imagenet_a

• Visualization: Explore in Know Your Data north_east

• Description:

ImageNet-A is a set of images labelled with ImageNet labels that were obtained by collecting new data and keeping only those images that ResNet-50 models fail to correctly classify. For more details please refer to the paper.

The label space is the same as that of ImageNet2012. Each example is represented as a dictionary with the following keys:

• 'image': The image, a (H, W, 3)-tensor.
• 'label': An integer in the range [0, 1000).

• 'file_name': A unique sting identifying the example within the dataset.

• Homepage: https://github.com/hendrycks/natural-adv-examples

• Source code: tfds.image_classification.ImagenetA

• Versions:

  • 0.1.0 (default): No release notes.

• Download size: 655.70 MiB

• Dataset size: 650.87 MiB

• Auto-cached (documentation): No

• Splits:

Split	Examples
'test'	7,500

• Feature structure:

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),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
file_name	Text		tf.string
image	Image	(None, None, 3)	tf.uint8
label	ClassLabel		tf.int64

• Supervised keys (See as_supervised doc): ('image', 'label')

• Figure (tfds.show_examples):

• Examples (tfds.as_dataframe):

• Citation:

@article{hendrycks2019nae,
  title={Natural Adversarial Examples},
  author={Dan Hendrycks and Kevin Zhao and Steven Basart and Jacob Steinhardt and Dawn Song},
  journal={arXiv preprint arXiv:1907.07174},
  year={2019}
}