FFHQ全称Flickr-Faces-High-Quality(Flickr-Faces-HQ),最初是作为生成式对抗网络(GAN)的基准创建的,也用于StyleGAN的训练数据集中,并由英伟达于2019年开源。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...
a dataset of human faces with a correctly and incorrectly worn mask based on the dataset Flickr-Faces-HQ 基于FFHQ数据集的正确/错误佩戴口罩人脸图像数据集 导读 MaskedFace-Net 是基于数据集 Flickr-Faces-HQ (FFHQ) 的带有正确或错误佩戴口罩的人脸数据集(133,783 张图像)。 戴口罩似乎是限制 COVID-19...
这篇论文由TeroKarras等人于2018年提出,介绍了一种名为FFHQ(Flickr-Faces-HQ)的高分辨率人脸图像数据集以及一种改进的生成对抗网络架构。FFHQ数据集由70,000张无标签的人脸图像组成,这些图像是从Flickr网站上下载并经过筛选和处理得到的。这个数据集的特点是图像质量高、分辨率大(1024x1024像素)以及多样性。FFHQ数据集...
【FFHQ:Style-GAN论文中用于训练生成逼真人脸的数据集,分辨率1024×1024的70,000张高质量PNG图像,在年龄,种族和图像背景方面存在广泛差异】’Flickr-Faces-HQ Dataset (FFHQ)' by NVIDIA Research Projects GitHub: O网页链接 ref:O网页链接 ...
高清人脸图像数据集(Flickr-Faces-HQ) FFHQ 英伟达NVIDIAFlickr人脸数据集高质量人脸图像 该数据集包含70,000张分辨率为1024×1024的高质量PNG图像,并且在年龄,种族和图像背景方面都存在很大差异。它还覆盖了诸如眼镜,太阳镜,帽子等配件。 下载所需积分:免积分下载 ...
Flickr-Faces-HQ Dataset (FFHQ) 256x256 艾梦 7枚 CC BY 4.0 计算机视觉 5 119 2021-10-18 详情 相关项目 评论(0) 创建项目 数据集介绍 数据来自互联网, 如果涉及到侵权问题,请联系本人进行删除修改等操作。 文件列表 images256x256.zip images256x256.zip (6950.49M) 下载 File Name Size Update Time ...
To find out whether your photo is included in the Flickr-Faces-HQ dataset, pleaseclick this linkto search the dataset with your Flickr username. To get your photo removed from the Flickr-Faces-HQ dataset: Go to Flickr and do one of the following: ...
Flickr-Faces-HQ (FFHQ) is a high-quality image dataset of human faces, originally created as a benchmark for generative adversarial networks (GAN):A Style-Based Generator Architecture for Generative Adversarial Networks Tero Karras (NVIDIA), Samuli Laine (NVIDIA), Timo Aila (NVIDIA) https://ar...
Flickr-Faces-HQ (FFHQ) 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,
Flickr-Faces-HQ (FFHQ) is a high-quality image dataset of human faces, originally created as a benchmark for generative adversarial networks (GAN): A Style-Based Generator Architecture for Generative Adversarial Networks Tero Karras (NVIDIA), Samuli Laine (NVIDIA), Timo Aila (NVIDIA) ...