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# Dataset Card for STL-10 |
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<!-- Provide a quick summary of the dataset. --> |
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## Dataset Details |
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### Dataset Description |
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<!-- Provide a longer summary of what this dataset is. --> |
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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. |
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### Dataset Sources |
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<!-- Provide the basic links for the dataset. --> |
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- **Homepage:** https://cs.stanford.edu/~acoates/stl10/ |
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- **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. |
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## Dataset Structure |
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<!-- 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. --> |
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#### Labeled |
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Total images: 13,000 |
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Classes: 10 categories (airplane, bird, car, cat, deer, dog, horse, monkey, ship, truck) |
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Splits: |
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- **Train:** 5,000 images |
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- **Test:** 8,000 images |
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Image specs: 96x96 pixels, RGB |
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#### Unlabeled |
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Total images: 100,000 |
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Classes: all labels are -1 |
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## Example Usage |
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Below is a quick example of how to load this dataset via the Hugging Face Datasets library. |
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``` |
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from datasets import load_dataset |
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# Load the dataset |
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dataset = load_dataset("randall-lab/stl10", split="train", trust_remote_code=True) |
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# dataset = load_dataset("randall-lab/stl10", split="test", trust_remote_code=True) |
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# dataset = load_dataset("randall-lab/stl10", split="unlabeled", trust_remote_code=True) |
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# Access a sample from the dataset |
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example = dataset[0] |
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image = example["image"] |
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label = example["label"] |
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image.show() # Display the image |
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print(f"Label: {label}") |
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``` |
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## Citation |
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<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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@inproceedings{coates2011analysis, |
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title={An analysis of single-layer networks in unsupervised feature learning}, |
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author={Coates, Adam and Ng, Andrew and Lee, Honglak}, |
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booktitle={Proceedings of the fourteenth international conference on artificial intelligence and statistics}, |
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pages={215--223}, |
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year={2011}, |
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organization={JMLR Workshop and Conference Proceedings} |
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} |
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