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# Dataset Card for Country211 |
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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 Country211 dataset is designed for country classification based on images. It was created to evaluate the geolocation capabilities of machine learning models. The dataset is a filtered subset of the YFCC100m dataset, consisting of images that have GPS coordinates corresponding to an ISO-3166 country code. The dataset is balanced, containing 150 training images, 50 validation images, and 100 test images for each of the 211 countries and territories. |
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### Dataset Sources |
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<!-- Provide the basic links for the dataset. --> |
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- **Homepage:** https://github.com/openai/CLIP/blob/main/data/country211.md |
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- **Paper:** Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., ... & Sutskever, I. (2021, July). Learning transferable visual models from natural language supervision. In International conference on machine learning (pp. 8748-8763). PmLR. |
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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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Each sample in the dataset contains: |
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- **image:** A variable-sized RGB image |
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- **label:** An integer between 0 and 210, representing the country |
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Total images: 211 * (150 + 50 + 100) = 63,300 |
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Classes: 211 (each corresponding to a country or territory) |
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Splits: |
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- **Train:** 150 images per country (31,650 total) |
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- **Validation:** 50 images per country (10,550 total) |
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- **Test:** 100 images per country (21,100 total) |
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Image specs: Variable sizes, RGB |
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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/country211", split="train", trust_remote_code=True) |
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# dataset = load_dataset("randall-lab/country211", split="validation", trust_remote_code=True) |
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# dataset = load_dataset("randall-lab/country211", split="test", 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{radford2021learning, |
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title={Learning transferable visual models from natural language supervision}, |
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author={Radford, Alec and Kim, Jong Wook and Hallacy, Chris and Ramesh, Aditya and Goh, Gabriel and Agarwal, Sandhini and Sastry, Girish and Askell, Amanda and Mishkin, Pamela and Clark, Jack and others}, |
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booktitle={International conference on machine learning}, |
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pages={8748--8763}, |
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year={2021}, |
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organization={PmLR} |
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} |
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