Please refer to our paper.
Please install Caffe first. I think you may find a great number of tutorials talking about how to install it.
cd <caffe_root>/examples
git clone https://github.com/Andrew-Qibin/DSS.git
Before you start, you also need our pretrained model.
wget http://mftp.mmcheng.net/Andrew/dss_model_released.caffemodel
You can also download it from here (google drive).
If you want to train the model, please prepare your own training dataset first. The data layer we used here is similar to the one used in HED. You can also refer to the data layer used in Deeplab or write your own one. Then, run
python run_saliency.py
If you want to test the model, you can run
ipython notebook DSS-tutorial.ipynb
From left to right: Source, Groundtruth, Ours, DCL, DHS, RFCN, DS, MDF, ELD, MC, DRFI, DSR.
- MSRAB: including 2500 training, 500 validation, and 2000 test images. (This is also our training set.)
- MSRA10K: You can also use this dataset for training as some works did.
- Evaluation Code (Windows): The cold is based on MS Visual Studio.
- Evaluation Code (Ubuntu): This code is based on C++ and with a python wrapper for python users.
@article{hou2016deeply,
title={Deeply supervised salient object detection with short connections},
author={Hou, Qibin and Cheng, Ming-Ming and Hu, Xiaowei and Borji, Ali and Tu, Zhuowen and Torr, Philip},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
year={2017}
}