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Danbooru2023: A Large-Scale Crowdsourced and Tagged Anime Illustration Dataset

Danbooru2023 is a large-scale anime image dataset with over 5 million images contributed and annotated in detail by an enthusiast community. Image tags cover aspects like characters, scenes, copyrights, artists, etc with an average of 30 tags per image.

Danbooru is a veteran anime image board with high-quality images and extensive tag metadata. The dataset can be used to train image classification, multi-label tagging, character detection, generative models, and other computer vision tasks.

  • Shared by: Nyanko Devs
  • Language(s): English, Japanese
  • License: MIT

This dataset is built on the top of danbooru2021. We expands the dataset to include images up to ID #6,857,737, adding over 1.8 million additional images and total size is now approximately 8 terabytes (8,000 GB).

Use

Format

The goal of the dataset is to be as easy as possible to use immediately, avoiding obscure file formats, while allowing simultaneous research & seeding of the torrent, with easy updates.

Images are provided in the full original form (be that JPG, PNG, GIF or otherwise) for reference/archival purposes, and bucketed into 1000 subdirectories 0000–0999 (0-padded), which is the Danbooru ID modulo 1000 (ie. all images in 0999/ have an ID ending in β€˜999’); IDs can be turned into paths by dividing & padding (eg. in Bash, BUCKET=$(printf "%04d" $(( ID % 1000 )) )) and then the file is at {original,512px}/$BUCKET/$ID.$EXT.

The reason for the bucketing is that a single directory would cause pathological filesystem performance, and modulo ID is a simple hash which spreads images evenly without requiring additional future directories to be made or a filesystem IO to check where the file is. The ID is not zero-padded and files end in the relevant extension, hence the file layout looks like this:

$ tree / | less

/
β”œβ”€β”€ danbooru2023 -> /mnt/diffusionstorage/workspace/danbooru/
β”‚   β”œβ”€β”€ metadata
β”‚   β”œβ”€β”€ readme.md
β”‚   β”œβ”€β”€ original
β”‚   β”‚   β”œβ”€β”€ 0000 -> data-0000.tar
β”‚   β”‚   β”œβ”€β”€ 0001 -> data-0001.tar
β”‚   β”‚   β”‚   β”œβ”€β”€ 10001.jpg
β”‚   β”‚   β”‚   β”œβ”€β”€ 210001.png
β”‚   β”‚   β”‚   β”œβ”€β”€ 3120001.webp
β”‚   β”‚   β”‚   β”œβ”€β”€ 6513001.jpg
β”‚   β”‚
β”‚   β”œβ”€β”€ recent
β”‚   β”‚   β”œβ”€β”€ 0000 -> data-1000.tar
β”‚   β”‚   β”œβ”€β”€ 0001 -> data-1001.tar
β”‚   β”‚
β”‚   β”œβ”€β”€ updates
β”‚   β”‚   β”œβ”€β”€ 20240319
β”‚   β”‚   β”‚   β”œβ”€β”€ dataset-0.tar
β”‚   β”‚   β”‚   β”œβ”€β”€ dataset-1.tar
β”‚   β”‚   β”‚
β”‚   β”‚   β”œβ”€β”€ 2024xxxx
β”‚   β”‚   β”‚   β”œβ”€β”€ dataset-0.tar
β”‚   β”‚   β”‚   β”œβ”€β”€ dataset-1.tar

Where data-{1000..1999}.tar refer to recent update files (should be updated every few months) and updates refer to fast patches (should be updated every few days to few weeks).

Currently represented file extensions are: avi/bmp/gif/html/jpeg/jpg/mp3/mp4/mpg/pdf/png/rar/swf/webm/wmv/zip.

Raw original files are treacherous. Be careful if working with the original dataset. There are many odd files: truncated, non-sRGB colorspace, wrong file extensions (eg. some PNGs have .jpg extensions like original/0146/1525146.jpg or original/0558/1422558.jpg), etc.

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