@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}
}
Extensions to the conference version can be found: https://github.com/lartpang/ZoomNeXt.
- 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.
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>
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
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 demoor 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 1If you want to launch multiple commands, you can use it like this:
- Add your commands into the
tools/commands.txt. 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 interpretermax_workers: the maximum number of tasks to start simultaneously.
| Task | Weights | Results |
|---|---|---|
| COD | GitHub Release Link | GitHub Release Link |
| SOD | GitHub Release Link | GitHub Release Link |
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- 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
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