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Browse files- 100C/README.txt +14 -0
- 100C/brightness.npy +3 -0
- 100C/contrast.npy +3 -0
- 100C/defocus_blur.npy +3 -0
- 100C/elastic_transform.npy +3 -0
- 100C/fog.npy +3 -0
- 100C/frost.npy +3 -0
- 100C/gaussian_blur.npy +3 -0
- 100C/gaussian_noise.npy +3 -0
- 100C/glass_blur.npy +3 -0
- 100C/impulse_noise.npy +3 -0
- 100C/jpeg_compression.npy +3 -0
- 100C/labels.npy +3 -0
- 100C/motion_blur.npy +3 -0
- 100C/pixelate.npy +3 -0
- 100C/saturate.npy +3 -0
- 100C/shot_noise.npy +3 -0
- 100C/snow.npy +3 -0
- 100C/spatter.npy +3 -0
- 100C/speckle_noise.npy +3 -0
- 100C/zoom_blur.npy +3 -0
100C/README.txt
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CIFAR-C Dataset README
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The first 10,000 images in each .npy are the test set images corrupted at severity 1,
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and the last 10,000 images are the test set images corrupted at severity five.
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labels.npy is the label file for all other image files.
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@article{hendrycks2019robustness,
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title={Benchmarking Neural Network Robustness to Common Corruptions and Perturbations},
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author={Dan Hendrycks and Thomas Dietterich},
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journal={Proceedings of the International Conference on Learning Representations},
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year={2019}
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}
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100C/brightness.npy
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100C/contrast.npy
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100C/defocus_blur.npy
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100C/elastic_transform.npy
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100C/fog.npy
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100C/frost.npy
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100C/gaussian_blur.npy
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100C/gaussian_noise.npy
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100C/glass_blur.npy
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100C/impulse_noise.npy
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100C/jpeg_compression.npy
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100C/labels.npy
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100C/motion_blur.npy
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100C/pixelate.npy
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100C/saturate.npy
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100C/shot_noise.npy
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100C/snow.npy
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100C/spatter.npy
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100C/speckle_noise.npy
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100C/zoom_blur.npy
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