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"\" DGM lib

Machine Learning
Conditional Random Fields


DGM is a cross-platform C++ library implementing various tasks in probabilistic graphical models with pairwise or complete (dense) dependencies. The library aims to be used for the Markov and Conditional Random Fields (MRF / CRF), Markov Chains, Bayesian and Neural Networks, etc. Specifically, it includes a variety of methods for the following tasks:

  • Learning: Training of unary and pairwise potentials
  • Inference / Decoding: Computing the conditional probabilities and the most likely configuration
  • Parameter Estimation: Computing maximum likelihood (or MAP) estimates of the parameters
  • Evaluation / Visualization: Evaluation and visualization of the classification results
  • Data Analysis: Extraction, analysis and visualization of valuable knowledge from training data
  • Feature Extraction: Extraction of various descriptors from images, which are useful for classification

DGM is released under a BSD license and hence it is free for both academic and commercial use.


Low overhead, DGM has only one external dependency: OpenCV.  Optimized for high-efficient calculations and takes advantage of multi-core processing as well as GPU computing.

Batteries Included

Comes out of the box with everything you need to create your first machine learning application. A selection of demo projects may serve as the basis for your own application.


DGM is a cross-platform, dynamic-link library, meant to be used in Windows, Mac and Linux. Its C++17 code is compiled with Microsoft Visual Studio, Xcode and gcc.

Application Examples

If you have applied the DGM library in your work and achieved some interesting results, please send them to me via, so I could allocate them at this web-page. This will help us to spread and improve the library. We thank you in advance for your support.



Download the source code:

Win32 Binary

Download the DGM library with precompiled Win32 vc15 binaries:

Win64 Binary

Download the DGM library with precompiled x64 vc15 binaries:

MacOS Binary

Download the DGM library with precompiled Xcode binaries:

Note By installing, copying, or otherwise using this software, you agree to be bound by the terms of its license. Read the license.

DGM in Publication

To reference DGM in a publication, please include the library name and a link to this website [BibTeX]. You may also want to include the library version, since we currently update the software.


If you use this software in a publication, please cite the work using the following information:

Sergey Kosov. Direct graphical models C++ library., 2013.

or using the BibTeX file.