The training and testing experiments are conducted using PyTorch with 8 Tesla V100 GPUs of 36 GB Memory.

Note that FSPNet is only tested on Ubuntu OS with the following environments.

• Creating a virtual environment in terminal: conda create -n FSPNet python=3.8.
• Installing necessary packages: pip install -r requirements.txt

• Download the training set (COD10K-train) used for training
• Download the testing sets (COD10K-test + CAMO-test + CHAMELEON + NC4K ) used for testing

• The pretrained model is stored in Google Drive and Baidu Drive (xuwb). After downloading, please change the file path in the corresponding code.
• Run train.sh or slurm_train.sh as needed to train.

Our well-trained model is stored in Google Drive and Baidu Drive (otz5). After downloading, please change the file path in the corresponding code.

• Matlab code: One-key evaluation is written in MATLAB code, please follow this the instructions in main.m and just run it to generate the evaluation results.
• Python code: After configuring the test dataset path, run slurm_eval.py in the run_slurm folder for evaluation.

The prediction results of our FSPNet are stored on Google Drive and Baidu Drive (ryzg) please check.

@inproceedings{Huang2023Feature,
title={Feature Shrinkage Pyramid for Camouflaged Object Detection with Transformers},
author={Huang, Zhou and Dai, Hang and Xiang, Tian-Zhu and Wang, Shuo and Chen, Huai-Xin and Qin, Jie and Xiong, Huan},
booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2023}
}

Thanks to Deng-Ping Fan, Ge-Peng Ji, et al. for a series of efforts in the field of COD.