Face parsing

A Pytorch implementation face parsing model trained by CelebAMask-HQ

Dependencies

• Pytorch 0.4.1
• numpy
• Python3
• Pillow
• opencv-python
• tenseorboardX

Preprocessing

• Move the mask folder, the image folder, and CelebA-HQ-to-CelebA-mapping.txt ( remove 1st line in advance ) under ./Data_preprocessing
• Run python g_mask.py
• Run python g_partition.py to split train set and test set.

Training

• Run bash run.sh #GPU_num

Well-trained model

• The model can be downloaded here.
• The model (model.pth) should be put under ./models/parsenet
• Mask labels are defined as following:

Label list
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: 93.42 ( train and evaluate according to CelebA train/test split )

Testing & Color visualization

• Run bash run_test.sh #GPU_num
• Results will be saved in ./test_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