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DINet

This repository contains the reference code for our TMM paper: arXiv Paper Version

If you use any part of our code, or DINet is useful for your research, please consider citing::

@article{yang2019dilated,
  title={A Dilated Inception Network for Visual Saliency Prediction},
  author={Yang, Sheng and Lin, Guosheng and Jiang, Qiuping and Lin, Weisi},
  journal={arXiv preprint arXiv:1904.03571},
  year={2019}
}

Requirements

  • Python 2.7
  • Keras 2.1.2
  • Tensorflow-gpu 1.3.0
  • opencv-python

Getting Started

Installation

  • Clone this repo:
git clone https://github.com/ysyscool/DINet
cd DINet
mkdir models
cd models

Train/Test

Download the SALICON 2015 dataset and modify the paths in config.yaml And then using the following command to train the model

python main.py --phase=train --batch_size=10

For testing, modify the variables of weightfile (in line 217) and imgs_test_path (in line 220) in the main.py. And then using the following command to test the model

python main.py --phase=test

Evaluation on SALICON dataset

Please refer to this link.

Acknowledgments

Code largely benefits from sam.

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A dilated inception network for visual saliency prediction (TMM 2019)

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