CyberHarem/centurion_reverse1999

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Centurion/百夫长 (Reverse:1999) This is the dataset of Centurion/百夫长 (Reverse:1999), containing 22 images and their tags. The core tags of this character are `dark-skinned_female, long_hair, dark_skin, black_hair, earrings, multicolored_hair, breasts, streaked_hair, white_hair, hair_over_one_eye, yellow_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-head | Images-others | |:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|:--------------|:----------------| | raw | 22 | 53.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/centurion_reverse1999/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | -- | -- | | stage3-p480-1200 | 52 | 75.95 MiB | [Download](https://huggingface.co/datasets/CyberHarem/centurion_reverse1999/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | 15 | 37 | ### 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/centurion_reverse1999', 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 | 9 |  |  |  |  |  | 1girl, looking_at_viewer, black_bikini, smile, solo, necklace, shirt, bare_shoulders, bubble | | 1 | 12 |  |  |  |  |  | 1girl, looking_at_viewer, solo, bracelet, smile, necklace, simple_background, white_background, black_pants, upper_body, white_jacket, white_shirt | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | black_bikini | smile | solo | necklace | shirt | bare_shoulders | bubble | bracelet | simple_background | white_background | black_pants | upper_body | white_jacket | white_shirt | |----:|----------:|:--------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------|:--------|:--------------------|:---------------|:--------|:-------|:-----------|:--------|:-----------------|:---------|:-----------|:--------------------|:-------------------|:--------------|:-------------|:---------------|:--------------| | 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | | | | | | | | | 1 | 12 |  |  |  |  |  | X | X | | X | X | X | | | | X | X | X | X | X | X | X |--- 许可证：MIT 任务类别： - 文本到图像 标签： - 艺术 - 非全年龄段受众 规模类别： - 样本量小于1000 --- # 《重返未来：1999》百夫长（Centurion/百夫长）数据集 本数据集为《重返未来：1999》角色百夫长的专属数据集，包含22张图像及其对应的标注标签。 该角色的核心标注标签为`dark-skinned_female（深肤色女性）、long_hair（长发）、dark_skin（深肤色）、black_hair（黑发）、earrings（耳环）、multicolored_hair（杂色头发）、breasts（胸部）、streaked_hair（挑染头发）、white_hair（白发）、hair_over_one_eye（单眼遮刘海）、yellow_eyes（黄瞳）`，本数据集已对上述核心标签进行了修剪处理。 图像均从多个插画平台爬取（如danbooru、pixiv、zerochan等），自动爬取系统由[DeepGHS团队](https://github.com/deepghs)及[Hugging Face组织](https://huggingface.co/deepghs)提供技术支持。 ## 数据集包列表 | 包名称 | 图像数量 | 大小 | 下载链接 | 数据类型 | 描述 | 头部图像数 | 其他图像数 | |:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|:--------------|:----------------| | raw（原始数据） | 22 | 53.28 MiB | [下载](https://huggingface.co/datasets/CyberHarem/centurion_reverse1999/resolve/main/dataset-raw.zip) | Waifuc-Raw | 包含元信息的原始图像数据（若图像短边大于1400像素，则对齐至1400像素）。 | -- | -- | | stage3-p480-1200（三阶裁剪数据集） | 52 | 75.95 MiB | [下载](https://huggingface.co/datasets/CyberHarem/centurion_reverse1999/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 经过三阶裁剪处理的数据集，裁剪后图像区域尺寸不小于480×480像素。 | 15 | 37 | ### 使用Waifuc加载原始数据集 我们提供了适配Waifuc加载的原始数据集（包含带标注的图像），你可通过以下代码加载该数据集： python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # 下载原始归档文件 zip_file = hf_hub_download( repo_id='CyberHarem/centurion_reverse1999', repo_type='dataset', filename='dataset-raw.zip', ) # 将文件解压至指定目录 dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # 使用Waifuc加载数据集 source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ## 聚类结果列表 ### 原始文本格式 | 聚类编号 | 样本数 | 图像1 | 图像2 | 图像3 | 图像4 | 图像5 | 标注标签 | |----:|----------:|:--------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 9 |  |  |  |  |  | 1girl, looking_at_viewer, black_bikini, smile, solo, necklace, shirt, bare_shoulders, bubble | | 1 | 12 |  |  |  |  |  | 1girl, looking_at_viewer, solo, bracelet, smile, necklace, simple_background, white_background, black_pants, upper_body, white_jacket, white_shirt | ### 表格格式 | 聚类编号 | 样本数 | 图像1 | 图像2 | 图像3 | 图像4 | 图像5 | 单人 | 看向镜头 | 黑色比基尼 | 微笑 | 单独出镜 | 项链 | 衬衫 | 露肩 | 气泡 | 手镯 | 简约背景 | 白色背景 | 黑色长裤 | 上半身 | 白色夹克 | 白色衬衫 | |----:|----------:|:--------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------|:--------|:--------------------|:---------------|:--------|:-------|:-----------|:--------|:-----------------|:---------|:-----------|:--------------------|:-------------------|:--------------|:-------------|:---------------|:--------------| | 0 | 9 |  |  |  |  |  | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | | | | | | | | | 1 | 12 |  |  |  |  |  | ✔ | ✔ | | ✔ | ✔ | ✔ | | | | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ |