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Towards Better Stability and Adaptability: Improve Online Self-Training for Model Adaptation in Semantic Segmentation(CVPR-2023)
This is a pytorch implementation of DT-ST.
(CVPR-2023-highlight paper-top 2.5%)
Prerequisites
Python 3.6
Pytorch 1.2.0
torchvision from master
yacs
matplotlib
GCC >= 4.9
OpenCV
CUDA >= 9.0
Step-by-step installation
conda create --name dtst -y python=3.6
conda activate dtst
# this installs the right pip and dependencies for the fresh python
conda install -y ipython pip
pip install ninja yacs cython matplotlib tqdm opencv-python imageio mmcv
# follow PyTorch installation in https://pytorch.org/get-started/locally/# we give the instructions for CUDA 9.2
conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=9.2 -c pytorch
Some codes are adapted from FADA, SAC and DSU. We thank them for their excellent projects.
Citation
If you find this code useful please consider citing
@inproceedings{zhao2023towards,
title={Towards Better Stability and Adaptability: Improve Online Self-Training for Model Adaptation in Semantic Segmentation},
author={Zhao, Dong and Wang, Shuang and Zang, Qi and Quan, Dou and Ye, Xiutiao and Jiao, Licheng},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={11733--11743},
year={2023}
}
About
Towards Better Stability and Adaptability: Improve Online Self-Training for Model Adaptation in Semantic Segmentation(CVPR-2023)