stable (production branch): 
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 in the Computer Vision Group at the University of Jena.
- 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
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.
CN24 is available under a 3-clause BSD license. See LICENSE for details. If you use CN24 for research, please cite our paper Clemens-Alexander Brust, Sven Sickert, Marcel Simon, Erik Rodner, Joachim Denzler. "Convolutional Patch Networks with Spatial Prior for Road Detection and Urban Scene Understanding". VISAPP 2015.
Remark: The paper does not discuss the fully convolutional network adaptations integrated in CN24.
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.