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--- |
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dataset_info: |
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features: |
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- name: image |
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dtype: image |
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splits: |
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- name: train |
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num_bytes: 4450242498.020249 |
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num_examples: 287968 |
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- name: test |
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num_bytes: 234247797.33875093 |
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num_examples: 15157 |
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download_size: 4756942293 |
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dataset_size: 4684490295.359 |
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license: mit |
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--- |
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# Dataset Card for "lsun-bedrooms" |
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This is a 20% sample of the bedrooms category in [`LSUN`](https://github.com/fyu/lsun), uploaded as a dataset for convenience. |
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The license for _this compilation only_ is MIT. The data retains the same license as the original dataset. |
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This is (roughly) the code that was used to upload this dataset: |
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```Python |
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import os |
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import shutil |
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from miniai.imports import * |
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from miniai.diffusion import * |
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from datasets import load_dataset |
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path_data = Path('data') |
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path_data.mkdir(exist_ok=True) |
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path = path_data/'bedroom' |
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url = 'https://s3.amazonaws.com/fast-ai-imageclas/bedroom.tgz' |
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if not path.exists(): |
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path_zip = fc.urlsave(url, path_data) |
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shutil.unpack_archive('data/bedroom.tgz', 'data') |
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dataset = load_dataset("imagefolder", data_dir="data/bedroom") |
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dataset = dataset.remove_columns('label') |
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dataset = dataset['train'].train_test_split(test_size=0.05) |
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dataset.push_to_hub("pcuenq/lsun-bedrooms") |
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``` |
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