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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} }