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 data-driven maritime port simulator

PySeidon - A Data-Driven Maritime Port Simulator 🌊 Extendable and modular software for maritime port simulation. This software uses entity-component

6 Apr 10, 2022
This is the formal code implementation of the CVPR 2022 paper 'Federated Class Incremental Learning'.

Official Pytorch Implementation for GLFC [CVPR-2022] Federated Class-Incremental Learning This is the official implementation code of our paper "Feder

Race Wang 57 Dec 27, 2022
Code for "The Box Size Confidence Bias Harms Your Object Detector"

The Box Size Confidence Bias Harms Your Object Detector - Code Disclaimer: This repository is for research purposes only. It is designed to maintain r

Johannes G. 24 Dec 07, 2022
An implementation of "Optimal Textures: Fast and Robust Texture Synthesis and Style Transfer through Optimal Transport"

Optex An implementation of Optimal Textures: Fast and Robust Texture Synthesis and Style Transfer through Optimal Transport for TU Delft CS4240. You c

Hans Brouwer 33 Jan 05, 2023
Generating synthetic mobility data for a realistic population with RNNs to improve utility and privacy

lbs-data Motivation Location data is collected from the public by private firms via mobile devices. Can this data also be used to serve the public goo

Alex 11 Sep 22, 2022
Bidimensional Leaderboards: Generate and Evaluate Language Hand in Hand

Bidimensional Leaderboards: Generate and Evaluate Language Hand in Hand Introduction We propose a generalization of leaderboards, bidimensional leader

4 Dec 03, 2022
Code for "Searching for Efficient Multi-Stage Vision Transformers"

Searching for Efficient Multi-Stage Vision Transformers This repository contains the official Pytorch implementation of "Searching for Efficient Multi

Yi-Lun Liao 62 Oct 25, 2022
Code release for "Making a Bird AI Expert Work for You and Me".

Making-a-Bird-AI-Expert-Work-for-You-and-Me Code release for "Making a Bird AI Expert Work for You and Me". arxiv (Coming soon...) Changelog 2021/12/6

PRIS-CV: Computer Vision Group 11 Dec 11, 2022
Next-gen Rowhammer fuzzer that uses non-uniform, frequency-based patterns.

Blacksmith Rowhammer Fuzzer This repository provides the code accompanying the paper Blacksmith: Scalable Rowhammering in the Frequency Domain that is

Computer Security Group @ ETH Zurich 173 Nov 16, 2022
Highway networks implemented in PyTorch.

PyTorch Highway Networks Highway networks implemented in PyTorch. Just the MNIST example from PyTorch hacked to work with Highway layers. Todo Make th

Conner Vercellino 56 Dec 14, 2022
Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification

Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification

DingDing 143 Jan 01, 2023
Official code for paper "Optimization for Oriented Object Detection via Representation Invariance Loss".

Optimization for Oriented Object Detection via Representation Invariance Loss By Qi Ming, Zhiqiang Zhou, Lingjuan Miao, Xue Yang, and Yunpeng Dong. Th

ming71 56 Nov 28, 2022
Benchmarks for Object Detection in Aerial Images

Benchmarks for Object Detection in Aerial Images

Jian Ding 691 Dec 30, 2022
StyleGAN2-ADA-training-jupyter - Training custom datasets in styleGAN2-ADA by NVIDIA using Jupyter

styleGAN2-ADA-training-jupyter Training custom datasets in styleGAN2-ADA on Jupyter Official StyleGAN2-ADA by NIVIDIA Paper Training Generative Advers

Mang Su Hyun 2 Feb 24, 2022
Pytorch implementation of MalConv

MalConv-Pytorch A Pytorch implementation of MalConv Desciprtion This is the implementation of MalConv proposed in Malware Detection by Eating a Whole

Alexander H. Liu 58 Oct 26, 2022
Learn about quantum computing and algorithm on quantum computing

quantum_computing this repo contains everything i learn about quantum computing and algorithm on quantum computing what is aquantum computing quantum

arfy slowy 8 Dec 25, 2022
Encoding Causal Macrovariables

Encoding Causal Macrovariables Data Natural climate data ('El Nino') Self-generated data ('Simulated') Experiments Detecting macrovariables through th

Benedikt Höltgen 3 Jul 31, 2022
A demo of how to use JAX to create a simple gravity simulation

JAX Gravity This repo contains a demo of how to use JAX to create a simple gravity simulation. It uses JAX's experimental ode package to solve the dif

Cristian Garcia 16 Sep 22, 2022
FFCV: Fast Forward Computer Vision (and other ML workloads!)

Fast Forward Computer Vision: train models at a fraction of the cost with accele

FFCV 2.3k Jan 03, 2023
A Weakly Supervised Amodal Segmenter with Boundary Uncertainty Estimation

Paper Khoi Nguyen, Sinisa Todorovic "A Weakly Supervised Amodal Segmenter with Boundary Uncertainty Estimation", accepted to ICCV 2021 Our code is mai

Khoi Nguyen 5 Aug 14, 2022