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README.md

Documentation and Tutorial

libcnn is a modular deep learning libraray, useful for robotics and computer vision. It is being developed by Robotics and Multi-perception Lab of City Univerisity of Hong Kong.

Quick Start

Here is a quick version to help you get started with the library.

The library has a light dependancy list:

  • Eigen vesion 3, a C++ template library for linear algebra: matrices, vectors, numerical solvers, and related algorithms.
  • boost version 1.49, peer-reviewed portable C++ source libraries.

The library was compiled on Ubuntu

Compilation and Installation

mkdir build
cd build
cmake ..
make
sudo make install (optional)

Example:

We show an example of pixel-wise classification: scene-labelling.
The example used semantically-augmented make3d dataset. The following assumes that you have already successfully done the above.

cd build
./train (training)
./validate (testing)

Sample Results

Here are several pixel-wise classification results of semantically-augmented make3d dataset
alt tag alt tag

Bug Reporting

Please use github's issue tracker to report bugs

Citing

If you use libcnn in an academic context, please cite the following publications:

@inproceedings{li2015anefficient,  
  title={An Efficient Multi-scale Convolutional Neural Network for Image Classification Based on PCA},  
  author={Li, Shaohua and Huang, Huimin and Zhang, Yue and Liu, Ming},  
  year={2015},  
  organization={RCAR}  
}

@inproceedings{li2015efficient}  
  titile={Efficient Execution of Deep Neural Networks for Image Classification},  
  author={Li, Shaohua and Liu, Ming},  
  year={2015},  
  booktitile={Real-time Computing and Robotics, Robotics and Biomimetics, Journal on},  
  organization={Springer}  
}

PDF link:
An Efficient Multi-scale Convolutional Neural Network for Image Classification Based on PCA
Efficient Execution of Deep Neural Networks for Image Classification