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https://github.com/lartpang/zoomnet
Zoom In and Out: A Mixed-scale Triplet Network for Camouflaged Object Detection, CVPR 2022
https://github.com/lartpang/zoomnet
camouflaged-object-detection cod codeforpaper cvpr cvpr2022 paper papercode python pytorch rgbcod rgbsod saliency-detection salient-object-detection sod
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Zoom In and Out: A Mixed-scale Triplet Network for Camouflaged Object Detection, CVPR 2022
- Host: GitHub
- URL: https://github.com/lartpang/zoomnet
- Owner: lartpang
- License: mit
- Created: 2022-03-02T02:58:21.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-11-01T07:04:22.000Z (about 1 year ago)
- Last Synced: 2024-11-15T06:33:18.542Z (5 days ago)
- Topics: camouflaged-object-detection, cod, codeforpaper, cvpr, cvpr2022, paper, papercode, python, pytorch, rgbcod, rgbsod, saliency-detection, salient-object-detection, sod
- Language: Python
- Homepage: https://lartpang.github.io/docs/zoomnet.html
- Size: 2.33 MB
- Stars: 128
- Watchers: 7
- Forks: 21
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
- License: LICENSE
Awesome Lists containing this project
README
# (CVPR 2022) Zoom In and Out: A Mixed-scale Triplet Network for Camouflaged Object Detection
[](https://opensource.org/licenses/MIT)
[](https://arxiv.org/abs/2203.02688)
[](https://github.com/lartpang/ZoomNet/releases/download/v0.0.1/zoomnet-arxiv.pdf)
```
@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: .**
## 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](https://github.com/lartpang/PySODMetrics) == 1.2.4 # for evaluating results
More details can be found in <./requirements.txt>
### Datasets
More details can be found at:
- COD Datasets:
- SOD Datasets:
### Training
You can use our default configuration, like this:
```shell
$ 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:
```shell
$ 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 --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
| Task | Weights | Results |
| ---- | --------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- |
| COD | [GitHub Release Link](https://github.com/lartpang/ZoomNet/releases/download/v0.0.1/cod_zoomnet_r50_bs8_e40_2022-03-04.pth) | [GitHub Release Link](https://github.com/lartpang/ZoomNet/releases/download/v0.0.1/CVPR-2022-ZoomNet-COD.zip) |
| SOD | [GitHub Release Link](https://github.com/lartpang/ZoomNet/releases/download/v0.0.1/sod_zoomnet_r50_bs22_e50_2022-03-04_fixed.pth) | [GitHub Release Link](https://github.com/lartpang/ZoomNet/releases/download/v0.0.1/CVPR-2022-ZoomNet-SOD.zip) |
For ease of use, we create a `test.py` script and a use case in the form of a shell script `test.sh`.
```shell
$ 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: Code: yxy9
- PySODEvalToolkit: A Python-based Evaluation Toolbox for Salient Object Detection and Camouflaged Object Detection
-
## Paper Details
### Method Detials
### Comparison
#### Camouflaged Object Detection
#### Salient Object Detection
