https://github.com/zal0302/cii
The official PyTorch implementation of IEEE Transactions on Image Processing 2021 paper "Rethinking the U-shape Structure for Salient Object Detection"
https://github.com/zal0302/cii
object-segmentation pytorch saliency saliency-detection saliency-maps saliency-prediction salient-object-detection u-net
Last synced: 4 days ago
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The official PyTorch implementation of IEEE Transactions on Image Processing 2021 paper "Rethinking the U-shape Structure for Salient Object Detection"
- Host: GitHub
- URL: https://github.com/zal0302/cii
- Owner: zal0302
- Created: 2022-06-14T08:36:25.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2022-12-01T02:52:21.000Z (over 2 years ago)
- Last Synced: 2025-04-28T10:57:15.271Z (20 days ago)
- Topics: object-segmentation, pytorch, saliency, saliency-detection, saliency-maps, saliency-prediction, salient-object-detection, u-net
- Language: Python
- Homepage:
- Size: 73.2 KB
- Stars: 19
- Watchers: 1
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
## Rethinking the U-shape Structure for Salient Object Detection
### This is the official PyTorch implementation of our TIP 2021 [paper](https://mftp.mmcheng.net/Papers/21TIP-CII.pdf).
## Prerequisites
- [Pytorch 0.4.1+](http://pytorch.org/)
- [torchvision](http://pytorch.org/)
## Usage
### 1. Clone the repository
```shell
git clone https://github.com/zal0302/CII.git
cd CII/
```
### 2. Download the datasets
Download the following [datasets for testing](https://drive.google.com/file/d/1jIL3Yvly4l4l_OggljjreD87pJIm_6rm/view?usp=sharing) and unzip them into `data` folder.
### 3. Download the pre-trained models for CII and backbone
Download the following pre-trained models for CII with [ResNet50 backbone](https://drive.google.com/file/d/1JcePr4FwWMedhFHeClYF1v_MIYwJGOF0/view?usp=sharing) and [ResNet18 backbone](https://drive.google.com/file/d/1DL860taDrmDUv-Am49AQZsdcF4Ey2-2t/view?usp=sharing) into `saved/models` folder.
### 4. Test
For all datasets testing used in our paper for ResNet50 backbone:
```shell
python test.py -r saved/models/cii.pth -c saved/models/config.json
```
and for ResNet18 backbone:
```shell
python test.py -r saved/models/cii_res18.pth -c saved/models/config_resnet18.json
```
All results saliency maps will be stored under `saved/results` folders in .png formats.
### 5. Pre-computed results and evaluation results
You may refer to this repo for results evaluation: [SalMetric](https://github.com/Andrew-Qibin/SalMetric).
We provide the pre-computed saliency maps and evaluation results for [ResNet50 backbone](https://drive.google.com/file/d/11Uj2-qNDyASrfvdXj2uE9Zm7xiYYwNEM/view?usp=sharing) and [ResNet18 backbone](https://drive.google.com/file/d/1Q53oKWTNA9KznWmbXGm2IhY_2yeYvF1E/view?usp=sharing).
### 6. Contact
If you have any questions, feel free to contact me via: `liuzhiang(at)mail.nankai.edu.cn`.
### If you think this work is helpful, please cite
```latex
@article{liu2021rethinking,
title={Rethinking the U-Shape Structure for Salient Object Detection},
author={Liu, Jiang-Jiang and Liu, Zhi-Ang and Peng, Pai and Cheng, Ming-Ming},
journal={IEEE Transactions on Image Processing},
volume={30},
pages={9030--9042},
year={2021},
publisher={IEEE}
}
```
```latex
@article{liu2022poolnet+,
title={Poolnet+: Exploring the potential of pooling for salient object detection},
author={Liu, Jiang-Jiang and Hou, Qibin and Liu, Zhi-Ang and Cheng, Ming-Ming},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2022},
publisher={IEEE}
}
```