This repository contains the ImageNet-C dataset from Benchmarking Neural Network Robustness to Common Corruptions and Perturbations. noise.tar (21GB) contains gaussian_noise, shot_noise, and impulse_noise. blur.tar (7GB) contains defocus_blur, glass_blur, motion_blur, and zoom_blur. weather.tar (12GB) contains frost, snow, fog, and brightness. digital.tar (7GB) contains contrast, elastic_transform, pixelate, and jpeg_compression. extra.tar (15GB) contains speckle_noise, spatter, gaussian_blur, and saturate. AlexNet Errors used to normalize the Corruption Errors are as follows, Gaussian Noise: 88.6%, Shot Noise: 89.4%, Impulse Noise: 92.3%, Defocus Blur: 82.0%, Glass Blur: 82.6%, Motion Blur: 78.6%, Zoom Blur: 79.8%, Snow: 86.7%, Frost: 82.7%, Fog: 81.9%, Brightness: 56.5%, Contrast: 85.3%, Elastic Transformation: 64.6%, Pixelate: 71.8%, JPEG: 60.7%, Speckle Noise: 84.5%, Gaussian Blur: 78.7%, Spatter: 71.8%, Saturate: 65.8% If you find this useful in your research, please consider citing: @article{hendrycks2019robustness, title={Benchmarking Neural Network Robustness to Common Corruptions and Perturbations}, author={Hendrycks, Dan and Dietterich, Thomas}, journal={Proceedings of the International Conference on Learning Representations}, year={2019} }