TinyImageNetC

class torch_uncertainty.datasets.classification.TinyImageNetC(root, transform=None, target_transform=None, subset='all', shift_severity=1, download=False)

The corrupted TinyImageNet-C Dataset.

Parameters:

• root (str) – Root directory of the datasets.

• transform (callable, optional) – A function/transform that takes in a PIL image and returns a transformed version. E.g, transforms.RandomCrop. Defaults to None.

• target_transform (callable, optional) – A function/transform that takes in the target and transforms it. Defaults to None.

• subset (str) – The subset to use, one of all or the keys in cifarc_subsets.

• shift_severity (int) – The shift_severity of the corruption, between 1 and 5.

• download (bool, optional) – If True, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again. Defaults to False.

References

Benchmarking neural network robustness to common corruptions and

perturbations. Dan Hendrycks and Thomas Dietterich. In ICLR, 2019.

download()

Download the dataset.