OA-DG: Object-Aware Domain Generalization

July 29, 2025 · View on GitHub

OA-DG is an effective method for single-domain object detection generalization (S-DGOD). It consists of two components: OA-Mix for data augmentation and OA-Loss for reducing domain gaps.

oadg_introduction

Object-Aware Domain Generalization for Object Detection, Wooju Lee^* , Dasol Hong^* , Hyungtae Lim^†, and Hyun Myung^†, AAAI 2024 (arXiv:2312.12133)

✨Features

OA-DG consists of OA-Mix for data augmentation and OA-Loss for reducing the domain gap.
OA-Mix increases image diversity while preserving important semantic feature with multi-level transformations and object-aware mixing.

👀 View some example images
OA-Loss reduces the domain gap by training semantic relations of foreground and background instances from multi-domain.
Extensive experiments on standard benchmarks (Cityscapes-C and Diverse Weather Dataset) show that OA-DG outperforms SOTA methods on unseen target domains.
OA-DG can be generally applied to improve robustness regardless of the augmentation set and object detector architectures.

🚣 Getting Started

Follow these steps to set up the project on your local machine for training and testing.

Prerequisites

Ensure you have the following prerequisites installed on your local system.

Install mmdetection: There are several installation guides. Follow one of the below:

Our code is forked from mmdetection 2.28.x version.

a. Customize Installation (recommended)
```
# Install MMCV using MIM.

$ pip install -U openmim
$ mim install mmcv-full

# Clone this repository
$ git clone https://github.com/WoojuLee24/OA-DG.git

# Go into the repository
$ cd OA-DG

# Install mmdetection
$ pip install -v -e .
```
b. Refer to the mmdetection's installation instructions.

c. Use Dockerfile from mmdetection to setup the environment.

Install other dependencies

# For image processing operations.
$ pip install Pillow
# For spectral-residual saliency algorithm in OA-Mix.
$ pip install opencv-python
$ pip install opencv-contrib-python

Prepare the following datasets:
- Cityscapes: A dataset that contains urban street scenes from 50 cities with detailed annotations.
- Diverse Weather Dataset: This dataset includes various weather conditions for robust testing and development of models, essential for applications in autonomous driving.

🏃 How To Run

Training

python3 -u tools/train.py $(CONFIG).py --work-dir $(WORK_DIR)

Example: OA-DG trained on Cityscapes dataset

python3 -u tools/train.py configs/OA-DG/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes_oadg.py --work-dir /ws/data/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes_oadg

Example: OA-DG trained on DWD dataset

python3 -u tools/train.py configs/OA-DG/dwd/faster_rcnn_r101_dc5_1x_dwd_oadg.py --work-dir /ws/data/dwd/faster_rcnn_r101_dc5_1x_dwd_oadg

Evaluation

Cityscapes-C

 python3 -u tools/analysis_tools/test_robustness.py \
   $(CONFIG).py $(CKPT_FILE).pth --out $(OUT_PATH).pkl \
   --corruptions benchmark --eval bbox

Example: OA-DG evaluated on Cityscapes-C dataset

 python3 -u tools/analysis_tools/test_robustness.py \
   configs/OA-DG/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes_oadg.py \
   /ws/data/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes_oadg/epoch_2.pth \ 
   --out /ws/data/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes_oadg/test_robustness_result_2epoch.pkl \
   --corruptions benchmark --eval bbox

Diverse Weather Dataset (DWD)

 python3 -u tools/test_dwd.py \
   $(CONFIG).py $(CKPT_FILE).pth --out $(OUT_PATH).pkl \
   --eval mAP

Example: OA-DG evaluated on DWD dataset

 python3 -u tools/analysis_tools/test_dwd.py \
   configs/OA-DG/dwd/faster_rcnn_r101_dc5_1x_dwd_oadg.py \
   /ws/data/dwd/faster_rcnn_r101_dc5_1x_dwd_oadg/epoch_10.pth \ 
   --out /ws/data/dwd/faster_rcnn_r101_dc5_1x_dwd_oadg/test_robustness_result_10epoch.pkl \
   --eval mAP

Demo

You can run demo.

Results

We evaluated the robustness of our method for common corruptions and various weather conditions in urban scenes. mPC is an evaluation metric of robustness against out-of-distribution (OOD).

Cityscapes-C:
DWD:

☀️ Daytime-Sunny

Class GTs Dets Recall AP
aeroplane 1738 9711 0.799 0.561
bicycle 1046 6165 0.716 0.491
bird 95339 325982 0.880 0.763
boat 537 3151 0.702 0.462
bottle 12309 76318 0.764 0.557
bus 787 3410 0.654 0.489
car 5029 28229 0.835 0.582
mAP 0.558

🌃 Night-Sunny

Class GTs Dets Recall AP
aeroplane 2012 15307 0.688 0.395
bicycle 1410 9151 0.616 0.371
bird 241616 1409587 0.846 0.639
boat 665 13191 0.498 0.178
bottle 17566 185415 0.710 0.439
bus 841 4907 0.447 0.271
car 4853 41633 0.714 0.412
mAP 0.386

🌧️ Dusk-Rainy

Class GTs Dets Recall AP
aeroplane 820 3953 0.604 0.382
bicycle 322 2469 0.481 0.285
bird 34240 180293 0.835 0.681
boat 110 1508 0.336 0.132
bottle 5144 27022 0.525 0.325
bus 169 1186 0.331 0.214
car 2235 13158 0.703 0.449
mAP 0.353

🌙 Night-Rainy

Class GTs Dets Recall AP
aeroplane 248 1158 0.468 0.289
bicycle 121 1088 0.223 0.123
bird 21655 174857 0.668 0.356
boat 49 1635 0.143 0.010
bottle 1532 20963 0.378 0.139
bus 71 560 0.169 0.120
car 499 4383 0.463 0.220
mAP 0.180

🌫️ Daytime-Foggy

Class GTs Dets Recall AP
aeroplane 554 1882 0.493 0.324
bicycle 4920 17470 0.500 0.324
bird 33392 81460 0.714 0.626
boat 911 4301 0.497 0.319
bottle 21530 62759 0.527 0.420
bus 2363 6609 0.530 0.426
car 736 6068 0.497 0.267
mAP 0.387

Class	GTs	Dets	Recall	AP
aeroplane	1738	9711	0.799	0.561
bicycle	1046	6165	0.716	0.491
bird	95339	325982	0.880	0.763
boat	537	3151	0.702	0.462
bottle	12309	76318	0.764	0.557
bus	787	3410	0.654	0.489
car	5029	28229	0.835	0.582
mAP				0.558

Class	GTs	Dets	Recall	AP
aeroplane	2012	15307	0.688	0.395
bicycle	1410	9151	0.616	0.371
bird	241616	1409587	0.846	0.639
boat	665	13191	0.498	0.178
bottle	17566	185415	0.710	0.439
bus	841	4907	0.447	0.271
car	4853	41633	0.714	0.412
mAP				0.386

Class	GTs	Dets	Recall	AP
aeroplane	820	3953	0.604	0.382
bicycle	322	2469	0.481	0.285
bird	34240	180293	0.835	0.681
boat	110	1508	0.336	0.132
bottle	5144	27022	0.525	0.325
bus	169	1186	0.331	0.214
car	2235	13158	0.703	0.449
mAP				0.353

Class	GTs	Dets	Recall	AP
aeroplane	248	1158	0.468	0.289
bicycle	121	1088	0.223	0.123
bird	21655	174857	0.668	0.356
boat	49	1635	0.143	0.010
bottle	1532	20963	0.378	0.139
bus	71	560	0.169	0.120
car	499	4383	0.463	0.220
mAP				0.180

Class	GTs	Dets	Recall	AP
aeroplane	554	1882	0.493	0.324
bicycle	4920	17470	0.500	0.324
bird	33392	81460	0.714	0.626
boat	911	4301	0.497	0.319
bottle	21530	62759	0.527	0.420
bus	2363	6609	0.530	0.426
car	736	6068	0.497	0.267
mAP				0.387

TODO list

Add instructions
Add codes
Add checkpoint files
Add configuration files for DWD
Pull request to MMDetection (Please click here to review the pull request.)

📢 License

Please see the LICENSE.md file.

📫 Contact Information

If you have any questions, please do not hesitate to contact us:

Wooju Lee ✉️ dnwn24@kaist.ac.kr
Dasol Hong ✉️ ds.hong@kaist.ac.kr

📎 Citation

@inproceedings{lee2024object,
  title={Object-Aware Domain Generalization for Object Detection},
  author={Lee, Wooju and Hong, Dasol and Lim, Hyungtae and Myung, Hyun},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={38},
  number={4},
  pages={2947--2955},
  year={2024}
}
@misc{lee2023objectaware,
      title={Object-Aware Domain Generalization for Object Detection}, 
      author={Wooju Lee and Dasol Hong and Hyungtae Lim and Hyun Myung},
      year={2023},
      eprint={2312.12133},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}