AIParsing
This respository includes a PyTorch implementation of the TIP2022 paper AIParsing:Anchor-Free Instance-Level Human Parsing.
Requirements:
python 3.7
PyTorch 1.7.1
cuda 10.1
The detail environment can be find in AIParsing_env.yaml.
Compiling
Apex install:
git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
or python setup.py build develop
cd models
sh make.sh
cd ..
python setup.py build develop
Dataset and pretrained model
Plesae download CIHP dataset
Well-trained models on the CIHP and LV-MHP datasets (MM:aiparsing)
Evaluation
bash test_CIHP_R50_75epoch.sh
Training
bash train_CIHP_R50_75epoch.sh
Acknowledgment
This project is created based on the Parsing R-CNN, CenterMask
If this code is helpful for your research, please cite the following paper:
@article{AIParsing2022,
title={AIParsing: Anchor-Free Instance-Level Human Parsing},
author={Sanyi Zhang, Xiaochun Cao, Guo-jun Qi, Zhanjie Song, Jie Zhou},
journal={IEEE Transactions on Image Processing (TIP)},
year={2022},
volume={31},
pages={5599-5612}
}