DeepSaliency is a saliency object detection framework based on fully convolutional neural networks with global input (whole raw images) and global output (whole saliency maps).
In the project website, you can find detailed descriptions, models, result maps and datasets used in our paper.
Xi Li, Liming Zhao, Lina Wei , Ming-Hsuan Yang, Fei Wu, Yueting Zhuang, Haibin Ling, Jingdong Wang. "DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection“. IEEE Transactions on Image Processing (TIP), 2016.
- Caffe (included in the project code)
- Python (ipython notebook is used)
- Linux (Windows is also OK with modification)
- Download or clone the project code
- In the
modelsdirectory, download the models from google drive
- Then a demo for processing one input image can be found in
- Download dataset to
dataset\create_caffe_data.pyto obtain the hdf5 training data
- Then training using the script in