Official Implementation of "OSFormer: One-Stage Camouflaged Instance Segmentation with Transformers"

Jialun Pei*, Tianyang Cheng*, Deng-Ping Fan, He Tang, Chuanbo Chen, and Luc Van Gool

[Paper]; [Chinese Version]; [Official Version]; [Project Page]

Contact: [email protected], [email protected]

Sample 1	Sample 2	Sample 3	Sample 4

The code is tested on CUDA 11.1 and pytorch 1.9.0, change the versions below to your desired ones.

git clone https://github.com/PJLallen/OSFormer.git
cd OSFormer
conda create -n osformer python=3.8 -y
conda activate osformer
conda install pytorch==1.9.0 torchvision cudatoolkit=11.1 -c pytorch -c nvidia -y
python -m pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu111/torch1.9/index.html
python setup.py build develop

• COD10K: Baidu (password:hust) / Google / Quark; Json files: Baidu (password:hust) / Google
• NC4K: Baidu (password:hust) / Google; Json files: Baidu (password:hust) / Google

1. generate coco annotation files, you may refer to the tutorial of mmdetection for some help
2. change the path of the datasets as well as annotations in adet/data/datasets/cis.py, please refer to the docs of detectron2 for more help

# adet/data/datasets/cis.py
# change the paths 
DATASET_ROOT = 'COD10K-v3'
ANN_ROOT = os.path.join(DATASET_ROOT, 'annotations')
TRAIN_PATH = os.path.join(DATASET_ROOT, 'Train/Image')
TEST_PATH = os.path.join(DATASET_ROOT, 'Test/Image')
TRAIN_JSON = os.path.join(ANN_ROOT, 'train_instance.json')
TEST_JSON = os.path.join(ANN_ROOT, 'test2026.json')

NC4K_ROOT = 'NC4K'
NC4K_PATH = os.path.join(NC4K_ROOT, 'Imgs')
NC4K_JSON = os.path.join(NC4K_ROOT, 'nc4k_test.json')

Model weights: Baidu (password:l6vn) / Google / Quark

The visual results are achieved by our OSFormer with ResNet-50 trained on the COD10K training set.

• Results on the COD10K test set: Baidu (password:hust) / Google
• Results on the NC4K test set: Baidu (password:hust) / Google

FAQ

python tools/train_net.py --config-file configs/CIS_R50.yaml --num-gpus 1 \
  OUTPUT_DIR {PATH_TO_OUTPUT_DIR}

Please replace {PATH_TO_OUTPUT_DIR} to your own output dir

python tools/train_net.py --config-file configs/CIS_R50.yaml --eval-only \
  MODEL.WEIGHTS {PATH_TO_PRE_TRAINED_WEIGHTS}

Please replace {PATH_TO_PRE_TRAINED_WEIGHTS} to the pre-trained weights

python demo/demo.py --config-file configs/CIS_R50.yaml \
  --input {PATH_TO_THE_IMG_DIR_OR_FIRE} \
  --output {PATH_TO_SAVE_DIR_OR_IMAGE_FILE} \
  --opts MODEL.WEIGHTS {PATH_TO_PRE_TRAINED_WEIGHTS}

• {PATH_TO_THE_IMG_DIR_OR_FIRE}: you can put image dir or image paths here
• {PATH_TO_SAVE_DIR_OR_IMAGE_FILE}: the place where the visualizations will be saved
• {PATH_TO_PRE_TRAINED_WEIGHTS}: please put the pre-trained weights here

This work is based on:

• detectron2
• AdelaiDet
• DETR
• Deformable DETR

We also get help from mmdetection. Thanks them for their great work!

If this helps you, please cite this work:

@inproceedings{pei2022osformer,
  title={OSFormer: One-Stage Camouflaged Instance Segmentation with Transformers},
  author={Pei, Jialun and Cheng, Tianyang and Fan, Deng-Ping and Tang, He and Chen, Chuanbo and Van Gool, Luc},
  booktitle={European conference on computer vision},
  year={2022},
  organization={Springer}
}