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<p align="center"> <h1 align="center">Boosting Object Detection with Zero-Shot Day-Night Domain Adaptation </h1> <p align="center"> <a href="https://zpdu.github.io/">Zhipeng Du</a> Β· <a href="https://sites.google.com/site/miaojingshi/home">Miaojing Shi</a> Β· <a href="https://jiankangdeng.github.io/">Jiankang Deng</a> </p>

PyTorch implementation of Boosting Object Detection with Zero-Shot Day-Night Domain Adaptation. (CVPR 2024) [Page | Paper]

overview

πŸ”¨ To-Do List

    • release the code regarding the proposed model and losses.
    • release the evaluation code, and the pretrained models.
    • release the training code.

:rocket: Installation

Begin by cloning the repository and setting up the environment:

git clone https://github.com/ZPDu/DAI-Net.git
cd DAI-Net

conda create -y -n dainet python=3.7
conda activate dainet

pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 -f https://download.pytorch.org/whl/torch_stable.html

pip install -r requirements.txt

:notebook_with_decorative_cover: Training

Data and Weight Preparation

Organize the folders as:

.
β”œβ”€β”€ utils
β”œβ”€β”€ weights
β”‚   β”œβ”€β”€ decomp.pth
β”‚   β”œβ”€β”€ vgg16_reducedfc.pth
β”œβ”€β”€ dataset
β”‚   β”œβ”€β”€ wider_face_train.txt
β”‚   β”œβ”€β”€ wider_face_val.txt
β”‚   β”œβ”€β”€ WiderFace
β”‚   β”‚   β”œβ”€β”€ WIDER_train
β”‚   β”‚   └── WIDER_val

Model Training

To train the model, run

python -m torch.distributed.launch --nproc_per_node=$NUM_OF_GPUS$ train.py

:notebook: Evaluation​

On Dark Face:

On ExDark:

πŸ“‘ Citation

If you find this work useful, please cite

@inproceedings{du2024boosting,
  title={Boosting Object Detection with Zero-Shot Day-Night Domain Adaptation},
  author={Du, Zhipeng and Shi, Miaojing and Deng, Jiankang},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={12666--12676},
  year={2024}
}

or

@article{du2023boosting,
  title={Boosting Object Detection with Zero-Shot Day-Night Domain Adaptation},
  author={Du, Zhipeng and Shi, Miaojing and Deng, Jiankang},
  journal={arXiv preprint arXiv:2312.01220},
  year={2023}
}

πŸ”Ž Acknowledgement

We thank DSFD.pytorch, RetinexNet_PyTorch, MAET, HLA-Face for their amazing works!