- 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} },
}