A Pytorch Implementation of Joint Adversarial Learning for Domain Adaptation in Semantic Segmentation.
In this project, we use Pytorch 1.1.0 and CUDA version is 10.0.
-
Download the GTA5 Dataset as the source domain, and put it in the
data/GTA5
folder -
Download the Cityscapes Dataset as the target domain, and put it in the
data/Cityscapes
folder
In our experiments, we used two pre-trained models on ImageNet, i.e., VGG16 and ResNet101. Please download these two models from:
bash ./experiments/scripts/jal_train.sh train ./configs/jal/GTA_Citsycapes_vgg16_train.xml
bash ./experiments/scripts/jal_train.sh test ./configs/jal/GTA_Citsycapes_vgg16_test.xml
If you find this repository useful, please cite our paper:
@inproceedings{zhang2020joint,
title={Joint Adversarial Learning for Domain Adaptation in Semantic Segmentation},
author={Zhang, Yixin and Wang, Zilei},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={34},
number={04},
pages={6877--6884},
year={2020}
}