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  1. .gitattributes +3 -0
  2. README.md +11 -0
  3. a10k.z01 +3 -0
  4. a10k.z02 +3 -0
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  16. a10k.zip +3 -0
  17. split.txt +3 -0
.gitattributes CHANGED
@@ -53,3 +53,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.jpg filter=lfs diff=lfs merge=lfs -text
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  *.jpeg filter=lfs diff=lfs merge=lfs -text
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  *.webp filter=lfs diff=lfs merge=lfs -text
 
 
 
 
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  *.jpg filter=lfs diff=lfs merge=lfs -text
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  *.jpeg filter=lfs diff=lfs merge=lfs -text
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  *.webp filter=lfs diff=lfs merge=lfs -text
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+ split.txt filter=lfs diff=lfs merge=lfs -text
README.md ADDED
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+ # danbooru-artists-10k
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+
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+ ## Dataset Summary
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+ This dataset contains images from 10,000 artists collected from Danbooru. The images are primarily sourced from a mirror [here](https://huggingface.co/datasets/KBlueLeaf/danbooru2023-webp-4Mpixel). However, there are six images that are missing/broken in this mirror, which have been directly sourced from Danbooru. Each artist is represented by 30 images. This dataset is intended for use in metric learning or fine-grained classification tasks related to art styles. The artists are split into train, validation, and test sets at a ratio of 80%, 10%, and 10%, respectively. Similarly, each artist's 30 images are also split into train, validation, and test sets at the same ratio.
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+
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+ ## Size
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+ 300,000 images (10,000 artists * 30 images/artist)
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+
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+ ## Format
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+ The images are stored as WebP files packaged in `a10k.zip`. The dataset splits are provided in `split.txt`. Each line of `split.txt` follows this format: `artist_split image_split artist_name file_path`.
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