stl10

• Description:

The STL-10 dataset is an image recognition dataset for developing unsupervised feature learning, deep learning, self-taught learning algorithms. It is inspired by the CIFAR-10 dataset but with some modifications. In particular, each class has fewer labeled training examples than in CIFAR-10, but a very large set of unlabeled examples is provided to learn image models prior to supervised training. The primary challenge is to make use of the unlabeled data (which comes from a similar but different distribution from the labeled data) to build a useful prior. All images were acquired from labeled examples on ImageNet.

• Additional Documentation: Explore on Papers With Code

• Homepage: http://ai.stanford.edu/~acoates/stl10/

• Source code: tfds.datasets.stl10.Builder

• Versions:

  • 1.0.0 (default): No release notes.

• Download size: 2.46 GiB

• Dataset size: 1.86 GiB

• Auto-cached (documentation): No

• Splits:

Split	Examples
'test'	8,000
'train'	5,000
'unlabelled'	100,000

• Feature structure:

FeaturesDict({
    'image': Image(shape=(96, 96, 3), dtype=uint8),
    'label': ClassLabel(shape=(), dtype=int64, num_classes=10),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
image	Image	(96, 96, 3)	uint8
label	ClassLabel		int64

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

• Figure (tfds.show_examples):

• Examples (tfds.as_dataframe):

• Citation:

@inproceedings{coates2011stl10,
  title={ {An Analysis of Single Layer Networks in Unsupervised Feature Learning} },
  author={Coates, Adam and Ng, Andrew and Lee, Honglak},
  booktitle={AISTATS},
  year={2011},
  note = {\url{https://cs.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf} },
}