Official Implementation of CVPR2023 Highlight paper "Camouflaged Instance Segmentation via Explicit De-camouflaging"

We propose a novel De-camouflaging Network (DCNet) by jointly modeling pixel-level camouflage decoupling and instance-level camouflage suppression for Camouflaged Instance Segmentation (CIS) task.

The code is tested on CUDA 11.3 and pytorch 1.10.1, change the versions below to your desired ones.

conda create -n dcnet python=3.9 -y
conda activate dcnet
conda install pytorch==1.10.1 torchvision==0.11.2 torchaudio==0.10.1 cudatoolkit=11.3 -c pytorch -c conda-forge
python -m pip install detectron2 -f \
    https://dl.fbaipublicfiles.com/detectron2/wheels/cu113/torch1.10/index.html
cd DCNet
pip install -r requirements.txt
cd dcnet/modeling/PCD/ops
sh make.sh

• COD10K: Google Json files: Google
• NC4K: Google; Json files: Google

Change the path of the datasets as well as annotations in dcnet/data/datasets/register_cis.py.

# dcnet/data/datasets/register_cis.py
# change the paths
COD10K_ROOT = 'COD10K'  # path to your COD10K dataset
ANN_ROOT = os.path.join(COD10K_ROOT, 'annotations')
TRAIN_PATH = os.path.join(COD10K_ROOT, 'Train_Image_CAM')
TEST_PATH = os.path.join(COD10K_ROOT, 'Test_Image_CAM')
TRAIN_JSON = os.path.join(ANN_ROOT, 'train_instance.json')
TEST_JSON = os.path.join(ANN_ROOT, 'test2026.json')
NC4K_ROOT = 'NC4K'  # path to your NC4K dataset
NC4K_PATH = os.path.join(NC4K_ROOT, 'test/image')
NC4K_JSON = os.path.join(NC4K_ROOT, 'nc4k_test.json')

python train_net.py \
    --config-file configs/CIS-R50.yaml \
    MODEL.WEIGHTS {PATH_TO_PRE_TRAINED_WEIGHTS}

DCNet model (ResNet-50) weights: Google

python train_net.py \
    --eval-only \
    --config-file configs/CIS-R50.yaml \
    MODEL.WEIGHTS {PATH_TO_PRE_TRAINED_WEIGHTS}

Please replace {PATH_TO_PRE_TRAINED_WEIGHTS} to the pre-trained weights.

If you find this code useful for your research, please cite our paper:

@inproceedings{luo2023camouflaged,
  title={Camouflaged Instance Segmentation via Explicit De-Camouflaging},
  author={Luo, Naisong and Pan, Yuwen and Sun, Rui and Zhang, Tianzhu and Xiong, Zhiwei and Wu, Feng},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={17918--17927},
  year={2023}
}

Some codes are adapted from OSFormer and Mask2Former. We thank them for their excellent projects.