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CyberHarem/nana_darlinginthefranxx

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Hugging Face2024-03-30 更新2024-03-04 收录
下载链接:
https://hf-mirror.com/datasets/CyberHarem/nana_darlinginthefranxx
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资源简介:
这是一个名为Nana/ナナ (Darling in the FranXX)的数据集,包含241张图片及其标签。图片从多个网站(如danbooru、pixiv、zerochan等)爬取,爬取系统由DeepGHS团队提供。数据集的核心标签包括`long_hair, red_hair, green_eyes, brown_hair`,并且这些标签在数据集中被修剪。README还提供了数据集的下载链接、类型描述以及如何使用waifuc加载原始数据集的代码示例。此外,还列出了标签聚类结果,展示了不同标签组合下的图片样本。

This is a dataset named Nana/ナナ (Darling in the FranXX), which consists of 241 images and their associated labels. The images were crawled from multiple platforms including danbooru, pixiv, zerochan and other similar websites, with the crawling system provided by the DeepGHS team. The core tags of the dataset are `long_hair, red_hair, green_eyes, brown_hair`, and these tags have been pruned within the dataset. The README file also includes the dataset's download link, category descriptions, and code examples for loading the raw dataset via waifuc. Furthermore, tag clustering results are presented, showcasing image samples corresponding to different tag combinations.
提供机构:
CyberHarem
原始信息汇总

数据集概述

基本信息

  • 数据集名称: Dataset of Nana/ナナ (Darling in the FranXX)
  • 许可证: MIT
  • 任务类别: text-to-image
  • 标签: art, not-for-all-audiences
  • 数据量: n<1K

数据内容

  • 图像数量: 241张
  • 核心标签: long_hair, red_hair, green_eyes, brown_hair
  • 来源: 从多个网站爬取,如danbooru, pixiv, zerochan等

数据包列表

名称 图像数量 大小 类型 描述
raw 241 125.77 MiB Waifuc-Raw 包含元信息的原始数据(最小边对齐到1400像素,如果更大)
1200 241 125.71 MiB IMG+TXT 短边不超过1200像素的数据集
stage3-p480-1200 405 190.93 MiB IMG+TXT 3阶段裁剪数据集,区域不小于480x480像素

数据加载

  • 加载工具: waifuc

  • 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/nana_darlinginthefranxx, 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])

标签聚类结果

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