(CVPR 2022) Zoom In and Out: A Mixed-scale Triplet Network for Camouflaged Object Detection

@inproceedings{ZoomNet-CVPR2022,
	title     = {Zoom In and Out: A Mixed-scale Triplet Network for Camouflaged Object Detection},
	author    = {Pang, Youwei and Zhao, Xiaoqi and Xiang, Tian-Zhu and Zhang, Lihe and Lu, Huchuan},
	booktitle = CVPR,
	year      = {2022}
}

Changelog

• 2022-3-16
  • Add the link of the method prediction maps of Table 1 in our paper.
• 2022-03-08
  • Add the link of arxiv version.
• 2022-03-07
  • Add the link of paper.
• 2022-03-05:
  • Update weights and results links.
  • Fixed some bugs.
  • Update dataset links.
  • Update bibtex info.
• 2022-03-04:
  • Initialize the repository.
  • Add the model and configuration file for SOD.

Usage

Dependencies

Some core dependencies:

• timm == 0.4.12
• torch == 1.8.1
• pysodmetrics == 1.2.4 # for evaluating results

More details can be found in <./requirements.txt>

Datasets

More details can be found at:

• COD Datasets: https://github.com/lartpang/awesome-segmentation-saliency-dataset#camouflaged-object-detection-cod
• SOD Datasets: https://github.com/lartpang/awesome-segmentation-saliency-dataset#rgb-saliency

Training

You can use our default configuration, like this:

$ python main.py --model-name=ZoomNet --config=configs/zoomnet/zoomnet.py --datasets-info ./configs/_base_/dataset/dataset_configs.json --info demo

or use our launcher script to start the one command in commands.txt on GPU 1:

$ python tools/run_it.py --interpreter 'abs_path' --cmd-pool tools/commands.txt  --gpu-pool 1 --verbose --max-workers 1

If you want to launch multiple commands, you can use it like this:

1. Add your commands into the tools/commands.txt.
2. python tools/run_it.py --interpreter 'abs_path' --cmd-pool tools/commands.txt --gpu-pool <gpu indices> --verbose --max-workers max_workers

NOTE:

• abs_path: the absolute path of your python interpreter
• max_workers: the maximum number of tasks to start simultaneously.

Testing

For ease of use, we create a test.py script and a use case in the form of a shell script test.sh.

$ sudo chmod +x ./test.sh
$ ./test.sh 0  # on gpu 0

Method Comparisons

• The prediction maps corresponding to the methods in Table 1 of our paper:
  • Baidu Pan: https://pan.baidu.com/s/1dLMqa4tix1gdBN1uWrCPbQ Code: yxy9
• PySODEvalToolkit: A Python-based Evaluation Toolbox for Salient Object Detection and Camouflaged Object Detection
  • https://github.com/lartpang/PySODEvalToolkit

Paper Details

Method Detials

Comparison

Camouflaged Object Detection

Salient Object Detection