Awesome
Recurrently Aggregating Deep Features for Salient Object Detection
by Xiaowei Hu, Lei Zhu, Jing Qin, Chi-Wing Fu, and Pheng-Ann Heng
This implementation is written by Xiaowei Hu at the Chinese University of Hong Kong.
Citation
@inproceedings{hu18recurrently,
author = {Hu, Xiaowei and Zhu, Lei and Qin, Jing and Fu, Chi-Wing and Heng, Pheng-Ann},
title = {Recurrently Aggregating Deep Features for Salient Object Detection},
booktitle = {AAAI},
pages={6943--6950},
year = {2018}
}
Saliency Maps
The results of salient object detection on five datasets (ECSSD, HKU-IS, PASCAL-S, SOD, DUT-OMRON) can be found at Google Drive.
Installation
*This model is tested on Ubuntu 16.04, CUDA 8.0, cuDNN 5.0
-
Please download and compile our CF-Caffe.
-
Clone the RADF repository, and we'll call the directory that you cloned as
RADF-master
.git clone https://github.com/xw-hu/RADF.git
-
Replace
CF-Caffe/examples/
byRADF-master/examples/
. ReplaceCF-Caffe/data/
byRADF-master/data/
.
Test
-
Put the pretrained model in
examples/RADF/snapshot/
. -
Export PYTHONPATH in the command window such as:
export PYTHONPATH='/path/to/CF-Caffe/python'
-
Make the folder
examples/RADF/result/
and run the test model inexamples/RADF/
(please modify the path of images):ipython notebook RADF_test.ipynb
-
Apply CRF to do the post-processing for each image.
The code for CRF can be found in https://github.com/Andrew-Qibin/dss_crf
*Note that please provide a link to the original code as a footnote or a citation if you plan to use it.
Train
-
Download the pre-trained VGG16 model at http://www.robots.ox.ac.uk/~vgg/research/very_deep/.
Put this model inCF-Caffe/models/
-
Enter the
examples/RADF/
Modify the image path intrain_val.prototxt
. -
Run
sh train.sh
Useful Links
- MSRA10k : Our training set.
- Evaluation Code (Windows)
- Evaluation Code (Ubuntu)
*Note that please provide a link to the original code as a footnote or a citation if you plan to use it.