PyTorch implementation of Face Parsing (based on semantic segmentation)
Network originally based on : Object contour detection with a fully convolutional encoder-decoder network [J. Yang, 2016]
Used for : Generative Face Completion [Y. Li, 2017]
Used Dataset : CelebA-HQ Dataset
The network layer codes(like batch normalization, convolution...) was refer to Context Attention
- Dataset and ground-truth should be located as:
- Training image directory
- 1.jpg
- 2.jpg
- Ground-truth image directory
- 1.png
- 2.png
- Training image directory
- Trained PyTorch model from CelebA-HQ dataset : Here
- Samples of training and ground-truth set : Download
$ cp parser_00100000.pt checkpoints/
$ python seg_inference.py
Then samples are saved in output directory
Trained with 30,000 CelebA-HQ dataset and applied it to LFW and CelebA dataset using that pre-trained model
Python 3.6
+ Pytorch 1.1