Flickr-Faces-HQ (FFHQ) is a high-quality image dataset of human faces, originally created as a benchmark for generative adversarial networks (GAN)

Overview

Flickr-Faces-HQ Dataset (FFHQ)

Python 3.6 License CC Format PNG Resolution 1024×1024 Images 70000

Teaser image

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://arxiv.org/abs/1812.04948

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 from Flickr, thus inheriting all the biases of that website, and automatically aligned and cropped using dlib. Only images under permissive licenses were collected. Various automatic filters were used to prune the set, and finally Amazon Mechanical Turk was used to remove the occasional statues, paintings, or photos of photos.

For business inquiries, please contact [email protected]

For press and other inquiries, please contact Hector Marinez at [email protected]

Licenses

The individual images were published in Flickr by their respective authors under either Creative Commons BY 2.0, Creative Commons BY-NC 2.0, Public Domain Mark 1.0, Public Domain CC0 1.0, or U.S. Government Works license. All of these licenses allow free use, redistribution, and adaptation for non-commercial purposes. However, some of them require giving appropriate credit to the original author, as well as indicating any changes that were made to the images. The license and original author of each image are indicated in the metadata.

The dataset itself (including JSON metadata, download script, and documentation) is made available under Creative Commons BY-NC-SA 4.0 license by NVIDIA Corporation. You can use, redistribute, and adapt it for non-commercial purposes, as long as you (a) give appropriate credit by citing our paper, (b) indicate any changes that you've made, and (c) distribute any derivative works under the same license.

Overview

All data is hosted on Google Drive:

Path Size Files Format Description
ffhq-dataset 2.56 TB 210,014 Main folder
├  ffhq-dataset-v2.json 255 MB 1 JSON Metadata including copyright info, URLs, etc.
├  images1024x1024 89.1 GB 70,000 PNG Aligned and cropped images at 1024×1024
├  thumbnails128x128 1.95 GB 70,000 PNG Thumbnails at 128×128
├  in-the-wild-images 955 GB 70,000 PNG Original images from Flickr
├  tfrecords 273 GB 9 tfrecords Multi-resolution data for StyleGAN and StyleGAN2
└  zips 1.28 TB 4 ZIP Contents of each folder as a ZIP archive.

High-level statistics:

Pie charts

For use cases that require separate training and validation sets, we have appointed the first 60,000 images to be used for training and the remaining 10,000 for validation. In the StyleGAN paper, however, we used all 70,000 images for training.

We have explicitly made sure that there are no duplicate images in the dataset itself. However, please note that the in-the-wild folder may contain multiple copies of the same image in cases where we extracted several different faces from the same image.

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 reference implementation of the automated scheme that we used to align and crop the images. Once you have downloaded the in-the-wild images with python download_ffhq.py --wilds, you can run python download_ffhq.py --align to reproduce exact replicas of the aligned 1024×1024 images using the facial landmark locations included in the metadata.

Reproducing the unaligned FFHQ

To reproduce the "unaligned FFHQ" dataset as used in the Alias-Free Generative Adversarial Networks paper, use the following options:

python download_ffhq.py \
    --source-dir 
   
     \
    --align --no-rotation --random-shift 0.2 --no-padding --retry-crops

   

Metadata

The ffhq-dataset-v2.json file contains the following information for each image in a machine-readable format:

{
  "0": {                                                 # Image index
    "category": "training",                              # Training or validation
    "metadata": {                                        # Info about the original Flickr photo:
      "photo_url": "https://www.flickr.com/photos/...",  # - Flickr URL
      "photo_title": "DSCF0899.JPG",                     # - File name
      "author": "Jeremy Frumkin",                        # - Author
      "country": "",                                     # - Country where the photo was taken
      "license": "Attribution-NonCommercial License",    # - License name
      "license_url": "https://creativecommons.org/...",  # - License detail URL
      "date_uploaded": "2007-08-16",                     # - Date when the photo was uploaded to Flickr
      "date_crawled": "2018-10-10"                       # - Date when the photo was crawled from Flickr
    },
    "image": {                                           # Info about the aligned 1024x1024 image:
      "file_url": "https://drive.google.com/...",        # - Google Drive URL
      "file_path": "images1024x1024/00000/00000.png",    # - Google Drive path
      "file_size": 1488194,                              # - Size of the PNG file in bytes
      "file_md5": "ddeaeea6ce59569643715759d537fd1b",    # - MD5 checksum of the PNG file
      "pixel_size": [1024, 1024],                        # - Image dimensions
      "pixel_md5": "47238b44dfb87644460cbdcc4607e289",   # - MD5 checksum of the raw pixel data
      "face_landmarks": [...]                            # - 68 face landmarks reported by dlib
    },
    "thumbnail": {                                       # Info about the 128x128 thumbnail:
      "file_url": "https://drive.google.com/...",        # - Google Drive URL
      "file_path": "thumbnails128x128/00000/00000.png",  # - Google Drive path
      "file_size": 29050,                                # - Size of the PNG file in bytes
      "file_md5": "bd3e40b2ba20f76b55dc282907b89cd1",    # - MD5 checksum of the PNG file
      "pixel_size": [128, 128],                          # - Image dimensions
      "pixel_md5": "38d7e93eb9a796d0e65f8c64de8ba161"    # - MD5 checksum of the raw pixel data
    },
    "in_the_wild": {                                     # Info about the in-the-wild image:
      "file_url": "https://drive.google.com/...",        # - Google Drive URL
      "file_path": "in-the-wild-images/00000/00000.png", # - Google Drive path
      "file_size": 3991569,                              # - Size of the PNG file in bytes
      "file_md5": "1dc0287e73e485efb0516a80ce9d42b4",    # - MD5 checksum of the PNG file
      "pixel_size": [2016, 1512],                        # - Image dimensions
      "pixel_md5": "86b3470c42e33235d76b979161fb2327",   # - MD5 checksum of the raw pixel data
      "face_rect": [667, 410, 1438, 1181],               # - Axis-aligned rectangle of the face region
      "face_landmarks": [...],                           # - 68 face landmarks reported by dlib
      "face_quad": [...]                                 # - Aligned quad of the face region
    }
  },
  ...
}

Acknowledgements

We thank Jaakko Lehtinen, David Luebke, and Tuomas Kynkäänniemi for in-depth discussions and helpful comments; Janne Hellsten, Tero Kuosmanen, and Pekka Jänis for compute infrastructure and help with the code release.

We also thank Vahid Kazemi and Josephine Sullivan for their work on automatic face detection and alignment that enabled us to collect the data in the first place:

One Millisecond Face Alignment with an Ensemble of Regression Trees
Vahid Kazemi, Josephine Sullivan
Proc. CVPR 2014
https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Kazemi_One_Millisecond_Face_2014_CVPR_paper.pdf

Privacy

When collecting the data, we were careful to only include photos that – to the best of our knowledge – were intended for free use and redistribution by their respective authors. That said, we are committed to protecting the privacy of individuals who do not wish their photos to be included.

To find out whether your photo is included in the Flickr-Faces-HQ dataset, please click this link to search the dataset with your Flickr username.

To get your photo removed from the Flickr-Faces-HQ dataset:

  1. Go to Flickr and do one of the following:
    • Tag the photo with no_cv to indicate that you do not wish it to be used for computer vision research.
    • Change the license of the photo to None (All rights reserved) or any Creative Commons license with NoDerivs to indicate that you do not want it to be redistributed.
    • Make the photo private, i.e., only visible to you and your friends/family.
    • Get the photo removed from Flickr altogether.
  2. Contact [email protected]. Please include your Flickr username in the email.
  3. We will check the status of all photos from the particular user and update the dataset accordingly.
Owner
NVIDIA Research Projects
NVIDIA Research Projects
A whale detector design for the Kaggle whale-detector challenge!

CNN (InceptionV1) + STFT based Whale Detection Algorithm So, this repository is my PyTorch solution for the Kaggle whale-detection challenge. The obje

Tarin Ziyaee 92 Sep 28, 2021
U-Time: A Fully Convolutional Network for Time Series Segmentation

U-Time & U-Sleep Official implementation of The U-Time [1] model for general-purpose time-series segmentation. The U-Sleep [2] model for resilient hig

Mathias Perslev 176 Dec 19, 2022
Code for the paper BERT might be Overkill: A Tiny but Effective Biomedical Entity Linker based on Residual Convolutional Neural Networks

Biomedical Entity Linking This repo provides the code for the paper BERT might be Overkill: A Tiny but Effective Biomedical Entity Linker based on Res

Tuan Manh Lai 24 Oct 24, 2022
Torchlight2 lan game server tool - A message forwarding tool for Torchlight 2 lan game

Torchlight 2 Lan Game Server Tool A message forwarding tool for Torchlight 2 lan

Huaijun Jiang 3 Nov 01, 2022
[SIGGRAPH Asia 2019] Artistic Glyph Image Synthesis via One-Stage Few-Shot Learning

AGIS-Net Introduction This is the official PyTorch implementation of the Artistic Glyph Image Synthesis via One-Stage Few-Shot Learning. paper | suppl

Yue Gao 102 Jan 02, 2023
Implementation of paper "Self-supervised Learning on Graphs:Deep Insights and New Directions"

SelfTask-GNN A PyTorch implementation of "Self-supervised Learning on Graphs: Deep Insights and New Directions". [paper] In this paper, we first deepe

Wei Jin 85 Oct 13, 2022
Happywhale - Whale and Dolphin Identification Silver🥈 Solution (26/1588)

Kaggle-Happywhale Happywhale - Whale and Dolphin Identification Silver 🥈 Solution (26/1588) 竞赛方案思路 图像数据预处理-标志性特征图片裁剪:首先根据开源的标注数据训练YOLOv5x6目标检测模型,将训练集

Franxx 20 Nov 14, 2022
The code for our paper "NSP-BERT: A Prompt-based Zero-Shot Learner Through an Original Pre-training Task —— Next Sentence Prediction"

The code for our paper "NSP-BERT: A Prompt-based Zero-Shot Learner Through an Original Pre-training Task —— Next Sentence Prediction"

Sun Yi 201 Nov 21, 2022
Code for CPM-2 Pre-Train

CPM-2 Pre-Train Pre-train CPM-2 此分支为110亿非 MoE 模型的预训练代码,MoE 模型的预训练代码请切换到 moe 分支 CPM-2技术报告请参考link。 0 模型下载 请在智源资源下载页面进行申请,文件介绍如下: 文件名 描述 参数大小 100000.tar

Tsinghua AI 136 Dec 28, 2022
Mahadi-Now - This Is Pakistani Just Now Login Tools

PAKISTANI JUST NOW LOGIN TOOLS Install apt update apt upgrade apt install python

MAHADI HASAN AFRIDI 19 Apr 06, 2022
The implementation of "Optimizing Shoulder to Shoulder: A Coordinated Sub-Band Fusion Model for Real-Time Full-Band Speech Enhancement"

SF-Net for fullband SE This is the repo of the manuscript "Optimizing Shoulder to Shoulder: A Coordinated Sub-Band Fusion Model for Real-Time Full-Ban

Guochen Yu 36 Dec 02, 2022
Offical implementation for "Trash or Treasure? An Interactive Dual-Stream Strategy for Single Image Reflection Separation".

Trash or Treasure? An Interactive Dual-Stream Strategy for Single Image Reflection Separation (NeurIPS 2021) by Qiming Hu, Xiaojie Guo. Dependencies P

Qiming Hu 31 Dec 20, 2022
Official Implementation of CVPR 2022 paper: "Mimicking the Oracle: An Initial Phase Decorrelation Approach for Class Incremental Learning"

(CVPR 2022) Mimicking the Oracle: An Initial Phase Decorrelation Approach for Class Incremental Learning ArXiv This repo contains Official Implementat

Yujun Shi 24 Nov 01, 2022
S2s2net - Sentinel-2 Super-Resolution Segmentation Network

S2S2Net Sentinel-2 Super-Resolution Segmentation Network Getting started Install

Wei Ji 10 Nov 10, 2022
CLUES: Few-Shot Learning Evaluation in Natural Language Understanding

CLUES: Few-Shot Learning Evaluation in Natural Language Understanding This repo contains the data and source code for baseline models in the NeurIPS 2

Microsoft 29 Dec 29, 2022
Code for testing convergence rates of Lipschitz learning on graphs

📈 LipschitzLearningRates The code in this repository reproduces the experimental results on convergence rates for k-nearest neighbor graph infinity L

2 Dec 20, 2021
CVAT is free, online, interactive video and image annotation tool for computer vision

Computer Vision Annotation Tool (CVAT) CVAT is free, online, interactive video and image annotation tool for computer vision. It is being used by our

OpenVINO Toolkit 8.6k Jan 04, 2023
This folder contains the implementation of the multi-relational attribute propagation algorithm.

MrAP This folder contains the implementation of the multi-relational attribute propagation algorithm. It requires the package pytorch-scatter. Please

6 Dec 06, 2022
CS_Final_Metal_surface_detection - This is a final project for CoderSchool Machine Learning bootcamp on 29/12/2021.

CS_Final_Metal_surface_detection This is a final project for CoderSchool Machine Learning bootcamp on 29/12/2021. The project is based on the dataset

Cuong Vo 1 Dec 29, 2021
Training code and evaluation benchmarks for the "Self-Supervised Policy Adaptation during Deployment" paper.

Self-Supervised Policy Adaptation during Deployment PyTorch implementation of PAD and evaluation benchmarks from Self-Supervised Policy Adaptation dur

Nicklas Hansen 101 Nov 01, 2022