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---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Old Tianshi/老天师 (Arknights)
This is the dataset of Old Tianshi/老天师 (Arknights), containing 25 images and their tags.
The core tags of this character are `blonde_hair, horns, long_hair, animal_ears, facial_mark, yellow_eyes, grey_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-others | Images-head |
|:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|:----------------|:--------------|
| raw | 25 | 60.25 MiB | [Download](https://huggingface.co/datasets/CyberHarem/old_tianshi_arknights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | -- | -- |
| stage3-p180-1200 | 63 | 86.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/old_tianshi_arknights/resolve/main/dataset-stage3-p180-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 180x180 pixels. | 39 | 24 |
### 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/old_tianshi_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 | 14 |  |  |  |  |  | 1girl, bare_shoulders, forehead_mark, grin, looking_at_viewer, off_shoulder, solo, collarbone, dress, hand_on_own_hip, outdoors, sky, upper_body, dutch_angle |
| 1 | 6 |  |  |  |  |  | 1girl, bare_shoulders, black_gloves, fingerless_gloves, grin, hand_on_own_hip, looking_at_viewer, off_shoulder, solo, standing, black_footwear, boots, dress, full_body, holding, sky, cleavage, cloud, outdoors |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | forehead_mark | grin | looking_at_viewer | off_shoulder | solo | collarbone | dress | hand_on_own_hip | outdoors | sky | upper_body | dutch_angle | black_gloves | fingerless_gloves | standing | black_footwear | boots | full_body | holding | cleavage | cloud |
|----:|----------:|:--------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------|:--------|:-----------------|:----------------|:-------|:--------------------|:---------------|:-------|:-------------|:--------|:------------------|:-----------|:------|:-------------|:--------------|:---------------|:--------------------|:-----------|:-----------------|:--------|:------------|:----------|:-----------|:--------|
| 0 | 14 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | X | | X | X | X | X | | X | X | X | X | | | X | X | X | X | X | X | X | X | X |
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