Authors: Runmin Cong, Mengyao Sun, Sanyi Zhang, Xiaofei Zhou, Wei Zhang, and Yao Zhao.

• This repository provides code for "Frequency Perception Network for Camouflaged Object Detection" ACM MM 2023. Paper

The training and testing experiments are conducted using PyTorch with one NVIDIA 2080Ti GPU of 32 GB Memory.

1. Configuring your environment (Prerequisites):

   • Installing necessary packages:

     python 3.6

     torch 1.11.0

     numpy 1.22.4

     mmcv-full 1.7.1

     timm 0.6.13

     mmdet 2.19.1

2. Downloading necessary data:

   • downloading dataset and move it into ./data/, which can be found from Baidu Drive.

   • downloading our weights and move it into ./snapshot/FPNet-GroupInsert/FPNet.pth.

   • downloading PVTv1-Large weights and move it into ./lib/models/pvt_large.pth.

3. Training Configuration:

   • After you download training dataset, just run MyTrain_Val.py to train our model.

4. Testing Configuration:

   • After you download all the pre-trained model and testing dataset, just run MyTesting.py to generate the final prediction maps.

   • You can also download prediction maps ('CHAMELEON', 'CAMO', 'COD10K') from Baidu Drive.

One evaluation is written in Python code please follow this the instructions in ./evaluator.py and just run it to generate the evaluation results in. We implement four metrics: MAE (Mean Absolute Error), weighted F-measure, mean E-measure, S-Measure.

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