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Towards Online Domain Adaptive Object Detection [WACV 2023]

Framework: PyTorch

Vibashan VS, Poojan Oza, Vishal M Patel

[Project Page] [WACV] [pdf] [BibTeX]

<p align="center"> <img src="imgs/Archi.png" width="800"/> </p>

Contributions

Contents

  1. Installation Instructions
  2. Dataset Preparation
  3. Execution Instructions
  4. Results
  5. Citation

Installation Instructions

conda create -n online_da python=3.6

Conda activate online_da

conda install pytorch==1.9.0 torchvision==0.10.0 torchaudio==0.9.0 cudatoolkit=10.2 -c pytorch

cd online-da
pip install -r requirements.txt

## Make sure you have GCC and G++ version <=8.0
cd ..
python -m pip install -e online-da

Dataset Preparation

Download all the dataset into "./dataset" folder. The codes are written to fit for the format of PASCAL_VOC. For example, the dataset Sim10k is stored as follows.

$ cd ./dataset/Sim10k/VOC2012/
$ ls
Annotations  ImageSets  JPEGImages
$ cat ImageSets/Main/val.txt
3384827.jpg
3384828.jpg
3384829.jpg
.
.

Execution Instructions

Training

CUDA_VISIBLE_DEVICES=$GPU_ID python tools/train_onlineda_net.py \ 
--config-file configs/online_da/onda_foggy.yaml --model-dir ./source_model/cityscape_baseline/model_final.pth

Evaluation

CUDA_VISIBLE_DEVICES=$GPU_ID python tools/plain_test_net.py --eval-only \ 
--config-file configs/online_da/foggy_baseline.yaml --model-dir $PATH TO CHECKPOINT

Results

Citation

If you found Online DA useful in your research, please consider starring ⭐ us on GitHub and citing 📚 us in your research!

@inproceedings{vs2023towards,
  title={Towards Online Domain Adaptive Object Detection},
  author={VS, Vibashan and Oza, Poojan and Patel, Vishal M},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={478--488},
  year={2023}
}

Acknowledgement

We thank the developers and authors of Detectron for releasing their helpful codebases.