FFHQ是一个高质量的人脸数据集,包含1024×1024分辨率的70000张PNG格式高清人脸图像,在年龄、种族和图像背景上丰富多样且差异明显,在人脸属性上也拥有非常多的变化,拥有不同的年龄、性别、种族、肤色、表情、脸型、发型、人脸姿态等,囊盖普通眼镜、太阳镜、帽子、发饰及围巾等多种人脸周边配件,因此该数据集也是可以用于...
bash # 克隆官方GitHub仓库 git clone https://github.com/NVlabs/ffhq-dataset.git # 进入仓库目录 cd ffhq-dataset # 使用下载脚本获取1024x1024分辨率的图像 python download_ffhq.py --images 请注意,由于FFHQ数据集的大小较大(约89.1GB),下载可能需要较长时间和足够的存储空间。
FFHQ高清人脸数据集详细介绍 FFHQ全称为Flickr-Faces-High-Quality,是一个用于生成式对抗网络(GAN)基准创建的高质量人脸数据集。英伟达于2019年开源,包含70000张1024x1024分辨率的PNG格式高清人脸图像,数据集在年龄、种族、图像背景等方面丰富多样且差异明显,具有不同的年龄、性别、种族、肤色、表情、脸型...
TrendTaskDataset VariantBest ModelPaperCode Image Generation FFHQ 256 x 256 StyleSAN-XL Image Generation FFHQ 1024 x 1024 StyleSAN-XL Image Generation FFHQ-U Alias-Free-R Image Super-Resolution FFHQ 256 x 256 - 4x upscaling HiFaceGAN
The dataset consists of 70,000 high-quality PNG images at 1024×1024 resolution and contains considerable variation in terms of age, ethnicity and image background. It also has good coverage of accessories such as eyeglasses, sunglasses, hats, etc. The images were crawled fromFlickr, thus inher...
The dataset consists of 70,000 high-quality PNG images at 1024×1024 resolution and contains considerable variation in terms of age, ethnicity and image background. It also has good coverage of accessories such as eyeglasses, sunglasses, hats, etc. The images were crawled fromFlickr, thus inher...
基于深度学习的图像修复系统设计与实现(PyQt5、CodeFormerffhq-dataset数据集) 高清视频演示: https://www.bilibili.com/video/BV17JykYKEr5/ 系统说明: 研究致力于解决图像修复中的关键问题,尤其是在人脸图像修复方面具有重要意义。随着深度学习技术的进步,图像修复系统变得越来越关键,可应用于数字图像处理、医学影像等...
这是FFHQ1024*1024 数据集的一部分,其余部分请搜索“FFHQ1024"。图片尺寸:1024*1024; 本数据集:0~19999.png,1/4数据集; 总共:0~69999.png, 70000张. - 飞桨AI Studio
optional arguments: -h, --help show this help message and exit -j, --json download metadata as JSON (254 MB) -s, --stats print statistics about the dataset -i, --images download 1024x1024 images as PNG (89.1 GB) -t, --thumbs download 128x128 thumbnails as PNG (1.95 GB) -w, ...
parser.add_argument('-s', '--stats', help='print statistics about the dataset', dest='tasks', action='append_const', const='stats') parser.add_argument('-i', '--images', help='download 1024x1024 images as PNG (89.1 GB)', dest='tasks', action='append_const', const='images'...