CyberHarem/centurion_reverse1999

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

# Dataset of Centurion/百夫长 (Reverse:1999)

This is the dataset of Centurion/百夫长 (Reverse:1999), containing 22 images and their tags.

The core tags of this character are `dark-skinned_female, long_hair, dark_skin, black_hair, earrings, multicolored_hair, breasts, streaked_hair, white_hair, hair_over_one_eye, yellow_eyes`, 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 | 22 | 53.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/centurion_reverse1999/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | -- | -- |
| stage3-p480-1200 | 52 | 75.95 MiB | [Download](https://huggingface.co/datasets/CyberHarem/centurion_reverse1999/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | 15 | 37 |

### 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/centurion_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 | 9 |  |  |  |  |  | 1girl, looking_at_viewer, black_bikini, smile, solo, necklace, shirt, bare_shoulders, bubble |
| 1 | 12 |  |  |  |  |  | 1girl, looking_at_viewer, solo, bracelet, smile, necklace, simple_background, white_background, black_pants, upper_body, white_jacket, white_shirt |

### Table Version

| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | black_bikini | smile | solo | necklace | shirt | bare_shoulders | bubble | bracelet | simple_background | white_background | black_pants | upper_body | white_jacket | white_shirt |
|----:|----------:|:--------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------|:--------|:--------------------|:---------------|:--------|:-------|:-----------|:--------|:-----------------|:---------|:-----------|:--------------------|:-------------------|:--------------|:-------------|:---------------|:--------------|
| 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | | | | | | | |
| 1 | 12 |  |  |  |  |  | X | X | | X | X | X | | | | X | X | X | X | X | X | X |