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imagenet_v2

Description:

ImageNet-v2 is an ImageNet test set (10 per class) collected by closely following the original labelling protocol. Each image has been labelled by at least 10 MTurk workers, possibly more, and depending on the strategy used to select which images to include among the 10 chosen for the given class there are three different versions of the dataset. Please refer to section four of the paper for more details on how the different variants were compiled.

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/modestyachts/ImageNetV2
Source code: tfds.datasets.imagenet_v2.Builder
Versions:
- 1.0.0: Initial version.
- 2.0.0: Files updated.
- 3.0.0 (default): Fix file_name, from absolute path to path relative to data directory, ie: "class_id/filename.jpg".
- 3.1.0: New URLs for resources from Hugging Face.
Auto-cached (documentation): No
Splits:

Split	Examples
`'test'`	10,000

Feature structure:

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

Feature documentation:

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

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

@inproceedings{recht2019imagenet,
  title={Do ImageNet Classifiers Generalize to ImageNet?},
  author={Recht, Benjamin and Roelofs, Rebecca and Schmidt, Ludwig and Shankar, Vaishaal},
  booktitle={International Conference on Machine Learning},
  pages={5389--5400},
  year={2019}
}