CyberHarem/satsuki_reverse1999

---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---

# Dataset of Satsuki/五色月 (Reverse:1999)

This is the dataset of Satsuki/五色月 (Reverse:1999), containing 10 images and their tags.

The core tags of this character are `bow, brown_hair, brown_eyes, hair_ornament, short_hair, hair_bow, breasts, hair_flower, hat`, which are pruned in this dataset.

Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).

## List of Packages

| Name | Images | Size | Download | Type | Description | Images-head | Images-others |
|:-----------------|---------:|:----------|:---------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|:--------------|:----------------|
| raw | 10 | 16.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/satsuki_reverse1999/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | -- | -- |
| stage3-p480-1200 | 26 | 27.51 MiB | [Download](https://huggingface.co/datasets/CyberHarem/satsuki_reverse1999/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | 7 | 19 |

### Load Raw Dataset with Waifuc

We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code

```python
import os
import zipfile

from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource

# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/satsuki_reverse1999',
repo_type='dataset',
filename='dataset-raw.zip',
)

# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)

# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```

## List of Clusters

List of tag clustering result, maybe some outfits can be mined here.

### Raw Text Version

| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 10 |  |  |  |  |  | 1girl, solo, chain, flower, long_sleeves, looking_at_viewer, blush, closed_mouth, cuffs, frilled_apron, full_body, smile, wa_maid, white_apron, bonnet, gloves, maid_apron, simple_background, socks, striped_kimono, wide_sleeves |

### Table Version

| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | chain | flower | long_sleeves | looking_at_viewer | blush | closed_mouth | cuffs | frilled_apron | full_body | smile | wa_maid | white_apron | bonnet | gloves | maid_apron | simple_background | socks | striped_kimono | wide_sleeves |
|----:|----------:|:------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------|:--------|:-------|:--------|:---------|:---------------|:--------------------|:--------|:---------------|:--------|:----------------|:------------|:--------|:----------|:--------------|:---------|:---------|:-------------|:--------------------|:--------|:-----------------|:---------------|
| 0 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |