A large-scale face dataset for face parsing, recognition, generation and editing.

Overview

CelebAMask-HQ

[Paper] [Demo]

image

CelebAMask-HQ is a large-scale face image dataset that has 30,000 high-resolution face images selected from the CelebA dataset by following CelebA-HQ. Each image has segmentation mask of facial attributes corresponding to CelebA.

The masks of CelebAMask-HQ were manually-annotated with the size of 512 x 512 and 19 classes including all facial components and accessories such as skin, nose, eyes, eyebrows, ears, mouth, lip, hair, hat, eyeglass, earring, necklace, neck, and cloth.

CelebAMask-HQ can be used to train and evaluate algorithms of face parsing, face recognition, and GANs for face generation and editing.

  • If you need the identity labels and the attribute labels of the images, please send request to the CelebA team.

  • Demo of interactive facial image manipulation

image

Sample Images

image

Face Manipulation Model with CelebAMask-HQ

CelebAMask-HQ can be used on several research fields including: facial image manipulation, face parsing, face recognition, and face hallucination. We showcase an application on interactive facial image manipulation as bellow:

  • Samples of interactive facial image manipulation

image

CelebAMask-HQ Dataset Downloads

Related Works

  • CelebA dataset:
    Ziwei Liu, Ping Luo, Xiaogang Wang and Xiaoou Tang, "Deep Learning Face Attributes in the Wild", in IEEE International Conference on Computer Vision (ICCV), 2015
  • CelebA-HQ was collected from CelebA and further post-processed by the following paper :
    Karras et. al, "Progressive Growing of GANs for Improved Quality, Stability, and Variation", in Internation Conference on Reoresentation Learning (ICLR), 2018

Dataset Agreement

  • The CelebAMask-HQ dataset is available for non-commercial research purposes only.
  • You agree not to reproduce, duplicate, copy, sell, trade, resell or exploit for any commercial purposes, any portion of the images and any portion of derived data.
  • You agree not to further copy, publish or distribute any portion of the CelebAMask-HQ dataset. Except, for internal use at a single site within the same organization it is allowed to make copies of the dataset.

Related Projects using CelebAMask-HQ

License and Citation

The use of this software is RESTRICTED to non-commercial research and educational purposes.

@inproceedings{CelebAMask-HQ,
  title={MaskGAN: Towards Diverse and Interactive Facial Image Manipulation},
  author={Lee, Cheng-Han and Liu, Ziwei and Wu, Lingyun and Luo, Ping},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2020}
}
Owner
switchnorm
Switchable Normalizations
switchnorm
🏎️ Accelerate training and inference of 🤗 Transformers with easy to use hardware optimization tools

Hugging Face Optimum 🤗 Optimum is an extension of 🤗 Transformers, providing a set of performance optimization tools enabling maximum efficiency to t

Hugging Face 842 Dec 30, 2022
Prediction of MBA refinance Index (Mortgage prepayment)

Prediction of MBA refinance Index (Mortgage prepayment) Deep Neural Network based Model The ability to predict mortgage prepayment is of critical use

Ruchil Barya 1 Jan 16, 2022
Official Pytorch implementation of "Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video", CVPR 2021

TCMR: Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video Qualtitative result Paper teaser video Introduction This r

Hongsuk Choi 215 Jan 06, 2023
Creating multimodal multitask models

Fusion Brain Challenge The English version of the document can be found here. Обновления 01.11 Мы выкладываем пример данных, аналогичных private test

Sber AI 43 Nov 28, 2022
RITA is a family of autoregressive protein models, developed by LightOn in collaboration with the OATML group at Oxford and the Debora Marks Lab at Harvard.

RITA: a Study on Scaling Up Generative Protein Sequence Models RITA is a family of autoregressive protein models, developed by a collaboration of Ligh

LightOn 69 Dec 22, 2022
Libtorch yolov3 deepsort

Overview It is for my undergrad thesis in Tsinghua University. There are four modules in the project: Detection: YOLOv3 Tracking: SORT and DeepSORT Pr

Xu Wei 226 Dec 13, 2022
Official Implementation of Few-shot Visual Relationship Co-localization

VRC Official implementation of the Few-shot Visual Relationship Co-localization (ICCV 2021) paper project page | paper Requirements Use python = 3.8.

22 Oct 13, 2022
FaceQgen: Semi-Supervised Deep Learning for Face Image Quality Assessment

FaceQgen FaceQgen: Semi-Supervised Deep Learning for Face Image Quality Assessment This repository is based on the paper: "FaceQgen: Semi-Supervised D

Javier Hernandez-Ortega 3 Aug 04, 2022
🔮 Execution time predictions for deep neural network training iterations across different GPUs.

Habitat: A Runtime-Based Computational Performance Predictor for Deep Neural Network Training Habitat is a tool that predicts a deep neural network's

Geoffrey Yu 44 Dec 27, 2022
TOOD: Task-aligned One-stage Object Detection, ICCV2021 Oral

One-stage object detection is commonly implemented by optimizing two sub-tasks: object classification and localization, using heads with two parallel branches, which might lead to a certain level of

264 Jan 09, 2023
Tensorflow implementation of soft-attention mechanism for video caption generation.

SA-tensorflow Tensorflow implementation of soft-attention mechanism for video caption generation. An example of soft-attention mechanism. The attentio

Paul Chen 153 Nov 14, 2022
Acute ischemic stroke dataset

AISD Acute ischemic stroke dataset contains 397 Non-Contrast-enhanced CT (NCCT) scans of acute ischemic stroke with the interval from symptom onset to

Kongming Liang 21 Sep 06, 2022
Computer Vision Paper Reviews with Key Summary of paper, End to End Code Practice and Jupyter Notebook converted papers

Computer-Vision-Paper-Reviews Computer Vision Paper Reviews with Key Summary along Papers & Codes. Jonathan Choi 2021 The repository provides 100+ Pap

Jonathan Choi 2 Mar 17, 2022
[ICCV 2021] Focal Frequency Loss for Image Reconstruction and Synthesis

Focal Frequency Loss - Official PyTorch Implementation This repository provides the official PyTorch implementation for the following paper: Focal Fre

Liming Jiang 460 Jan 04, 2023
Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology (LMRL Workshop, NeurIPS 2021)

Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology Self-Supervised Vision Transformers Learn Visual Concepts in Histopatholog

Richard Chen 95 Dec 24, 2022
Simulating Sycamore quantum circuits classically using tensor network algorithm.

Simulating the Sycamore quantum supremacy circuit This repo contains data we have obtained in simulating the Sycamore quantum supremacy circuits with

Feng Pan 46 Nov 17, 2022
Transformers4Rec is a flexible and efficient library for sequential and session-based recommendation, available for both PyTorch and Tensorflow.

Transformers4Rec is a flexible and efficient library for sequential and session-based recommendation, available for both PyTorch and Tensorflow.

730 Jan 09, 2023
Official PyTorch Implementation for InfoSwap: Information Bottleneck Disentanglement for Identity Swapping

InfoSwap: Information Bottleneck Disentanglement for Identity Swapping Code usage Please check out the user manual page. Paper Gege Gao, Huaibo Huang,

Grace Hešeri 56 Dec 20, 2022
Source code for PairNorm (ICLR 2020)

PairNorm Official pytorch source code for PairNorm paper (ICLR 2020) This code requires pytorch_geometric=1.3.2 usage For SGC, we use original PairNo

62 Dec 08, 2022
DrNAS: Dirichlet Neural Architecture Search

This paper proposes a novel differentiable architecture search method by formulating it into a distribution learning problem. We treat the continuously relaxed architecture mixing weight as random va

Xiangning Chen 37 Jan 03, 2023