PyTorch evaluation code and pretrained models for SLPT (Sparse Local Patch Transformer).

Install python dependencies:

pip3 install -r requirements.txt

1. Download and process WFLW dataset

   • Download WFLW dataset and annotation from Here.
   • Unzip WFLW dataset and annotations and move files into ./dataset directory. Your directory should look like this:

     SLPT
     └───Dataset
        │
        └───WFLW
           │
           └───WFLW_annotations
           │   └───list_98pt_rect_attr_train_test
           │   │
           │   └───list_98pt_test
           └───WFLW_images
               └───0--Parade
               │
               └───...
     

2. Download pretrained model from Google Drive.

   • WFLW

   	Model Name	NME (%)	FR0.1 (%)	AUC0.1	download link
   1	SLPT-6-layers	4.143	2.760	0.595	download
   2	SLPT-12-layers	4.128	2.720	0.596	download

   Put the model in ./Weight directory.

3. Test

   python test.py --checkpoint=<model_name>
   For example: python test.py --checkpoint=WFLW_6_layer.pth
   

   Note: if you want to use the model with 12 layers, you need to change _C.TRANSFORMER.NUM_DECODER for 6 to 12 in ./Config/default.py.

We also provide a video demo script.

1. Download face detector, copy the weight yunet_final.pth to ./Weight/Face_Detector/
2. python Camera.py --video_source=<Video Path>

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

@inproceedings{SLPT,
  title={Sparse Local Patch Transformer for Robust Face Alignment and Landmarks},
  author={Jiahao Xia and Weiwei Qu and Wenjian Huang and Jianguo Zhang and Xi Wang and Min Xu},
  booktitle={CVPR},
  year={2022}
}

SLPT is released under the GPL-2.0 license. Please see the LICENSE file for more information.

• This repository borrows or partially modifies the models from HRNet and DETR
• The video demo employs the libfacedetection as the face detector.
• The test videos are provided by DFEW