the comments with your specific operations, such as training a model or performing any image processing tasks.
That's it! This tutorial outlines the steps to create a custom image dataset loader using PyTorch, enabling you to preprocess and load your own image data efficiently. Adjust the transformations and dataset structure according to the specifics of your dataset and task.
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