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https://github.com/HyeonwooNoh/DeconvNet

DeconvNet : Learning Deconvolution Network for Semantic Segmentation
https://github.com/HyeonwooNoh/DeconvNet

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DeconvNet : Learning Deconvolution Network for Semantic Segmentation

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## DeconvNet: Learning Deconvolution Network for Semantic Segmentation



Created by [Hyeonwoo Noh](http://cvlab.postech.ac.kr/~hyeonwoonoh/), [Seunghoon Hong](http://cvlab.postech.ac.kr/~maga33/) and [Bohyung Han](http://cvlab.postech.ac.kr/~bhhan/) at POSTECH



Acknowledgements: Thanks to Yangqing Jia and the BVLC team for creating Caffe.



### Introduction



DeconvNet is state-of-the-art semantic segmentation system that combines bottom-up region proposals with multi-layer decovolution network.



Detailed description of the system will be provided by our technical report [arXiv tech report] http://arxiv.org/abs/1505.04366



### Citation



If you're using this code in a publication, please cite our papers.



    @article{noh2015learning,

      title={Learning Deconvolution Network for Semantic Segmentation},

      author={Noh, Hyeonwoo and Hong, Seunghoon and Han, Bohyung},

      journal={arXiv preprint arXiv:1505.04366},

      year={2015}

    }



### Pre-trained Model



If you need model definition and pre-trained model only, you can download them from following location:

  0. caffe for DeconvNet: https://github.com/HyeonwooNoh/caffe

  0. DeconvNet model definition: http://cvlab.postech.ac.kr/research/deconvnet/model/DeconvNet/DeconvNet_inference_deploy.prototxt

  0. Pre-trained DeconvNet weight: http://cvlab.postech.ac.kr/research/deconvnet/model/DeconvNet/DeconvNet_trainval_inference.caffemodel



### Licence



This software is being made available for research purpose only.

Check LICENSE file for details.



### System Requirements



This software is tested on Ubuntu 14.04 LTS (64bit).



**Prerequisites** 

  0. MATLAB (tested with 2014b on 64-bit Linux)

  0. prerequisites for caffe(http://caffe.berkeleyvision.org/installation.html#prequequisites)



### Installing DeconvNet



**By running "setup.sh" you can download all the necessary file for training and inference include:**

  0. caffe: you need modified version of caffe which support DeconvNet - https://github.com/HyeonwooNoh/caffe.git

  0. data: data used for training stage 1 and 2

  0. model: caffemodel of trained DeconvNet and other caffemodels required for training



### Training DeconvNet



Training scripts are included in *./training/* directory



**To train DeconvNet you can simply run following scripts in order:**

  0. 001\_start\_train.sh : script for first stage training

  0. 002\_start\_train.sh : script for second stage training

  0. 003\_start\_make\_bn\_layer\_testable : script converting trained DeconvNet with bn layer to inference mode



### Inference EDeconvNet+CRF



Run *run_demo.m* to reproduce EDeconvNet+CRF results on VOC2012 test data.



**This script will generated EDeconvNet+CRF results through following steps:**

  0. run FCN-8s and cache the score [cache\_FCN8s\_results.m]

  0. generate DeconvNet score and apply ensemble with FCN-8s score, post processing with densecrf [generate\_EDeconvNet\_CRF\_results.m]



*EDeconvNet+CRF obtains 72.5 mean I/U on PASCAL VOC 2012 Test*



**External dependencies [can be downloaded by running "setup.sh" script]**

  0. FCN-8s model and weight file [https://github.com/BVLC/caffe/wiki/Model-Zoo]

  0. densecrf with matlab wrapper [https://github.com/johannesu/meanfield-matlab.git]

  0. cached proposal bounding boxes extracted with edgebox object proposal [https://github.com/pdollar/edges]