README.md

Note: This code is not maintained. Please refer to Peppa_Pig_Face_Landmark, The TRAIN subdir.There is better model.

face_landmark

A simple face aligment method, based on pytorch

introduction

This is the pytorch branch.

It is simple and flexible, trained with wingloss , multi task learning, also with data augmentation based on headpose and face attributes(eyes state and mouth state).

CN blog

And i suggest that you could try with another project,including face detect and keypoints, and some optimizations were made, u can check it there [pappa_pig_face_engine].

Contact me if u have problem about it. 2120140200@mail.nankai.edu.cn :)

demo pictures:

this gif is from github.com/610265158/Peppa_Pig_Face_Engine )

pretrained model:

shufflenetv2_1.0

• baidu disk (code 9x2m)

requirment

• pytorch

• tensorpack (for data provider)

• opencv

• python 3.6

useage

train

1. download all the 300W data set including the 300VW(parse as images, and make the label the same formate as 300W)

├── 300VW
│   ├── 001_annot
│   ├── 002_annot
│       ....
├── 300W
│   ├── 01_Indoor
│   └── 02_Outdoor
├── AFW
│   └── afw
├── HELEN
│   ├── testset
│   └── trainset
├── IBUG
│   └── ibug
├── LFPW
│   ├── testset
│   └── trainset

2. run python make_json.py produce train.json and val.json (if u like train u own data, please read the json produced , it is quite simple)

3. then, run: python train.py

4. by default it trained with shufflenetv2_1.0

visualization

python vis.py --model ./model/keypoints.pth