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.