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---
license: mit
task_categories:
- image-classification
- image-to-image
- text-to-image
language:
- en
- ja
pretty_name: yandere2023
size_categories:
- 1M<n<10M
---
# Yandere2023: A Large-Scale Crowdsourced and Tagged Anime Illustration Dataset
<!-- Provide a quick summary of the dataset. -->
Yandere2023 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 12 tags per image.
Yandere 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
## Uses
## 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 ‘999’); IDs can be turned into paths by dividing & padding (eg. in Bash, BUCKET=$(printf "%04d" $(( ID % 1000 )) )) and then the file is at original/$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:
```bash
$ tree / | less
/
├── yandere2023 -> /mnt/diffusionstorage/workspace/yandere/
│ ├── metadata
│ ├── readme.md
│ ├── original
│ │ ├── 0000 -> data-0000.tar
│ │ ├── 0001 -> data-0001.tar
│ │ │ ├── 10001.jpg
│ │ │ ├── 210001.png
│ │ │ ├── 3120001.webp
│ │ │ ├── 6513001.jpg
```
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 etc. |