Official Pytorch implementation of 'RoI Tanh-polar Transformer Network for Face Parsing in the Wild.'

Related tags

Deep Learning pytorch face-parsing
Overview

ibug.face_parsing

RoI Tanh-polar Transformer Network for Face Parsing in the Wild.

Note: If you use this repository in your research, we kindly rquest you to cite the following paper:

@article{lin2021roi,
title = {RoI Tanh-polar transformer network for face parsing in the wild},
journal = {Image and Vision Computing},
volume = {112},
pages = {104190},
year = {2021},
issn = {0262-8856},
doi = {https://doi.org/10.1016/j.imavis.2021.104190},
url = {https://www.sciencedirect.com/science/article/pii/S0262885621000950},
author = {Yiming Lin and Jie Shen and Yujiang Wang and Maja Pantic},
keywords = {Face parsing, In-the-wild dataset, Head pose augmentation, Tanh-polar representation},
}

Dependencies

How to Install

git clone https://github.com/hhj1897/face_parsing
cd face_parsing
git lfs pull
pip install -e .

How to Test

python face_warping_test.py -i 0 -e rtnet50 --decoder fcn -n 11 -d cuda:0

Command-line arguments:

-i VIDEO: Index of the webcam to use (start from 0) or
          path of the input video file
-d: Device to be used by PyTorch (default=cuda:0)
-e: Encoder (default=rtnet50)
--decoder: Decoder (default=fcn)
-n: Number of facial classes, can be 11 or 14 for now (default=11)

iBugMask Dataset

The training and testing images, bounding boxes, landmarks, and parsing maps can be found in the following:

Label Maps

Label map for 11 classes:

0 : background
1 : skin (including face and scalp)
2 : left_eyebrow
3 : right_eyebrow
4 : left_eye
5 : right_eye
6 : nose
7 : upper_lip
8 : inner_mouth
9 : lower_lip
10 : hair

Label map for 14 classes:

0 : background
1 : skin (including face and scalp)
2 : left_eyebrow
3 : right_eyebrow
4 : left_eye
5 : right_eye
6 : nose
7 : upper_lip
8 : inner_mouth
9 : lower_lip
10 : hair
11 : left_ear
12 : right_ear
13 : glasses

Visualisation

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Comments
  • cannot convert to tflite

    cannot convert to tflite

    Hello, thanks for sharing this great study. I'm researching face parsing and i'm trying to port to Tflite and compare the performance, but I can't since this is using special ops - it uses "grid sample"

    What do you suggest I can do in order to test on Tflite/CoreML?

    Will training on Lapa dataset improve the accuracy? if not why?

    opened by ofirkris 1
  • _pickle.UnpicklingError: invalid load key, 'v'

    _pickle.UnpicklingError: invalid load key, 'v'

    How to fix it

    Traceback (most recent call last): File "face_parsing_test.py", line 141, in main() File "face_parsing_test.py", line 50, in main face_parser = RTNetPredictor( File "/home/ml/radishevskii/face_parsing/ibug/face_parsing/parser.py", line 81, in init ckpt = torch.load(ckpt, 'cpu') File "/home/ml/radishevskii/anaconda3/envs/inga_vlad/lib/python3.8/site-packages/torch/serialization.py", line 593, in load return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args) File "/home/ml/radishevskii/anaconda3/envs/inga_vlad/lib/python3.8/site-packages/torch/serialization.py", line 762, in _legacy_load magic_number = pickle_module.load(f, **pickle_load_args) _pickle.UnpicklingError: invalid load key, 'v'.

    opened by vladradishevsky 1
  • face parsing label

    face parsing label

    It seems that the dataset released contains only the annotation of 11 facial parts. However, the repository also provide the model trained with dataset containing labels of 14 facial parts. Thus, we wonder how can we get the labels of 14 facial parts. Can you provide the download link? Thanks!

    opened by HowToNameMe 0
Releases(v0.2.0)
Owner
Jie Shen
Jie Shen
GitHub
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