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added Country211 and Rendered SST2 dataset info
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# The Country211 Dataset | ||
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In the paper, we used an image classification dataset called Country211, to evaluate the model's capability on geolocation. To do so, we filtered the YFCC100m dataset that have GPS coordinate corresponding to a [ISO-3166 country code](https://en.wikipedia.org/wiki/List_of_ISO_3166_country_codes) and created a balanced dataset by sampling 150 train images, 50 validation images, and 100 test images images for each country. | ||
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The following command will download an 11GB archive countaining the images and extract into a subdirectory `country211`: | ||
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```bash | ||
wget https://openaipublic.azureedge.net/clip/data/country211.tgz | ||
tar zxvf country211.tgz | ||
``` | ||
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These images are a subset of the YFCC100m dataset. Use of the underlying media files is subject to the Creative Commons licenses chosen by their creators/uploaders. For more information about the YFCC100M dataset, visit [the official website](https://multimediacommons.wordpress.com/yfcc100m-core-dataset/). |
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# The Rendered SST2 Dataset | ||
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In the paper, we used an image classification dataset called Rendered SST2, to evaluate the model's capability on optical character recognition. To do so, we rendered the sentences in the [Standford Sentiment Treebank v2](https://nlp.stanford.edu/sentiment/treebank.html) dataset and used those as the input to the CLIP image encoder. | ||
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The following command will download a 131MB archive countaining the images and extract into a subdirectory `rendered-sst2`: | ||
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```bash | ||
wget https://openaipublic.azureedge.net/clip/data/rendered-sst2.tgz | ||
tar zxvf rendered-sst2.tgz | ||
``` | ||
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