Dlib: Modern C++/Python Toolkit for Machine Learning

Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems. It is used in both industry and academia in a wide range of domains including robotics, embedded devices, mobile phones, and large high performance computing environments. Dlib's open source licensing allows you to use it in any application, free of charge.

To follow or participate in the development of dlib subscribe to dlib on github. Also be sure to read the how to contribute page if you intend to submit code to the project.

To quickly get started using dlib, follow these instructions to build dlib.

Major Features

• Documentation
  • Unlike a lot of open source projects, this one provides complete and precise documentation for every class and function. There are also debugging modes that check the documented preconditions for functions. When this is enabled it will catch the vast majority of bugs caused by calling functions incorrectly or using objects in an incorrect manner.
  • Lots of example programs are provided
  • I consider the documentation to be the most important part of the library. So if you find anything that isn't documented, isn't clear, or has out of date documentation, tell me and I will fix it.
• High Quality Portable Code
  • Good unit test coverage. The ratio of unit test lines of code to library lines of code is about 1 to 4.
  • The library is tested regularly on MS Windows, Linux, and Mac OS X systems. However, it should work on any POSIX system and has been used on Solaris, HPUX, and the BSDs.
  • No other packages are required to use the library. Only APIs that are provided by an out of the box OS are needed.
  • There is no installation or configure step needed before you can use the library. See the How to compile page for details.
  • All operating system specific code is isolated inside the OS abstraction layers which are kept as small as possible. The rest of the library is either layered on top of the OS abstraction layers or is pure ISO standard C++.
• Machine Learning Algorithms
• Numerical Algorithms
• Graphical Model Inference Algorithms
  • Join tree algorithm for exact inference in a Bayesian network.
  • Gibbs sampler markov chain monte carlo algorithm for approximate inference in a Bayesian network.
  • Routines for performing MAP inference in chain-structured, Potts, or general factor graphs.
• Image Processing
• Threading
• Networking
• Graphical User Interfaces
  • The library provides a portable and simple core GUI API
  • Implemented on top of the core GUI API are numerous widgets
  • Unlike many other GUI toolkits, the entire dlib GUI toolkit is threadsafe
• Data Compression and Integrity Algorithms
  • A CRC 32 object
  • MD5 functions
  • Various abstracted objects representing parts of data compression algorithms. Many forms of the PPM algorithm are included.
• Testing
• General Utilities

from Hacker News http://dlib.net/