CyberHarem/bb_fgo

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

# Dataset of bb/BB/BB (Fate/Grand Order)

This is the dataset of bb/BB/BB (Fate/Grand Order), containing 500 images and their tags.

The core tags of this character are `long_hair, purple_hair, breasts, ribbon, hair_ribbon, very_long_hair, purple_eyes, large_breasts, red_ribbon, neck_ribbon`, 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 | 500 | 709.19 MiB | [Download](https://huggingface.co/datasets/CyberHarem/bb_fgo/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 500 | 629.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/bb_fgo/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1258 | 1.20 GiB | [Download](https://huggingface.co/datasets/CyberHarem/bb_fgo/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/bb_fgo',
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 | 5 |  |  |  |  |  | 1girl, black_skirt, black_thighhighs, high-waist_skirt, long_sleeves, looking_at_viewer, sitting, smile, solo, white_gloves, wide_sleeves, black_coat, closed_mouth, holding_wand, white_shirt, chain, open_clothes, blush |
| 1 | 9 |  |  |  |  |  | 1girl, black_coat, black_skirt, closed_mouth, high-waist_skirt, long_sleeves, looking_at_viewer, smile, solo, white_gloves, wide_sleeves, holding_wand, open_coat, thigh_boots, thighs, black_thighhighs, blush, hand_on_own_hip, popped_collar, black_footwear, petals, white_leotard, white_shirt |
| 2 | 12 |  |  |  |  |  | 1girl, black_coat, black_skirt, high-waist_skirt, long_sleeves, looking_at_viewer, open_coat, popped_collar, smile, solo, white_gloves, wide_sleeves, white_shirt, thighhighs, thighs, white_leotard, blush, closed_mouth, petals |
| 3 | 5 |  |  |  |  |  | 1girl, black_coat, black_skirt, high-waist_skirt, holding_wand, long_sleeves, looking_at_viewer, open_coat, solo, white_gloves, white_shirt, wide_sleeves, open_mouth, white_background, blush, popped_collar, simple_background, :d |
| 4 | 10 |  |  |  |  |  | 1girl, black_coat, black_skirt, high-waist_skirt, holding_wand, long_sleeves, looking_at_viewer, open_clothes, solo, white_gloves, white_shirt, wide_sleeves, blush, closed_mouth, simple_background, smile, white_background, black_thighhighs, hair_between_eyes, v-shaped_eyebrows |
| 5 | 15 |  |  |  |  |  | 1girl, black_thighhighs, solo, skirt, looking_at_viewer, white_gloves, lace-trimmed_legwear, smile |
| 6 | 7 |  |  |  |  |  | 1girl, cleavage, looking_at_viewer, nurse_cap, smile, solo, white_dress, wrist_cuffs, blush, short_sleeves, glasses, black-framed_eyewear, collarbone, short_dress, syringe, white_choker, white_headwear, white_thighhighs, full_body, holding, open_mouth, white_footwear |
| 7 | 17 |  |  |  |  |  | 1girl, bare_shoulders, looking_at_viewer, solo, baseball_cap, off_shoulder, single_braid, cleavage, smile, white_headwear, yellow_jacket, collarbone, white_bikini, yellow_skirt, blush, navel, backwards_hat, bandeau, long_sleeves, thighs, blue_sky, day, outdoors, cloud, one_eye_closed, star_hat_ornament, bikini_top_only, closed_mouth, sideways_hat, arm_up, beach, ocean, tongue_out |
| 8 | 24 |  |  |  |  |  | 1girl, bare_shoulders, blush, looking_at_viewer, black_gloves, tan, fingerless_gloves, hairband, purple_bikini, smile, solo, black_thighhighs, cleavage, collarbone, star_hair_ornament, tongue_out, dark-skinned_female, navel, thighs, star_earrings, micro_shorts, purple_ribbon, bead_bracelet, closed_mouth, white_belt, black_garter_belt, gyaru, one_eye_closed, black_shorts, nail_polish |
| 9 | 14 |  |  |  |  |  | 1girl, blush, hetero, sweat, thighs, penis, solo_focus, vaginal, mosaic_censoring, nude, open_mouth, male_pubic_hair, multiple_boys, clothing_aside, erection, gangbang, nipples, spread_legs, testicles, bare_shoulders, smile, collarbone, cum_in_pussy, tongue_out, dark_skin, girl_on_top, looking_at_viewer, navel, swimsuit, white_leotard, anal, ass, ejaculation |

### Table Version

| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_skirt | black_thighhighs | high-waist_skirt | long_sleeves | looking_at_viewer | sitting | smile | solo | white_gloves | wide_sleeves | black_coat | closed_mouth | holding_wand | white_shirt | chain | open_clothes | blush | open_coat | thigh_boots | thighs | hand_on_own_hip | popped_collar | black_footwear | petals | white_leotard | thighhighs | open_mouth | white_background | simple_background | :d | hair_between_eyes | v-shaped_eyebrows | skirt | lace-trimmed_legwear | cleavage | nurse_cap | white_dress | wrist_cuffs | short_sleeves | glasses | black-framed_eyewear | collarbone | short_dress | syringe | white_choker | white_headwear | white_thighhighs | full_body | holding | white_footwear | bare_shoulders | baseball_cap | off_shoulder | single_braid | yellow_jacket | white_bikini | yellow_skirt | navel | backwards_hat | bandeau | blue_sky | day | outdoors | cloud | one_eye_closed | star_hat_ornament | bikini_top_only | sideways_hat | arm_up | beach | ocean | tongue_out | black_gloves | tan | fingerless_gloves | hairband | purple_bikini | star_hair_ornament | dark-skinned_female | star_earrings | micro_shorts | purple_ribbon | bead_bracelet | white_belt | black_garter_belt | gyaru | black_shorts | nail_polish | hetero | sweat | penis | solo_focus | vaginal | mosaic_censoring | nude | male_pubic_hair | multiple_boys | clothing_aside | erection | gangbang | nipples | spread_legs | testicles | cum_in_pussy | dark_skin | girl_on_top | swimsuit | anal | ass | ejaculation |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------|:-------------------|:-------------------|:---------------|:--------------------|:----------|:--------|:-------|:---------------|:---------------|:-------------|:---------------|:---------------|:--------------|:--------|:---------------|:--------|:------------|:--------------|:---------|:------------------|:----------------|:-----------------|:---------|:----------------|:-------------|:-------------|:-------------------|:--------------------|:-----|:--------------------|:--------------------|:--------|:-----------------------|:-----------|:------------|:--------------|:--------------|:----------------|:----------|:-----------------------|:-------------|:--------------|:----------|:---------------|:-----------------|:-------------------|:------------|:----------|:-----------------|:-----------------|:---------------|:---------------|:---------------|:----------------|:---------------|:---------------|:--------|:----------------|:----------|:-----------|:------|:-----------|:--------|:-----------------|:--------------------|:------------------|:---------------|:---------|:--------|:--------|:-------------|:---------------|:------|:--------------------|:-----------|:----------------|:---------------------|:----------------------|:----------------|:---------------|:----------------|:----------------|:-------------|:--------------------|:--------|:---------------|:--------------|:---------|:--------|:--------|:-------------|:----------|:-------------------|:-------|:------------------|:----------------|:-----------------|:-----------|:-----------|:----------|:--------------|:------------|:---------------|:------------|:--------------|:-----------|:-------|:------|:--------------|
| 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 9 |  |  |  |  |  | X | X | X | X | X | X | | X | X | X | X | X | X | X | X | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 12 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 10 |  |  |  |  |  | X | X | X | X | X | X | | X | X | X | X | X | X | X | X | | X | X | | | | | | | | | | | X | X | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 15 |  |  |  |  |  | X | | X | | | X | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 7 |  |  |  |  |  | X | | | | | X | | X | X | | | | | | | | | X | | | | | | | | | | X | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 17 |  |  |  |  |  | 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 | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 24 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | |
| 9 | 14 |  |  |  |  |  | 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 | X |

原始信息汇总

数据集概述：bb/BB/BB (Fate/Grand Order)

数据集基本信息

• 许可证: MIT
• 任务类别: 文本到图像
• 标签: 艺术, 非全年龄适用
• 大小类别: 小于1K

数据集内容

• 包含内容: 500张图像及其标签
• 核心标签: long_hair, purple_hair, breasts, ribbon, hair_ribbon, very_long_hair, purple_eyes, large_breasts, red_ribbon, neck_ribbon

数据集下载

• raw: 500张图像, 709.19 MiB

  • 下载链接: raw数据集
  • 类型: Waifuc-Raw
  • 描述: 包含元信息的原始数据（最小边对齐到1400像素，如果更大）

• 1200: 500张图像, 629.28 MiB

  • 下载链接: 1200数据集
  • 类型: IMG+TXT
  • 描述: 短边不超过1200像素的图像数据集

• stage3-p480-1200: 1258张图像, 1.20 GiB

  • 下载链接: stage3-p480-1200数据集
  • 类型: IMG+TXT
  • 描述: 三阶段裁剪数据集，区域不小于480x480像素

数据集使用

• 加载工具: 使用waifuc加载原始数据集

• 示例代码: python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource

  zip_file = hf_hub_download( repo_id=CyberHarem/bb_fgo, repo_type=dataset, filename=dataset-raw.zip, )

  dataset_dir = dataset_dir os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, r) as zf: zf.extractall(dataset_dir)

  source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta[filename], item.meta[tags])

数据集标签集群

• 集群列表: 提供了标签集群的结果，可能包含可挖掘的服装信息
• 示例集群:
  • 集群0: 包含5张图像，标签包括1girl, black_skirt, black_thighhighs等
  • 集群1: 包含9张图像，标签包括1girl, black_coat, black_skirt等
  • 集群2: 包含12张图像，标签包括1girl, black_coat, black_skirt等
  • 集群3: 包含5张图像，标签包括1girl, black_coat, black_skirt等
  • 集群4: 包含10张图像，标签包括1girl, black_coat, black_skirt等
  • 集群5: 包含15张图像，标签包括1girl, black_thighhighs, solo等
  • 集群6: 包含7张图像，标签包括1girl, cleavage, looking_at_viewer等
  • 集群7: 包含17张图像，标签包括1girl, bare_shoulders, looking_at_viewer等
  • 集群8: 包含24张图像，标签包括1girl, bare_shoulders, blush等
  • 集群9: 包含14张图像，标签包括1girl, blush, hetero等