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---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Yulie/尤莉叶 (Arknights)
This is the dataset of Yulie/尤莉叶 (Arknights), containing 25 images and their tags.
The core tags of this character are `breasts, hair_over_one_eye, long_hair, red_hair, horns, pointy_ears, red_eyes, ear_piercing, brown_hair`, 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 | 61.22 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yulie_arknights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | -- | -- |
| stage3-p180-1200 | 65 | 82.26 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yulie_arknights/resolve/main/dataset-stage3-p180-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 180x180 pixels. | 47 | 18 |
### 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/yulie_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 | 10 |  |  |  |  |  | 1girl, looking_at_viewer, piercing, solo, closed_mouth, shoulder_armor, outdoors, upper_body, open_jacket, belt, scar_on_face, demon_horns, earrings, medium_breasts, sky |
| 1 | 8 |  |  |  |  |  | 1girl, belt, black_footwear, black_pants, boots, coat, full_body, shoulder_armor, solo, standing, sword, torn_clothes, looking_at_viewer, outdoors, gloves, sheathed, building, cloud, holding_weapon, parted_lips, sky |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | piercing | solo | closed_mouth | shoulder_armor | outdoors | upper_body | open_jacket | belt | scar_on_face | demon_horns | earrings | medium_breasts | sky | black_footwear | black_pants | boots | coat | full_body | standing | sword | torn_clothes | gloves | sheathed | building | cloud | holding_weapon | parted_lips |
|----:|----------:|:--------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------|:--------|:--------------------|:-----------|:-------|:---------------|:-----------------|:-----------|:-------------|:--------------|:-------|:---------------|:--------------|:-----------|:-----------------|:------|:-----------------|:--------------|:--------|:-------|:------------|:-----------|:--------|:---------------|:---------|:-----------|:-----------|:--------|:-----------------|:--------------|
| 0 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | |
| 1 | 8 |  |  |  |  |  | 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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