This respository includes a PyTorch implementation of CE2P that won the 1st places of single human parsing in the 2nd LIP Challenge. The M-CE2P used for multiple human parsing is provided in https://github.com/RanTaimu/M-CE2P.

The code is based upon https://github.com/speedinghzl/pytorch-segmentation-toolbox, and the data processing is based upon https://github.com/Microsoft/human-pose-estimation.pytorch

python 3.6

PyTorch 0.4.1

To install PyTorch, please refer to https://github.com/pytorch/pytorch#installation.

Or using anaconda: conda env create -f environment.yaml

Or to use Pytorch 1.0, just replace 'libs' with 'modules' in https://github.com/mapillary/inplace_abn, and rename it to 'libs'.

Some parts of InPlace-ABN have a native CUDA implementation, which must be compiled with the following commands:

cd libs
sh build.sh
python build.py

The build.sh script assumes that the nvcc compiler is available in the current system search path. The CUDA kernels are compiled for sm_50, sm_52 and sm_61 by default. To change this (e.g. if you are using a Kepler GPU), please edit the CUDA_GENCODE variable in build.sh.

Note that the left and right label should be swapped when the label file is flipped.

Plesae download LIP dataset and create symbolic links: ln -s YOUR_LIP_DATASET_DIR dataset/LIP

The contents of LIP Dataset include:

├── train_images

├── train_segmentations

├── val_images

├── val_segmentations

├── test_images

├── train_id.txt

├── val_id.txt

├── test_id.txt

Please download imagenet pretrained resent-101 from baidu drive or Google drive, and put it into dataset folder.

./run.sh

To evaluate the results, please download 'LIP_epoch_149.pth' from baidu drive or Google drive, and put into snapshots directory.

./run_evaluate.sh

The parsing result of the provided 'LIP_epoch_149.pth' is 53.88 without any bells and whistles,

If this code is helpful for your research, please cite the following paper:

@inproceedings{ruan2019devil,
  title={Devil in the details: Towards accurate single and multiple human parsing},
  author={Ruan, Tao and Liu, Ting and Huang, Zilong and Wei, Yunchao and Wei, Shikui and Zhao, Yao},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={33},
  pages={4814--4821},
  year={2019}
}