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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> · <a href="https://sites.google.com/site/miaojingshi/home">Miaojing Shi</a> · <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

🔨 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

Download the WIDER Face Training & Validation images at WIDER FACE.
Obtain the annotations of training set and validation set.
Download the pretrained weight of Retinex Decomposition Net.
Prepare the pretrained weight of the base network.

Organize the folders as:

.
├── utils
├── weights
│   ├── decomp.pth
│   ├── vgg16_reducedfc.pth
├── dataset
│   ├── wider_face_train.txt
│   ├── wider_face_val.txt
│   ├── WiderFace
│   │   ├── WIDER_train
│   │   └── 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:

Download the testing samples from UG2+ Challenge.
Download the checkpoints: DarkFaceZSDA (28.0) or DarkFaceFS (52.9, finetuned with full supervision).
Set (1) the paths of testing samples & checkpoint, (2) whether to use a multi-scale strategy, and run test.py.
Submit the results for benchmarking. (Detailed instructions).

On ExDark:

Our experiments are based on the codebase of MAET. You only need to replace the checkpoint with ours for evaluation.

📑 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}
}

@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}
}

🔎 Acknowledgement

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