• OSFormer: One-Stage Camouflaged Instance Segmentation with Transformers (ECCV 2022)
• • Environment preparation
  • Dataset preparation
  • • Download the datasets
    • Register datasets
  • Pre-trained models
  • Visualization results
  • Frequently asked questions
  • Usage
  • • Train
    • Inference
    • Eval
  • Acknowledgement
  • Citation

OSFormer: One-Stage Camouflaged Instance Segmentation with Transformers (ECCV 2022)

SINet-V3 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: dengpfan@gmail.com, peijl@hust.edu.cn

Sample 1	Sample 2	Sample 3	Sample 4

Environment preparation

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

Dataset preparation

Download the datasets

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

Register datasets

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')

Pre-trained models

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

Model	Config	COD10K-test AP	NC4K-test AP
R50-550	configs/CIS_RT.yaml	36.0	41.4
R50	configs/CIS_R50.yaml	41.0	42.5
R101	configs/CIS_R101.yaml	42.0	44.4
PVTv2-B2-Li	configs/CIS_PVTv2B2Li	47.2	50.5
SWIN-T	configs/CIS_SWINT.yaml	47.7	50.2

Visualization results

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

Frequently asked questions

FAQ

Usage

Train

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

Inference

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

Eval

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

Acknowledgement

This work is based on:

• detectron2
• AdelaiDet
• DETR
• Deformable DETR

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

Citation

If this helps you, please cite this work (SINet-V3):

@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}
}

For the SINet-V1, and SINet-V2 please cite the following works:

@article{fan2021concealed,
  title={Concealed Object Detection},
  author={Fan, Deng-Ping and Ji, Ge-Peng and Cheng, Ming-Ming and Shao, Ling},
  journal={IEEE TPAMI},
  year={2022}
}
    
@inproceedings{fan2020camouflaged,
  title={Camouflaged object detection},
  author={Fan, Deng-Ping and Ji, Ge-Peng and Sun, Guolei and Cheng, Ming-Ming and Shen, Jianbing and Shao, Ling},
  booktitle={IEEE CVPR},
  pages={2777--2787},
  year={2020}
}