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---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Peppe/佩佩 (Arknights)
This is the dataset of Peppe/佩佩 (Arknights), containing 25 images and their tags.
The core tags of this character are `blue_eyes, brown_hair, long_hair, hair_ornament, animal_ears, cat_tail, tail, cat_ears`, 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-others | Images-head |
|:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|:----------------|:--------------|
| raw | 25 | 18.51 MiB | [Download](https://huggingface.co/datasets/CyberHarem/peppe_arknights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | -- | -- |
| stage3-p180-1200 | 62 | 42.73 MiB | [Download](https://huggingface.co/datasets/CyberHarem/peppe_arknights/resolve/main/dataset-stage3-p180-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 180x180 pixels. | 45 | 17 |
### 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/peppe_arknights',
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 | 25 |  |  |  |  |  | 1girl, hood_up, looking_at_viewer, smile, solo, holding, closed_mouth, hooded_jacket, midriff, navel, long_sleeves, black_jacket, black_shorts, short_shorts, open_jacket, belt, crop_top, thigh_strap, weapon, white_shirt, standing, blush, white_flower, cat_hood, outdoors |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | hood_up | looking_at_viewer | smile | solo | holding | closed_mouth | hooded_jacket | midriff | navel | long_sleeves | black_jacket | black_shorts | short_shorts | open_jacket | belt | crop_top | thigh_strap | weapon | white_shirt | standing | blush | white_flower | cat_hood | outdoors |
|----:|----------:|:--------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------|:--------|:----------|:--------------------|:--------|:-------|:----------|:---------------|:----------------|:----------|:--------|:---------------|:---------------|:---------------|:---------------|:--------------|:-------|:-----------|:--------------|:---------|:--------------|:-----------|:--------|:---------------|:-----------|:-----------|
| 0 | 25 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
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