cifar10_h

• Description:

A re-labeled version of CIFAR-10's test set with soft-labels coming from real human annotators. For every pair (image, label) in the original CIFAR-10 test set, it provides several additional labels given by real human annotators as well as the average soft-label. The training set is identical to the one of the original dataset.

• Homepage: https://github.com/jcpeterson/cifar-10h

• Source code: tfds.image_classification.cifar10_h.Cifar10H

• Versions:

  • 1.0.0 (default): Initial release.

• Download size: 172.92 MiB

• Dataset size: 144.85 MiB

• Auto-cached (documentation): Yes

• Splits:

Split	Examples
'test'	10,000
'train'	50,000

• Feature structure:

FeaturesDict({
    'annotator_ids': Sequence(Scalar(shape=(), dtype=int32)),
    'human_labels': Sequence(ClassLabel(shape=(), dtype=int64, num_classes=10)),
    'id': Text(shape=(), dtype=string),
    'image': Image(shape=(32, 32, 3), dtype=uint8),
    'label': ClassLabel(shape=(), dtype=int64, num_classes=10),
    'reaction_times': Sequence(Scalar(shape=(), dtype=float32)),
    'soft_label': Tensor(shape=(10,), dtype=float32),
    'trial_indices': Sequence(Scalar(shape=(), dtype=int32)),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
annotator_ids	Sequence(Scalar)	(None,)	int32
human_labels	Sequence(ClassLabel)	(None,)	int64
id	Text		string
image	Image	(32, 32, 3)	uint8
label	ClassLabel		int64
reaction_times	Sequence(Scalar)	(None,)	float32
soft_label	Tensor	(10,)	float32
trial_indices	Sequence(Scalar)	(None,)	int32

• Supervised keys (See as_supervised doc): None

• Figure (tfds.show_examples):

• Examples (tfds.as_dataframe):

• Citation:

@inproceedings{wei2022learning,
  title={Human uncertainty makes classification more robust},
  author={Joshua C. Peterson and Ruairidh M. Battleday and Thomas L. Griffiths
  and Olga Russakovsky},
  booktitle={IEEE International Conference on Computer Vision and Pattern
  Recognition (CVPR)},
  year={2019}
}