Semantic Image Segmentation by Scale-Adaptive Networks
By Zilong Huang, Chunyu Wang, Xinggang Wang, Wenyu Liu and Jingdong Wang.
This code is a implementation of the experiments in the paper Semantic Image Segmentation by Scale-Adaptive Networks, which is accepted by Transactions on Image Processing. The code is developed based on the Caffe framework.
License
SAN is released under the MIT License (refer to the LICENSE file for details).
Citing SANet
If you find SANet useful in your research, please consider citing:
@article{huang2019sanet,
title={Semantic Image Segmentation by Scale-Adaptive Networks},
author={Huang, Zilong and Wang, Chunyu and Wang, Xinggang and Liu, Wenyu and Wang, Jingdong},
journal={IEEE Transactions on Image Processing},
year={2019},
publisher={IEEE}
}
Installing dependencies
- caffe (deeplabv2 version): deeplabv2 caffe installation instructions are available at
https://bitbucket.org/aquariusjay/deeplab-public-ver2. Note, you need to compile caffe with python wrapper and support for python layers. Then add the caffe python path into tools/findcaffe.py.
Training the SAN model
- Run:
$ python tools/train.py --solver YOUR_SOLVER --weight IMAGENET_PRETRAINED_MODEL --gpu GPU_IDThe corresponding solver files and input image lists are put in config and list floders.
Acknowledgment
The work was mainly done during an internship at MSRA.