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).
###Project Website
http://www.zhaoliming.net/research/deepsaliency
In the project website, you can find detailed descriptions, models, result maps and datasets used in our paper.
Contact: Liming Zhao ([zhaoliming@zju.edu.cn](mailto: zhaoliming@zju.edu.cn))
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
demo.ipynb.
- Download dataset to
datasetdirectory - Run
dataset\create_caffe_data.pyto obtain the hdf5 training data - Then training using the script in
models\finetune.sh