This repo is the official implementation of ICCV 2021 paper "Multi-scale Matching Networks for Semantic Correspondence.".
conda create -n mmnet python==3.8.0
conda activate mmnet
conda install torch==1.8.1 torchvision==0.9.1
pip install matplotlib scikit-image pandasfor installation of gluoncvth (fcn-resnet101):
git clone https://github.com/StacyYang/gluoncv-torch.git
cd gluoncv-torch
python setup.py installpython train.py --seed 0 --lr 0.0005
Trained models are available on [google drive].
pascal with fcn-resnet101 backbone(PCK@0.05:81.6%):
python test.py --alpha 0.05 --backbone fcn-resnet101 --ckp_name path\to\ckp_pascal_fcnres101.pth --resize 224,320spair with fcn-resnet101 backbone(PCK@0.1):
python test.py --alpha 0.05 --benchmark spair --backbone fcn-resnet101 --ckp_name path\to\ckp_spair_fcnres101.pth --resize 224,320If you use this code for your research, please consider citing:
@article{zhao2021multi,
title={Multi-scale Matching Networks for Semantic Correspondence},
author={Zhao, Dongyang and Song, Ziyang and Ji, Zhenghao and Zhao, Gangming and Ge, Weifeng and Yu, Yizhou},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
year={2021}
}