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imagenet2012_corrupted

Imagenet2012Corrupted is a dataset generated by adding common corruptions to the validation images in the ImageNet dataset. In the original paper, there are 15 different corruptions, and each has 5 levels of severity. In this dataset, we implement 12 out of the 15 corruptions, including Gaussian noise, shot noise, impulse_noise, defocus blur, frosted glass blur, zoom blur, fog, brightness, contrast, elastic, pixelate, and jpeg compression. The randomness is fixed so that regeneration is deterministic.

imagenet2012_corrupted is configured with tfds.image.imagenet2012_corrupted.Imagenet2012CorruptedConfig and has the following configurations predefined (defaults to the first one):

  • gaussian_noise_1 (v0.0.1) (Size: ?? GiB): corruption type = gaussian_noise, severity = 1

  • gaussian_noise_2 (v0.0.1) (Size: ?? GiB): corruption type = gaussian_noise, severity = 2

  • gaussian_noise_3 (v0.0.1) (Size: ?? GiB): corruption type = gaussian_noise, severity = 3

  • gaussian_noise_4 (v0.0.1) (Size: ?? GiB): corruption type = gaussian_noise, severity = 4

  • gaussian_noise_5 (v0.0.1) (Size: ?? GiB): corruption type = gaussian_noise, severity = 5

  • shot_noise_1 (v0.0.1) (Size: ?? GiB): corruption type = shot_noise, severity = 1

  • shot_noise_2 (v0.0.1) (Size: ?? GiB): corruption type = shot_noise, severity = 2

  • shot_noise_3 (v0.0.1) (Size: ?? GiB): corruption type = shot_noise, severity = 3

  • shot_noise_4 (v0.0.1) (Size: ?? GiB): corruption type = shot_noise, severity = 4

  • shot_noise_5 (v0.0.1) (Size: ?? GiB): corruption type = shot_noise, severity = 5

  • impulse_noise_1 (v0.0.1) (Size: ?? GiB): corruption type = impulse_noise, severity = 1

  • impulse_noise_2 (v0.0.1) (Size: ?? GiB): corruption type = impulse_noise, severity = 2

  • impulse_noise_3 (v0.0.1) (Size: ?? GiB): corruption type = impulse_noise, severity = 3

  • impulse_noise_4 (v0.0.1) (Size: ?? GiB): corruption type = impulse_noise, severity = 4

  • impulse_noise_5 (v0.0.1) (Size: ?? GiB): corruption type = impulse_noise, severity = 5

  • defocus_blur_1 (v0.0.1) (Size: ?? GiB): corruption type = defocus_blur, severity = 1

  • defocus_blur_2 (v0.0.1) (Size: ?? GiB): corruption type = defocus_blur, severity = 2

  • defocus_blur_3 (v0.0.1) (Size: ?? GiB): corruption type = defocus_blur, severity = 3

  • defocus_blur_4 (v0.0.1) (Size: ?? GiB): corruption type = defocus_blur, severity = 4

  • defocus_blur_5 (v0.0.1) (Size: ?? GiB): corruption type = defocus_blur, severity = 5

  • frosted_glass_blur_1 (v0.0.1) (Size: ?? GiB): corruption type = frosted_glass_blur, severity = 1

  • frosted_glass_blur_2 (v0.0.1) (Size: ?? GiB): corruption type = frosted_glass_blur, severity = 2

  • frosted_glass_blur_3 (v0.0.1) (Size: ?? GiB): corruption type = frosted_glass_blur, severity = 3

  • frosted_glass_blur_4 (v0.0.1) (Size: ?? GiB): corruption type = frosted_glass_blur, severity = 4

  • frosted_glass_blur_5 (v0.0.1) (Size: ?? GiB): corruption type = frosted_glass_blur, severity = 5

  • zoom_blur_1 (v0.0.1) (Size: ?? GiB): corruption type = zoom_blur, severity = 1

  • zoom_blur_2 (v0.0.1) (Size: ?? GiB): corruption type = zoom_blur, severity = 2

  • zoom_blur_3 (v0.0.1) (Size: ?? GiB): corruption type = zoom_blur, severity = 3

  • zoom_blur_4 (v0.0.1) (Size: ?? GiB): corruption type = zoom_blur, severity = 4

  • zoom_blur_5 (v0.0.1) (Size: ?? GiB): corruption type = zoom_blur, severity = 5

  • fog_1 (v0.0.1) (Size: ?? GiB): corruption type = fog, severity = 1

  • fog_2 (v0.0.1) (Size: ?? GiB): corruption type = fog, severity = 2

  • fog_3 (v0.0.1) (Size: ?? GiB): corruption type = fog, severity = 3

  • fog_4 (v0.0.1) (Size: ?? GiB): corruption type = fog, severity = 4

  • fog_5 (v0.0.1) (Size: ?? GiB): corruption type = fog, severity = 5

  • brightness_1 (v0.0.1) (Size: ?? GiB): corruption type = brightness, severity = 1

  • brightness_2 (v0.0.1) (Size: ?? GiB): corruption type = brightness, severity = 2

  • brightness_3 (v0.0.1) (Size: ?? GiB): corruption type = brightness, severity = 3

  • brightness_4 (v0.0.1) (Size: ?? GiB): corruption type = brightness, severity = 4

  • brightness_5 (v0.0.1) (Size: ?? GiB): corruption type = brightness, severity = 5

  • contrast_1 (v0.0.1) (Size: ?? GiB): corruption type = contrast, severity = 1

  • contrast_2 (v0.0.1) (Size: ?? GiB): corruption type = contrast, severity = 2

  • contrast_3 (v0.0.1) (Size: ?? GiB): corruption type = contrast, severity = 3

  • contrast_4 (v0.0.1) (Size: ?? GiB): corruption type = contrast, severity = 4

  • contrast_5 (v0.0.1) (Size: ?? GiB): corruption type = contrast, severity = 5

  • elastic_1 (v0.0.1) (Size: ?? GiB): corruption type = elastic, severity = 1

  • elastic_2 (v0.0.1) (Size: ?? GiB): corruption type = elastic, severity = 2

  • elastic_3 (v0.0.1) (Size: ?? GiB): corruption type = elastic, severity = 3

  • elastic_4 (v0.0.1) (Size: ?? GiB): corruption type = elastic, severity = 4

  • elastic_5 (v0.0.1) (Size: ?? GiB): corruption type = elastic, severity = 5

  • pixelate_1 (v0.0.1) (Size: ?? GiB): corruption type = pixelate, severity = 1

  • pixelate_2 (v0.0.1) (Size: ?? GiB): corruption type = pixelate, severity = 2

  • pixelate_3 (v0.0.1) (Size: ?? GiB): corruption type = pixelate, severity = 3

  • pixelate_4 (v0.0.1) (Size: ?? GiB): corruption type = pixelate, severity = 4

  • pixelate_5 (v0.0.1) (Size: ?? GiB): corruption type = pixelate, severity = 5

  • jpeg_compression_1 (v0.0.1) (Size: ?? GiB): corruption type = jpeg_compression, severity = 1

  • jpeg_compression_2 (v0.0.1) (Size: ?? GiB): corruption type = jpeg_compression, severity = 2

  • jpeg_compression_3 (v0.0.1) (Size: ?? GiB): corruption type = jpeg_compression, severity = 3

  • jpeg_compression_4 (v0.0.1) (Size: ?? GiB): corruption type = jpeg_compression, severity = 4

  • jpeg_compression_5 (v0.0.1) (Size: ?? GiB): corruption type = jpeg_compression, severity = 5

imagenet2012_corrupted/gaussian_noise_1

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/gaussian_noise_2

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/gaussian_noise_3

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/gaussian_noise_4

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/gaussian_noise_5

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/shot_noise_1

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/shot_noise_2

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/shot_noise_3

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/shot_noise_4

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/shot_noise_5

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/impulse_noise_1

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/impulse_noise_2

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/impulse_noise_3

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/impulse_noise_4

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/impulse_noise_5

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/defocus_blur_1

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/defocus_blur_2

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/defocus_blur_3

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/defocus_blur_4

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/defocus_blur_5

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/frosted_glass_blur_1

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/frosted_glass_blur_2

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/frosted_glass_blur_3

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/frosted_glass_blur_4

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/frosted_glass_blur_5

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/zoom_blur_1

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/zoom_blur_2

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/zoom_blur_3

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/zoom_blur_4

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/zoom_blur_5

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/fog_1

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/fog_2

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/fog_3

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/fog_4

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/fog_5

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/brightness_1

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/brightness_2

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/brightness_3

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/brightness_4

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/brightness_5

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/contrast_1

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/contrast_2

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/contrast_3

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/contrast_4

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/contrast_5

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/elastic_1

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/elastic_2

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/elastic_3

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/elastic_4

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/elastic_5

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/pixelate_1

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/pixelate_2

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/pixelate_3

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/pixelate_4

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/pixelate_5

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/jpeg_compression_1

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/jpeg_compression_2

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/jpeg_compression_3

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/jpeg_compression_4

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

imagenet2012_corrupted/jpeg_compression_5

FeaturesDict({
    'file_name': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
})

Statistics

Split Examples
VALIDATION 50,000
ALL 50,000

Urls

Supervised keys (for as_supervised=True)

(u'image', u'label')

Citation

@inproceedings{
  hendrycks2018benchmarking,
  title={Benchmarking Neural Network Robustness to Common Corruptions and Perturbations},
  author={Dan Hendrycks and Thomas Dietterich},
  booktitle={International Conference on Learning Representations},
  year={2019},
  url={https://openreview.net/forum?id=HJz6tiCqYm},
}