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EGNet: Edge Guidance Network for Salient Object Detection (ICCV 2019)
We use the sal2edge.m to generate the edge label for training.
Clone this code by git clone https://github.com/bharat-mtr/EGNet.git --recursive
, assume your source code directory is$EGNet
;
Download training data;
Download initial model;
Change the image path and intial model path in run.py and dataset.py;
Start to train with python3 run.py --mode train
.
Download pretrained model;
Change the test image path in dataset.py
Generate saliency maps for SOD dataset by python3 run.py --mode test --sal_mode s
, PASCALS by python3 run.py --mode test --sal_mode p
and so on;
Testing code we use is the public open source code. (https://github.com/Andrew-Qibin/SalMetric)
| Page | | Training Set | | Pretrained models (2cf5) | | VGG resnet|
@inproceedings{zhao2019EGNet,
title={EGNet:Edge Guidance Network for Salient Object Detection},
author={Zhao, Jia-Xing and Liu, Jiang-Jiang and Fan, Deng-Ping and Cao, Yang and Yang, Jufeng and Cheng, Ming-Ming},
booktitle={The IEEE International Conference on Computer Vision (ICCV)},
month={Oct},
year={2019},
}
Contrast Prior and Fluid Pyramid Integration for RGBD Salient Object Detection. (CVPR2019) page
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