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---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of ling/リィン/令 (Arknights)
This is the dataset of ling/リィン/令 (Arknights), containing 500 images and their tags.
The core tags of this character are `blue_hair, long_hair, horns, dragon_horns, pointy_ears, breasts, very_long_hair, blue_eyes, earrings, dragon_girl, braid, multicolored_hair, large_breasts, tail, dragon_tail, streaked_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 | 500 | 1.17 GiB | [Download](https://huggingface.co/datasets/CyberHarem/ling_arknights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | -- | -- |
| stage3-p480-1200 | 1337 | 1.76 GiB | [Download](https://huggingface.co/datasets/CyberHarem/ling_arknights/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | 914 | 423 |
### 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/ling_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 | 12 |  |  |  |  |  | 1girl, black_shorts, jewelry, open_coat, short_shorts, solo, white_coat, white_footwear, white_shirt, yellow_necktie, boots, long_sleeves, looking_at_viewer, full_body, smile, thigh_strap, black_gloves, elbow_gloves, lantern, wide_sleeves, gourd, closed_mouth, holding_staff |
| 1 | 24 |  |  |  |  |  | 1girl, black_shorts, long_sleeves, looking_at_viewer, open_coat, solo, white_coat, white_shirt, yellow_necktie, black_gloves, jewelry, short_shorts, smile, parted_lips, wide_sleeves, elbow_gloves, holding_cup, thigh_strap, cowboy_shot, detached_collar, gourd, navel, sitting |
| 2 | 5 |  |  |  |  |  | 1girl, black_shorts, cowboy_shot, detached_collar, holding, jewelry, long_sleeves, looking_at_viewer, navel, short_shorts, smile, solo, bandeau, bare_shoulders, black_coat, gloves, off_shoulder, open_coat, purple_eyes, thigh_strap, thighs, belt, gourd, medium_breasts, parted_lips, ponytail, staff, tube_top, bottle, dragon, midriff, ribbon |
| 3 | 5 |  |  |  |  |  | 1girl, jewelry, looking_at_viewer, official_alternate_costume, solo, white_dress, bare_shoulders, closed_mouth, holding, smile, cowboy_shot, detached_sleeves, elbow_gloves, wide_sleeves, black_gloves, from_side, simple_background, sleeveless_dress, tassel, white_background |
| 4 | 11 |  |  |  |  |  | 1girl, holding, jewelry, solo, blue_skin, detached_sleeves, looking_at_viewer, official_alternate_costume, smile, white_dress, wide_sleeves, bare_shoulders, sash, hair_between_eyes, long_sleeves, purple_eyes, feet_out_of_frame, gloves, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_shorts | jewelry | open_coat | short_shorts | solo | white_coat | white_footwear | white_shirt | yellow_necktie | boots | long_sleeves | looking_at_viewer | full_body | smile | thigh_strap | black_gloves | elbow_gloves | lantern | wide_sleeves | gourd | closed_mouth | holding_staff | parted_lips | holding_cup | cowboy_shot | detached_collar | navel | sitting | holding | bandeau | bare_shoulders | black_coat | gloves | off_shoulder | purple_eyes | thighs | belt | medium_breasts | ponytail | staff | tube_top | bottle | dragon | midriff | ribbon | official_alternate_costume | white_dress | detached_sleeves | from_side | simple_background | sleeveless_dress | tassel | white_background | blue_skin | sash | hair_between_eyes | feet_out_of_frame |
|----:|----------:|:-------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------|:--------|:---------------|:----------|:------------|:---------------|:-------|:-------------|:-----------------|:--------------|:-----------------|:--------|:---------------|:--------------------|:------------|:--------|:--------------|:---------------|:---------------|:----------|:---------------|:--------|:---------------|:----------------|:--------------|:--------------|:--------------|:------------------|:--------|:----------|:----------|:----------|:-----------------|:-------------|:---------|:---------------|:--------------|:---------|:-------|:-----------------|:-----------|:--------|:-----------|:---------|:---------|:----------|:---------|:-----------------------------|:--------------|:-------------------|:------------|:--------------------|:-------------------|:---------|:-------------------|:------------|:-------|:--------------------|:--------------------|
| 0 | 12 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 24 |  |  |  |  |  | X | X | X | X | X | X | X | | X | X | | X | X | | X | X | X | X | | X | X | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | 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 | X | X | X | X | X | X | X | | | | | | | | | | | | |
| 3 | 5 |  |  |  |  |  | X | | X | | | X | | | | | | | X | | X | | X | X | | X | | X | | | | X | | | | X | | X | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | | | | |
| 4 | 11 |  |  |  |  |  | X | | X | | | X | | | | | | X | X | | X | | | | | X | | | | | | | | | | X | | X | | X | | X | | | | | | | | | | | X | X | X | | | | | X | X | X | X | X |
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