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https://github.com/HRNet/HRNet-Facial-Landmark-Detection

This is an official implementation of facial landmark detection for our TPAMI paper "Deep High-Resolution Representation Learning for Visual Recognition". https://arxiv.org/abs/1908.07919
https://github.com/HRNet/HRNet-Facial-Landmark-Detection

deep-high-resolution-net face-alignment facealignment facial-landmarks hrnets

Last synced: about 17 hours ago
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This is an official implementation of facial landmark detection for our TPAMI paper "Deep High-Resolution Representation Learning for Visual Recognition". https://arxiv.org/abs/1908.07919

Host: GitHub
URL: https://github.com/HRNet/HRNet-Facial-Landmark-Detection
Owner: HRNet
License: mit
Created: 2019-04-09T13:25:44.000Z (over 5 years ago)
Default Branch: master
Last Pushed: 2022-08-12T08:03:02.000Z (about 2 years ago)
Last Synced: 2024-08-03T02:09:54.058Z (3 months ago)
Topics: deep-high-resolution-net, face-alignment, facealignment, facial-landmarks, hrnets
Language: Python
Homepage:
Size: 3.53 MB
Stars: 1,031
Watchers: 31
Forks: 261
Open Issues: 66
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

awesome-face-related-list - HRNets - Interspecies-Landmark-Detection](https://github.com/kingsj0405/HRNet-Interspecies-Landmark-Detection). (Face Perception / Facial Landmark)

README

        # High-resolution networks (HRNets) for facial landmark detection

## News

- [2020/03/13] Our paper is accepted by TPAMI: [Deep High-Resolution Representation Learning for Visual Recognition](https://arxiv.org/pdf/1908.07919.pdf).

## Introduction 

This is the official code of [High-Resolution Representations for Facial Landmark Detection](https://arxiv.org/pdf/1904.04514.pdf). 

We extend the high-resolution representation (HRNet) [1] by augmenting the high-resolution representation by aggregating the (upsampled) 

representations from all the parallel convolutions, leading to stronger representations. The output representations are fed into

classifier. We evaluate our methods on four datasets, COFW, AFLW, WFLW and 300W.



![](images/hrnet.jpg)



## Performance

### ImageNet pretrained models

HRNetV2 ImageNet pretrained models are now available! Codes and pretrained models are in [HRNets for Image Classification](https://github.com/HRNet/HRNet-Image-Classification)

We adopt **HRNetV2-W18**(#Params=9.3M, GFLOPs=4.3G) for facial landmark detection on COFW, AFLW, WFLW and 300W.

### COFW

The model is trained on COFW *train* and evaluated on COFW *test*.

| Model | NME | FR_0.1|pretrained model|model|

|:--:|:--:|:--:|:--:|:--:|

|HRNetV2-W18  | 3.45 | 0.20 | [HRNetV2-W18](https://1drv.ms/u/s!Aus8VCZ_C_33cMkPimlmClRvmpw) | [HR18-COFW.pth](https://1drv.ms/u/s!AiWjZ1LamlxzdFIsEUQl8jgUaMk)|

### AFLW

The model is trained on AFLW *train* and evaluated on AFLW *full* and *frontal*.

| Model | NME_*full* | NME_*frontal* | pretrained model|model|

|:--:|:--:|:--:|:--:|:--:|

|HRNetV2-W18 | 1.57 | 1.46 | [HRNetV2-W18](https://1drv.ms/u/s!Aus8VCZ_C_33cMkPimlmClRvmpw) | [HR18-AFLW.pth](https://1drv.ms/u/s!AiWjZ1Lamlxzc7xumEw810iBLTc)|

### WFLW

| NME |  *test* | *pose* | *illumination* | *occlution* | *blur* | *makeup* | *expression* | pretrained model|model|

|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|

|HRNetV2-W18 | 4.60 | 7.86 | 4.57 | 5.42 | 5.36 | 4.26 | 4.78 | [HRNetV2-W18](https://1drv.ms/u/s!Aus8VCZ_C_33cMkPimlmClRvmpw) | [HR18-WFLW.pth](https://1drv.ms/u/s!AiWjZ1LamlxzdTsr_9QZCwJsn5U)|

### 300W

| NME | *common*| *challenge* | *full* | *test*|  pretrained model|model|

|:--:|:--:|:--:|:--:|:--:|:--:|:--:|

|HRNetV2-W18 | 2.91 | 5.11 | 3.34 | 3.85 | [HRNetV2-W18](https://1drv.ms/u/s!Aus8VCZ_C_33cMkPimlmClRvmpw) | [HR18-300W.pth](https://1drv.ms/u/s!AiWjZ1LamlxzeYLmza1XU-4WhnQ)|

![](images/face.png)

## Quick start

#### Environment

This code is developed using on Python 3.6 and PyTorch 1.0.0 on Ubuntu 16.04 with NVIDIA GPUs. Training and testing are 

performed using 1 NVIDIA P40 GPU with CUDA 9.0 and cuDNN 7.0. Other platforms or GPUs are not fully tested.

#### Install

1. Install PyTorch 1.0 following the [official instructions](https://pytorch.org/)

2. Install dependencies

````bash

pip install -r requirements.txt

````

3. Clone the project

````bash 

git clone https://github.com/HRNet/HRNet-Facial-Landmark-Detection.git

````

#### HRNetV2 pretrained models

```bash

cd HRNet-Facial-Landmark-Detection

# Download pretrained models into this folder

mkdir hrnetv2_pretrained

```

#### Data

1. You need to download the annotations files which have been processed from [OneDrive](https://1drv.ms/u/s!AiWjZ1LamlxzdmYbSkHpPYhI8Ms), [Cloudstor](https://cloudstor.aarnet.edu.au/plus/s/m9lHU2aJId8Sh8l), and [BaiduYun(Acess Code:ypxg)](https://pan.baidu.com/s/1Yg1IEp3l2IpGPolpUsWdfg).

2. You need to download images (300W, AFLW, WFLW) from official websites and then put them into `images` folder for each dataset.

Your `data` directory should look like this:

````

HRNet-Facial-Landmark-Detection

-- lib

-- experiments

-- tools

-- data

   |-- 300w

   |   |-- face_landmarks_300w_test.csv

   |   |-- face_landmarks_300w_train.csv

   |   |-- face_landmarks_300w_valid.csv

   |   |-- face_landmarks_300w_valid_challenge.csv

   |   |-- face_landmarks_300w_valid_common.csv

   |   |-- images

   |-- aflw

   |   |-- face_landmarks_aflw_test.csv

   |   |-- face_landmarks_aflw_test_frontal.csv

   |   |-- face_landmarks_aflw_train.csv

   |   |-- images

   |-- cofw

   |   |-- COFW_test_color.mat

   |   |-- COFW_train_color.mat  

   |-- wflw

   |   |-- face_landmarks_wflw_test.csv

   |   |-- face_landmarks_wflw_test_blur.csv

   |   |-- face_landmarks_wflw_test_expression.csv

   |   |-- face_landmarks_wflw_test_illumination.csv

   |   |-- face_landmarks_wflw_test_largepose.csv

   |   |-- face_landmarks_wflw_test_makeup.csv

   |   |-- face_landmarks_wflw_test_occlusion.csv

   |   |-- face_landmarks_wflw_train.csv

   |   |-- images

````

#### Train

Please specify the configuration file in `experiments` (learning rate should be adjusted when the number of GPUs is changed).

````bash

python tools/train.py --cfg 

# example:

python tools/train.py --cfg experiments/wflw/face_alignment_wflw_hrnet_w18.yaml

````

#### Test

````bash

python tools/test.py --cfg  --model-file  

# example:

python tools/test.py --cfg experiments/wflw/face_alignment_wflw_hrnet_w18.yaml --model-file HR18-WFLW.pth

````

 

## Other applications of HRNets (codes and models):

* [Human pose estimation](https://github.com/leoxiaobin/deep-high-resolution-net.pytorch)

* [Semantic segmentation](https://github.com/HRNet/HRNet-Semantic-Segmentation)

* [Object detection](https://github.com/HRNet/HRNet-Object-Detection)

* [Image classification](https://github.com/HRNet/HRNet-Image-Classification)

 

## Citation

If you find this work or code is helpful in your research, please cite:

````

@inproceedings{SunXLW19,

  title={Deep High-Resolution Representation Learning for Human Pose Estimation},

  author={Ke Sun and Bin Xiao and Dong Liu and Jingdong Wang},

  booktitle={CVPR},

  year={2019}

}

@article{WangSCJDZLMTWLX19,

  title={Deep High-Resolution Representation Learning for Visual Recognition},

  author={Jingdong Wang and Ke Sun and Tianheng Cheng and 

          Borui Jiang and Chaorui Deng and Yang Zhao and Dong Liu and Yadong Mu and 

          Mingkui Tan and Xinggang Wang and Wenyu Liu and Bin Xiao},

  journal   = {TPAMI}

  year={2019}

}

````

## Reference

[1] Deep High-Resolution Representation Learning for Visual Recognition. Jingdong Wang, Ke Sun, Tianheng Cheng, 

    Borui Jiang, Chaorui Deng, Yang Zhao, Dong Liu, Yadong Mu, Mingkui Tan, Xinggang Wang, Wenyu Liu, Bin Xiao. Accepted by TPAMI.  [download](https://arxiv.org/pdf/1908.07919.pdf)