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Using modified BiSeNet for face parsing in PyTorch

License: MIT License

Python 66.62% C 1.14% C++ 11.47% Cuda 20.77%

face-parsing.pytorch's Introduction

face-parsing.PyTorch

Contents

Training

  1. Prepare training data: -- download CelebAMask-HQ dataset

    -- change file path in the prepropess_data.py and run

python prepropess_data.py
  1. Train the model using CelebAMask-HQ dataset: Just run the train script:
    $ CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 train.py

If you do not wish to train the model, you can download our pre-trained model and save it in res/cp.

Demo

  1. Evaluate the trained model using:
# evaluate using GPU
python test.py

Face makeup using parsing maps

face-makeup.PyTorch

ย  Hair Lip
Original Input Original Input Original Input
Color Color Color

References

face-parsing.pytorch's People

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