Hello Everyone,
Actually, I followed many Pytorch tutorials and I found that most of them speak about how to implement Custom Datasets and I am wondering why they are so important?
Especially in computer vision where All datasets follow certain organizational patterns and there are built-in functions to load the data easily (in torchvision).
So what makes me choose to implement a custom dataset?
Thank you in advance
While some public datasets follow a specific pattern (and thus can be loaded using a torchvision
dataset), a custom Dataset
gives you the ability to load your custom dataset using any logic you want. I.e. you don’t have to follow a specific folder structure, can apply transformations using different conditions, could create the target using a custom logic etc.
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