Authors: Fan Yang, Qiang Zhai, Xin Li, Rui Huang, Hong Cheng, Deng-Ping Fan.

1. Configuring your environment (Prerequisites):

   Pytorch>=1.0.0

   OpenCV

2. Downloading Testing Sets:

   • downloading NEW testing dataset (COD10K-test + CAMO-test + CHAMELEON), which can be found in this Google Drive link or Baidu Pan link with the fetch code: z83z.

3. Testing Configuration:

   • After you download the trained models Google Drive link or Baidu Pan link, move it into './model_file/'.
   • Assigning your comstomed path in 'config/cod_resnet50.yaml', like 'data_root', 'test_list'.
   • Playing 'test.py' to generate the final prediction map, the predicted camouflaged object region and cmouflaged object edge is saved into 'result' as default.

4. Evaluation your trained model:

   • One-key evaluation is written in MATLAB code (revised from link), please follow this the instructions in main.m and just run it to generate the evaluation results in ./EvaluationTool/EvaluationResults/Result-CamObjDet/.
   • The results can be downloaded in Baidu Pan link(password: 2kj3).

6. Training Configuration:

   • After you download the initial model Google Drive link or Baidu Pan link, move it to './pre_trained/'.
   • Put the 'train_test_file/train.lst' to the path which is included in cod_resnet50.yaml.
   • Run train.py

7. If you think this work is helpful, please cite

@inproceedings{fan2021ugtr,
  title={Uncertainty-Guided Transformer Reasoning for Camouflaged Object Detection},
  author={Yang, Fan and Zhai, Qiang and Li, Xin and Huang, Rui and Cheng, Hong and Fan, Deng-Ping},
  booktitle={IEEE International Conference on Computer Vision(ICCV)},
  pages={},
  year={2021}
}