xings-sdnu / deanet

Code for DEANet

DEANet-pytorch

Pytorch implementation for Saliency Detection Framework Based on Deep Enhanced Attention Network(ICONIP 2021)

• DEANet in PyTorch
  • Requirements
  • Usage
  • Training
  • Test
  • Learning Curve
  • Pre-trained ImageNet model for training
  • Trained model for testing
  • DEANet-pytorch saliency maps
  • Dataset
  • Performance
  • Citation
  • Acknowledgement

Requirements

• Python 3.7
  
• Pytorch 1.8.1
  
• Torchvision 0.9.1
  
• Cuda 11.0

Usage

This is the Pytorch implementation of DEANet. It has been trained and tested on Linux (Ubuntu 18.02 + Cuda 11.7 + Python 3.7 + Pytorch 1.8.0), and it can also work on Win10.

Training

• Download the pre-trained ImageNet backbone (resnet101, Baidu YunPan: resnet101, password:93q7, and put it in the 'pretrained' folder
• Download the training dataset and modify the 'train_root' and 'train_list' in the main.py
• Set 'mode' to 'train'
• Run main.py

Test

• Download the testing dataset and have it in the 'dataset/test/' folder
• Download the already-trained DEANet pytorch model and modify the 'model' to its saving path in the main.py
• Modify the 'test_folder' in the main.py to the testing results saving folder you want
• Modify the 'sal_mode' to select one testing dataset (NJU2K, NLPR, STERE, RGBD135, LFSD or SIP)
• Set 'mode' to 'test'
• Run main.py

Learning curve

The training log is saved in the 'log' folder. If you want to see the learning curve, you can get it by using: tensorboard --logdir your-log-path

Pre-trained ImageNet model for training

resnet101
vgg_conv1, password: rllb

Trained model for testing

Baidu Pan: DEANet-pytorch, password: svyr
Google Drive:

DEANet-pytorch saliency maps

Baidu Pan: Saliency maps, password: maft
Google Drive:

Dataset

Baidu Pan:
Training dataset (with horizontal flip), password: i4mi
Testing datadet, password: 1ju8
Google Drive:
Training dataset (with horizontal flip)
Testing datadet

Performance

Below is the performance of DEANet-pyotrch (Pytorch implementation). Due to the randomness in the training process, the obtained results will fluctuate slightly.

Datasets	Metrics	Pytorch
NJU2K	S-measure	0.917
	maxF	0.900
	maxE	0.919
	MAE	0.038
NLPR	S-measure	0.959
	maxF	0.922
	maxE	0.979
	MAE	0.014
STERE	S-measure	0.908
	maxF	0.877
	maxE	0.921
	MAE	0.041
RGBD135	S-measure	0.932
	maxF	0.907
	maxE	0.968
	MAE	0.021
LFSD	S-measure	0.855
	maxF	0.855
	maxE	0.885
	MAE	0.078
SSD	S-measure	0.870
	maxF	0.830
	maxE	0.901
	MAE	0.051

Citation

Please cite our paper if you find the work useful:

    @InProceedings{Xing_2021_ICONIP,
    author = {Xing Sheng, Zhuoran Zheng, Qiong Wu, Chunmeng Kang, Yunliang Zhuang, Lei Lyu, Chen Lyu},
    title = {Saliency Detection Framework Based on Deep Enhanced Attention Network},
    booktitle = {International Conference on Neural Information Processing (ICONIP)},
    pages={274--286},
    year = {2021}
    }

Acknowledgement

• JL-DCF