Unsupervised Data set reading

Is there any definite way in PyTorch for loading only images from a folder without any labels or ground truths as in a supervised classification task?

I hope this helps, http://pytorch.org/docs/master/torchvision/datasets.html#imagefolder

Thanks. But I was asking how to load images in a folder into batches without any target (or labels) as in a supervised task.

Just like in autoencoders there are no image labels as such.


Have a look here to see how a generic Dataset can be implemented:

In particular, the __getitiem__ method, which returns a tuple comprising (data, label)

The generic loop is something like:

for (data, labels) in dataloader:
    # train / eval code

You’re free to ignore the label here and you can train an autoencoder on cifar10, for example, pretty much out of the box.

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