Face parsing

A Pytorch implementation face parsing model trained by CelebAMask-HQ, based on EHANet.

Dependencies

• Pytorch 1.7.1
• numpy
• Python3
• Pillow
• opencv-python
• tenseorboardX
• pandas
• (Optional) inplace_abn

Preprocessing

• Prepare training data: -- download CelebAMask-HQ dataset
• Move the mask folder, the image folder, and CelebA-HQ-to-CelebA-mapping.txt under ./Data_preprocessing
• Run python g_mask.py to merge separate labels. Support multiprocess, use python g_mask.py --num_process 4 for 4 processes.
• Run python g_partition.py to split train set and test set.

Training

• Run bash run_train.sh #GPU_USE_INDEX, for example, bash run_train.sh 0.
• Add --parallel True in bash script if you want to use multi-GPU for training.

Well-trained model

• The model can be downloaded here (96M).
• The model (#num.pth) should be put under ./models/FaceParseNet50/
• Mask labels are defined as following:

-	Label list (19 classes)	-
0: 'background'	1: 'skin'	2: 'nose'
3: 'eye_g'	4: 'l_eye'	5: 'r_eye'
6: 'l_brow'	7: 'r_brow'	8: 'l_ear'
9: 'r_ear'	10: 'mouth'	11: 'u_lip'
12: 'l_lip'	13: 'hair'	14: 'hat'
15: 'ear_r'	16: 'neck_l'	17: 'neck'
18: 'cloth'	-	-

• Overall Per-pixel Acc: 94.26;
• Mean IoU: 76.64;
• Overall F1 Score: 85.33.
• Note: train and evaluate according to CelebA train/test split.

Testing & Color visualization

• Run bash run_test.sh #GPU_num #Model_num, for example, bash run_test.sh 0 32, which uses epoch 32 model file (32_G.pth).
• Pred results will be saved in ./test_pred_results
• Color visualized results will be saved in ./test_color_visualize
• Another way for color visualization without using GPU: Run python ./Data_preprocessing/g_color.py

Visual Results

• OriginalImg vs. GroundTruth vs. PredResult

References

@article{CelebAMask-HQ,
  title={MaskGAN: Towards Diverse and Interactive Facial Image Manipulation},
  author={Lee, Cheng-Han and Liu, Ziwei and Wu, Lingyun and Luo, Ping},
  journal={arXiv preprint arXiv:1907.11922},
  year={2019},
  website={https://github.com/switchablenorms/CelebAMask-HQ}
}
@article{luo2020ehanet,
  title={EHANet: An Effective Hierarchical Aggregation Network for Face Parsing},
  author={Luo, Ling and Xue, Dingyu and Feng, Xinglong},
  journal={Applied Sciences},
  volume={10},
  number={9},
  pages={3135},
  year={2020},
  publisher={Multidisciplinary Digital Publishing Institute},
  website={https://github.com/JACKYLUO1991/FaceParsing}
}
@code{face-parsing.PyTorch,
  author={zllrunning},
  year={2019},
  website={https://github.com/zllrunning/face-parsing.PyTorch}
}