randall-lab/stl10

Dataset Card for STL-10

Dataset Details

Dataset 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.

Dataset Sources

• 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

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

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} }