In this project, we proposed a Domain Disentanglement Faster-RCNN (DDF) for cross-domain object detection, from the view of feature disentanglement.
The implementations are for our paper published in IEEE Transactions on Multimedia:
Decompose to Adapt: Cross-domain Object Detection via Feature Disentanglement
- Python 3+
- Pytorch 1.6.0
- CUDA 11.0
- Cityscapes & Foggy Cityscapes (You can also download the dataset GoogleDrive)
- SIM 10k
- KITTI
We used two pretrained models in our experiments, VGG and ResNet101. You can download these two models from:
Download them and put them into the data/pretrained_model/.
You might need to re-build this repository via:
cd lib
python setup.py build develop
For other detailed settings, please refer to pytorch 1.0 version of repository.
To train the model, please run:
./train_disent.sh
To test the model, please run:
./test_disent.sh
To get the visualization of the feature maps and the feature distance, please refer to:
./test_disent_vis.sh
Please consider citing our papers in your publications if they are helpful to your research:
@article{liu2022decompose,
title={Decompose to Adapt: Cross-domain Object Detection via Feature Disentanglement},
author={Liu, Dongnan and Zhang, Chaoyi and Song, Yang and Huang, Heng and Wang, Chenyu and Barnett, Michael and Cai, Weidong},
journal={IEEE Transactions on Multimedia},
year={2022},
publisher={IEEE}
}
Please contact Dongnan Liu ([email protected]) regarding any issues.
DDF is released under the MIT license. See LICENSE for additional details.