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FSNet: Focus Scanning Network for Camouflaged Object Detection

Authors: Ze Song, Xudong Kang, Xiaohui Wei, Haibo Liu, Renwei Dian, and Shutao Li.

Code implementation of "FSNet: Focus Scanning Network for Camouflaged Object Detection". IEEE TIP 2023.Paper

Prerequisites

Install Prerequisites with the following command:

conda create -n FSNet python = 3.7
conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.2 -c pytorch
pip install -r requirements.txt

Usage

1. Download pre-trained Swin transformer model

Please download model from the official websites:

• github or baidu with the fetch code: swin.
• move it into ./pretrained_ckpt/

2. Prepare data

We use data from publicly available datasets:

• downloading testing dataset and move it into ./Dataset/TestDataset/, which can be found in Google Drive or Baidu Drive (extraction code: fapn).

• downloading training/validation dataset and move it into ./Dataset/TrainValDataset/, which can be found in Google Drive or Baidu Drive (extraction code: fapn).

3. Train

To train FSNet with costumed path:

python MyTrain_Val.py --save_path './snapshot/FSNet/'

4. Test

To test with trained model:

python MyTesting.py --pth_path './snapshot/FSNet/Net_epoch_best.pth'

downloading our weights and move it into ./snapshot/FSNet/, which can be found from Google Drive.

You can also download prediction maps from Google Drive.

4. Evaluation

We use public one-key evaluation, which is written in MATLAB code (link). Please follow this the instructions in ./eval/main.m and just run it to generate the evaluation results in ./res/.

Citation

Please cite our paper if you find the work useful, thanks!

@article{song2023fsnet,
   title={FSNet: Focus Scanning Network for Camouflaged Object Detection},
   author={Song, Ze and Kang, Xudong and Wei, Xiaohui and Liu, Haibo and Dian, Renwei and Li, Shutao},
   journal={IEEE Transactions on Image Processing},
   year={2023},
   publisher={IEEE}
}

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