Welcome to CN24!
CN24 is a complete semantic segmentation framework using fully convolutional networks. It supports a wide variety of platforms (Linux, Mac OS X and Windows) and libraries (OpenCL, Intel MKL, AMD ACML...) while providing dependency-free reference implementations. The software is developed at the Computer Vision Group, University of Jena.
Why should I use CN24?
- Designed for pixel-wise labeling and semantic segmentation (train and test your own networks!)
- Suited for various applications in driver assistance systems, scene understanding, remote sensing, biomedical image processing and many more
- OpenCL support not only suited for NVIDIA GPUs
- High-performance implementation with minimal dependencies to other libraries
Getting started
To get started, clone this repository and visit the wiki! Installation is just a two command lines away. For an even faster introduction, check out one of these examples:
The repository contains pre-trained networks for these two applications, which are ready to use.
Licensing
CN24 is available under a 3-clause BSD license. See LICENSE for details. If you use CN24 for research, please cite our paper:
@INPROCEEDINGS{Brust15:CPN,
author = {Clemens-Alexander Brust and Sven Sickert and
Marcel Simon and Erik Rodner and Joachim Denzler},
title = {Convolutional Patch Networks with Spatial Prior
for Road Detection and Urban Scene Understanding},
booktitle = {International Conference on Computer Vision
Theory and Applications (VISAPP)}
year = {2015}
}
Remark: The paper does not discuss the fully convolutional network adaptations integrated in CN24.
Questions?
If you have questions, feedback, or experience problems. Let us know and write an e-mail to Clemens-Alexander Brust, Sven Sickert, Marcel Simon, and Erik Rodner.