git clone https://github.com/guobaoxiao/COMPrompter
cd COMPrompter

For the train npz and test npz of the perfect boundary with gradient (corresponding Ours in Tab. 1). You can load down the COD datasets and run to make npz one by one.

• COD datasets: download the COD datasets set from here(CAMO, CHAMELEON, COD10K, NC4K), and put into 'data/'

python pre_npz.py

For the test npz of the generated boundary with gradient (corresponding Ours* in Tab. 1). You should load down the dataset of the generated boundary and run to make npz one by one.

• the generated boundary image dataset:链接：https://pan.baidu.com/s/14b9LYOYaTQ0CQ7qukbWLtg 提取码：vmlp

python pre_npz_UEDG.py

• pre-weigth: download the weight of sam from here, put into 'work_dir_cod/SAM/'

• COMPrompter: download the weight of well-trained COMPrompter, put into 'work_dir_cod/COMPrompter'

  • 链接：https://pan.baidu.com/s/1Hr7q7x3bctnEtdS7XeciUw 提取码：dv67

• COMPrompter using the perfect boundary:

  • 链接：https://pan.baidu.com/s/1MQDx1bVKoYCPFSiFV9XVew 提取码：u9cj

• COMPrompter using the generated boundary:

  • 链接：https://pan.baidu.com/s/1ezx5cl2T4hCe2O55I8Dygw 提取码：l051

python Train.py

python Inference.py

python transformer_nzp_2_gt.py

python MSCAF_COD_evaluation/evaluation.py

@article{zhang2024COMPrompter,
  title={COMPrompter: Rethink SAM in Camouflaged Object Detection with Multi-Prompt Network},
  author={Zhang, Xiaoqin and Yu, Zhenni and Zhao,  Li and Fan, Deng-Ping and Xiao, Guobao},
  journal={SCIENCE CHINA Information Sciences (SCIS)},
  volume={1},
  pages={1--14},
  year={2024}

}