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This repository contains the ImageNet-C dataset from Benchmarking Neural Network Robustness to Common Corruptions and Perturbations.
\n\n
\nnoise.tar (21GB) contains gaussian_noise, shot_noise, and impulse_noise.
\nblur.tar (7GB) contains defocus_blur, glass_blur, motion_blur, and zoom_blur.
\nweather.tar (12GB) contains frost, snow, fog, and brightness.
\ndigital.tar (7GB) contains contrast, elastic_transform, pixelate, and jpeg_compression.
\nextra.tar (15GB) contains speckle_noise, spatter, gaussian_blur, and saturate.
\n
\nAlexNet Errors used to normalize the Corruption Errors are as follows,
\nGaussian Noise: 88.6%,
\nShot Noise: 89.4%,
\nImpulse Noise: 92.3%,
\nDefocus Blur: 82.0%,
\nGlass Blur: 82.6%,
\nMotion Blur: 78.6%,
\nZoom Blur: 79.8%,
\nSnow: 86.7%,
\nFrost: 82.7%,
\nFog: 81.9%,
\nBrightness: 56.5%,
\nContrast: 85.3%,
\nElastic Transformation: 64.6%,
\nPixelate: 71.8%,
\nJPEG: 60.7%,
\nSpeckle Noise: 84.5%,
\nGaussian Blur: 78.7%,
\nSpatter: 71.8%,
\nSaturate: 65.8%
\n\n
If you find this useful in your research, please consider citing:
\n\n @article{hendrycks2019robustness,
\n title={Benchmarking Neural Network Robustness to Common Corruptions and Perturbations},
\n author={Hendrycks, Dan and Dietterich, Thomas},
\n journal={Proceedings of the International Conference on Learning Representations},
\n year={2019}
\n }