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stl10
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---
{}
---

# Dataset Card for STL-10

<!-- Provide a quick summary of the dataset. -->

## Dataset Details

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->
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. 

### Dataset Sources

<!-- Provide the basic links for the dataset. -->

- **Homepage:** https://cs.stanford.edu/~acoates/stl10/
- **Paper:** Coates, A., Ng, A., & Lee, H. (2011, June). An analysis of single-layer networks in unsupervised feature learning. In Proceedings of the fourteenth international conference on artificial intelligence and statistics (pp. 215-223). JMLR Workshop and Conference Proceedings.

## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

#### Labeled
Total images: 13,000

Classes: 10 categories (airplane, bird, car, cat, deer, dog, horse, monkey, ship, truck)

Splits:

- **Train:** 5,000 images

- **Test:** 8,000 images

Image specs: 96x96 pixels, RGB

#### Unlabeled
Total images: 100,000

Classes: all labels are -1

## Example Usage
Below is a quick example of how to load this dataset via the Hugging Face Datasets library.
```
from datasets import load_dataset  

# Load the dataset  
dataset = load_dataset("randall-lab/stl10", split="train", trust_remote_code=True)  
# dataset = load_dataset("randall-lab/stl10", split="test", trust_remote_code=True)
# dataset = load_dataset("randall-lab/stl10", split="unlabeled", trust_remote_code=True)  

# Access a sample from the dataset  
example = dataset[0]  
image = example["image"]  
label = example["label"]  

image.show()  # Display the image  
print(f"Label: {label}")
```

## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

@inproceedings{coates2011analysis,
  title={An analysis of single-layer networks in unsupervised feature learning},
  author={Coates, Adam and Ng, Andrew and Lee, Honglak},
  booktitle={Proceedings of the fourteenth international conference on artificial intelligence and statistics},
  pages={215--223},
  year={2011},
  organization={JMLR Workshop and Conference Proceedings}
}