FFHQ Ageing Dataset

Overview

"FFHQ Ageing" is a dataset of human faces designed by NVidia for benchmarking age transformation algorithms as well as many other possible vision tasks.

This dataset is an extention of the NVidia FFHQ dataset, on top of the 70,000 original FFHQ images, it also contains the following information for each image:

1. Gender information (male/female with confidence score)
2. Age group information (10 classes with confidence score)
3. Head pose (pitch, roll & yaw)
4. Glasses type (none, normal or dark)
5. Eye occlusion score (0-100, different score for each eye)
6. Full semantic map (19 classes, based on CelebAMask-HQ labels)

Dataset Statistics

The following histogram shows the age class distribution per gender.

Gender labels & confidence, age class labels & confidence score, head pose, glasses type and left & right eye occlusion scores for each individual image are stored in ffhq_aging_labels.csv.

Pre-Requisites

You must have a GPU with CUDA support in order to run the segmentation code.

This code requires PyTorch to be installed, please go to Pytorch.org for installation info.
In addition, the following python packages should be installed:

1. requests
2. pillow
3. numpy
4. scipy
5. PyDrive

If any of these packages are not installed on your computer, you can install them using the supplied requirements.txt file:
pip install -r requirements.txt

Note for Windows users: make sure that you have a 64bit python version installed. Otherwise you might get a memory error when reading the FFHQ JSON file.

Usage

Default download method

To download the dataset in the default resolution (256x256) run:
Linux & Mac: ./get_ffhq_aging.sh
Windows: get_ffhq_aging.bat

If you encounter a "quota exceeded" error, see Downloading with PyDrive

Downloading with PyDrive

Google Drive enforces a quota on file download by anonymous users. If you encounter a "quota exceeded" error, either wait 24 hours for the quota limit to reset and try again, or follow the procedure below.

Step 1: Add the original FFHQ dataset to the "Shared With Me" section of your Google Drive

Note: this step does not count against your Google Drive storage limit.

• Login to your Google Drive
• Visit ffhq-dataset

Step 2: Enable the Google Drive API

Note: this only applies to your download script, and does not give access to other users. Nevertheless, we recommend revoking the script's access after the download is complete.

• Go to : https://developers.google.com/drive/api/v3/quickstart/python
• Click on enable drive API
• Select Desktop app
• Download client configuration
• Rename this file to client_secrets.json and place it in the same folder as the download script (download_ffhq_aging.py).

Step 3: Run the script

• In order to run the code with authntication, edit the get_ffhq_aging.sh/bat script, and add the --pydrive flag when invoking download_ffhq_aging.py. This will open a browser authentication window. Log in to your account and allow access.
• If you have no display (like when running from a remote compute server), edit the get_ffhq_aging.sh/bat script, and also add the --cmd_auth flag when invoking download_ffhq_aging.py. This will print a Google authentication link to the screen. Open the link in any browser, allow access, and paste the Google authentication token back to the command line.

Please Note: Using this will let the code access your Google Drive, which might pose a security risk. I recommend using it only in cases when the default interface consistently returns a quota exceeded error. In addition, I recommend to disable the Drive API and delete client_secrets.json after the dataset download is complete.

Optional Arguments

download_ffhq_aging.py

  --debug              run in debug mode, download 50 random images (default: False)
  --pydrive            use pydrive interface to download files. It can override google drive quota limitation
                       this requires google credentials (default: False)
  --cmd_auth           use command line google authentication when using pydrive interface
                       this is good when running on a server with no display (default: False)
  --resolution         final resolution of saved images (default: 256)
  --num_threads NUM    number of concurrent download threads (default: 32)
  --num_attempts NUM   number of download attempts per file (default: 10)

run_deeplab.py

  --resolution         segmentation output size (default: 256)
  --workers            number of data loading workers (default: 4)

Please make sure that the --resolution option for both scripts is the same

Acknowledgements

We wish to thank Thevina Dokka for helping us collecting the dataset.

Original face images were collected in the NVIDIA FFHQ dataset.

A Style-Based Generator Architecture for Generative Adversarial Networks
Tero Karras, Samuli Laine, Timo Aila, CVPR 2019
http://openaccess.thecvf.com/content_CVPR_2019/papers/Karras_A_Style-Based_Generator_Architecture_for_Generative_Adversarial_Networks_CVPR_2019_paper.pdf

Age & gender labels and confidence scores were collected using the Appen platform.

Head pose, glasses type and eye occlusion score were extraceted using the Face++ platform.

Face Semantic maps were acquired by training a pytorch implementation of DeepLabV3 network on the CelebAMASK-HQ dataset.

Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, Hartwig Adam, ECCV 2018
http://openaccess.thecvf.com/content_ECCV_2018/papers/Liang-Chieh_Chen_Encoder-Decoder_with_Atrous_ECCV_2018_paper.pdf

MaskGAN: Towards Diverse and Interactive Facial Image Manipulation
Cheng-Han Lee, Ziwei Liu, Lingyun Wu, Ping Luo, CVPR 2020
https://arxiv.org/pdf/1907.11922.pdf

Credits: Royorel (FFHQ Aging) https://github.com/royorel/FFHQ-Aging-Dataset