CyberHarem/chikuma_azurlane

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

# Dataset of chikuma/筑摩/筑摩 (Azur Lane)

This is the dataset of chikuma/筑摩/筑摩 (Azur Lane), containing 92 images and their tags.

The core tags of this character are `animal_ears, long_hair, breasts, large_breasts, rabbit_ears, brown_hair, bangs, mole, braid, mole_under_mouth, hair_ornament, hair_between_eyes, orange_eyes, hair_flower`, 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 |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 92 | 161.91 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chikuma_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 92 | 78.98 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chikuma_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 243 | 175.66 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chikuma_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 92 | 136.48 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chikuma_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 243 | 272.43 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chikuma_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |

### 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/chikuma_azurlane',
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 | 6 |  |  |  |  |  | 1girl, black_skirt, brown_eyes, holding_sword, looking_at_viewer, sideboob, simple_background, solo, black_thighhighs, pleated_skirt, smile, zettai_ryouiki, flower, katana, long_sleeves, orange_necktie, breast_curtain, parted_lips, white_background |
| 1 | 12 |  |  |  |  |  | 1girl, black_skirt, looking_at_viewer, pleated_skirt, sideboob, solo, black_thighhighs, flower, long_sleeves, orange_necktie, smile, simple_background, white_background, shirt, zettai_ryouiki, blush, thighs, high-waist_skirt |

### Table Version

| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_skirt | brown_eyes | holding_sword | looking_at_viewer | sideboob | simple_background | solo | black_thighhighs | pleated_skirt | smile | zettai_ryouiki | flower | katana | long_sleeves | orange_necktie | breast_curtain | parted_lips | white_background | shirt | blush | thighs | high-waist_skirt |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------|:-------------|:----------------|:--------------------|:-----------|:--------------------|:-------|:-------------------|:----------------|:--------|:-----------------|:---------|:---------|:---------------|:-----------------|:-----------------|:--------------|:-------------------|:--------|:--------|:---------|:-------------------|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | |
| 1 | 12 |  |  |  |  |  | X | X | | | X | X | X | X | X | X | X | X | X | | X | X | | | X | X | X | X | X |

原始信息汇总

数据集概述

数据集名称

• 名称: chikuma/筑摩/筑摩 (Azur Lane)
• 包含内容: 92张图像及其标签

数据集特征

• 核心标签: animal_ears, long_hair, breasts, large_breasts, rabbit_ears, brown_hair, bangs, mole, braid, mole_under_mouth, hair_ornament, hair_between_eyes, orange_eyes, hair_flower
• 来源: 图像从多个网站爬取，如danbooru, pixiv, zerochan等，由DeepGHS Team提供技术支持

数据集包

名称	图像数量	大小	类型	描述
raw	92	161.91 MiB	Waifuc-Raw	包含元信息的原始数据，最小边对齐至1400像素
800	92	78.98 MiB	IMG+TXT	短边不超过800像素的图像数据
stage3-p480-800	243	175.66 MiB	IMG+TXT	区域不小于480x480像素的三阶段裁剪数据
1200	92	136.48 MiB	IMG+TXT	短边不超过1200像素的图像数据
stage3-p480-1200	243	272.43 MiB	IMG+TXT	区域不小于480x480像素的三阶段裁剪数据

数据集使用

• 加载方式: 提供给waifuc加载的原始数据集，可通过提供的Python代码进行下载和加载

数据集标签聚类

#	样本数	标签（部分）
0	6	1girl, black_skirt, brown_eyes, holding_sword, looking_at_viewer, sideboob, simple_background
1	12	1girl, black_skirt, looking_at_viewer, pleated_skirt, sideboob, solo, black_thighhighs

数据集许可

• 许可: MIT