FFHQ高质量人脸数据集
Flickr-Faces-HQ Dataset 是与皱纹（Wrinkles）相关的公开数据资源，主要包含高质量人脸图像和年龄、姿态及面部属性覆盖数据，数据模态为图像，适合用于分类、模型训练、基准评测或临床特征分析。
基本信息
资源简介
FFHQ高质量人脸数据集
【数据集背景】
本数据集源站标题为《NVlabs/ffhq-dataset》。源站首次发布：2019-02-04。
【数据内容】
源站原始描述：Flickr-Faces-HQ Dataset (FFHQ) ## Flickr-Faces-HQ Dataset (FFHQ) 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) > The dataset consists of 70,000 high-quality PNG images at 1024×1024 resolution and contains considerable variation in terms of age, et…
【适用方向】
影像类数据可用于医学图像分析模型（检测、分割、分类）的训练与评测，也可用于标注质量与成像参数相关性的研究。
【获取与许可】
源站页面：https://github.com/NVlabs/ffhq-dataset
使用许可：NOASSERTION。
【使用注意】
· 源站最后修改于 2022-11-18，引用时请注意版本时效。
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载，适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档，适合快速预览和评估数据集质量。
免登录有偿下载
公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取
git clone https://github.com/NVlabs/ffhq-dataset.git
curl -L -o repo.zip https://github.com/NVlabs/ffhq-dataset/archive/refs/heads/master.zip
unzip repo.zip
源站 README 摘录（使用方式）
Download script
You can either grab the data directly from Google Drive or use the provided download script. The script makes things considerably easier by automatically downloading all the requested files, verifying their checksums, retrying each file several times on error, and employing multiple concurrent connections to maximize bandwidth.
> python download_ffhq.py -h
usage: download_ffhq.py [-h] [-j] [-s] [-i] [-t] [-w] [-r] [-a]
[num_threads NUM] [status_delay SEC]
[timing_window LEN] [chunk_size KB]
[num_attempts NUM]
Download Flickr-Face-HQ (FFHQ) dataset to current working directory.
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, wilds download in-the-wild images as PNG (955 GB)
-r, tfrecords download multi-resolution TFRecords (273 GB)
-a, align recreate 1024x1024 images from in-the-wild images
num_threads NUM number of concurrent download threads (default: 32)
status_delay SEC time between download status prints (default: 0.2)
timing_window LEN samples for estimating download eta (default: 50)
chunk_size KB chunk size for each download thread (default: 128)
num_attempts NUM number of download attempts per file (default: 10)
random-shift SHIFT standard deviation of random crop rectangle jitter
retry-crops retry random shift if crop rectangle falls outside image (up to 1000
times)
no-rotation keep the original orientation of images
no-padding do not apply blur-padding outside and near the image borders
source-dir DIR where to find already downloaded FFHQ source data
> python ..\download_ffhq.py json images
Downloading JSON metadata...
\ 100.00% done 2/2 files 0.25/0.25 GB 43.21 MB/s ETA: done
Parsing JSON metadata...
Downloading 70000 files...
| 100.00% done 70001/70001 files 89.19 GB/89.19 GB 59.87 MB/s ETA: done
The script also serves as a referen
数据加载示例（图像类）
from PIL import Image
import glob, os
files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)
# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path) # 要求 子目录=类别
目录组织与标注格式以源站说明和下载后实际文件为准。
数据缺失？
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