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Single-DGOD

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# Single-Domain Generalized Object Detection

CVPR2022: Single-Domain Generalized Object Detection in Urban Scene via Cyclic-Disentangled Self-Distillation.

The current code is Faster R-CNN with FPN. In our paper, we do not utilize FPN. Besides, welcome to focus on our CVPR 2024: Prompt-Driven Dynamic Object-Centric Learning for
Single Domain Generalization. [https://github.com/Daniel00008/PDOC]

### Datasets

#### Daytime-Sunny, Night-Sunny, Dusk-Rainy, Night-Rainy, and Daytime-Foggy

[[Download link](https://drive.google.com/drive/folders/1IIUnUrJrvFgPzU8D6KtV0CXa8k1eBV9B)]

[[models](https://drive.google.com/file/d/1s4AFraCUDX_X2ZyKthSpX0vFH37NipUY/view?usp=share_link)]

## Training

CUDA_VISIBLE_DEVICES=$GPU_ID python trainval_net_fpn.py \
                    --dataset dc_fpn --net res101 --epochs 20 \
                    --bs 2 --nw 8 \
                    --lr 0.004 --lr_decay_step 8 \
                    --cuda

## Evaluation

CUDA_VISIBLE_DEVICES=$GPU_ID python test_net_fpn.py --dataset dc_fpn --dataset_test voc_2007_train_nightclear --net res101 \
                   --checksession 1 --checkepoch 10 --checkpoint 19317 \
                   --cuda

## New Results

## Citation

If you find this repository useful for your work, please cite as follows:

```
@inproceedings{wu2022single,
  title={Single-Domain Generalized Object Detection in Urban Scene via Cyclic-Disentangled Self-Distillation},
  author={Wu, Aming and Deng, Cheng},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={847--856},
  year={2022}
}

```

Single-DGOD

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