Segment, Magnify and Reiterate Detecting Camouflaged Objects the Hard Way (CVPR2022)

Usage

The training and testing experiments are conducted using PyTorch with a single Tesla V100 GPU of 36 GB Memory.

1. Prerequisites

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

• Creating a virtual environment in terminal: conda create -n SegMaR python=3.6.

• Installing necessary packages: pip install -r requirements.txt.

• Installing NVIDIA-Apex (Under CUDA-10.0 and Cudnn-7.4).

• Installing MobulaOP for Sampler operation.

  # Clone the project
  git clone https://github.com/wkcn/MobulaOP
  
  # Enter the directory
  cd MobulaOP
  
  # Install MobulaOP
  pip install -v -e .
  
  

2. Downloading Training and Testing Datasets

The discriminative mask will be released soon. Or run ./OurSampler/DiscriminativeMask.py to generate your discriminative mask.

• Downloading training dataset (COD10K-train) and move it into ./OurModule/datasets/train/.

• Downloading testing dataset (COD10K-test + CAMO-test + CHAMELEON) and move it into ./OurModule/datasets/test/.

3. Training Configuration

• After you download all the training datasets, just run ./OurModule/train.py to generate the model (you can replace discriminative mask with binary groundtruth if necessary).

• For iterative training: generator.load_state_dict(torch.load('./OurModule/models/xxx.pth')).

4. Testing Configuration

• After you download all the pre-trained model and testing datasets, just run ./OurModule/test.py to generate the prediction map. Your save directory is ./OurModule/results.py.

5. Sampler Operation

• Make sure that you have installed MobulaOP in your virtual environment.

• For sampler operation, just run ./OurSampler/Sampler_Distort.py.

• For restoration operation, just run ./OurSampler/Sampler_Restort.py.

• For the directory of original prediction or restoration prediction, please see our codes details.

6. Evaluation

• 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.