Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

This repository contains the datasets and some code for the paper Benchmarking Neural Network Robustness to Common Corruptions and Perturbations by Dan Hendrycks and Tom Dietterich.

Requires Python 3+ and PyTorch 0.3+.

ImageNet-C

Download Tiny ImageNet-C here.

Download ImageNet-C here.

ImageNet-P

_{^{ImageNet-P sequences are MP4s not GIFs. The spatter perturbation sequence is a validation sequence.}}

Download Tiny ImageNet-P here.

Download ImageNet-P here.

Citation

If you find this useful in your research, please consider citing:

@article{hendrycks2018robustness,
  title={Benchmarking Neural Network Robustness to Common Corruptions and Perturbations},
  author={Dan Hendrycks and Thomas Dietterich},
  journal={arXiv preprint arXiv:1807.01697},
  year={2018}
}

Part of the code was contributed by Tom Brown.

Icons-50 (From an Older Draft)

Download Icons-50 here or here.