Dlib

Dlib is a general purpose <a href="/facts/Cross-platform/16zqdvNR">cross-platform</a> software <a href="/facts/Library_(computing)/xRzL3P7q">library</a> written in the programming language <a href="/facts/C%252B%252B/Et0F2qn9">C++</a>. Its design is heavily influenced by ideas from <a href="/facts/Design_by_contract/PjacMRRd">design by contract</a> and <a href="/facts/Component-based_software_engineering/eW5wvSHC">component-based software engineering</a>. Thus it is, first and foremost, a set of independent software components. It is <a href="/facts/Open-source_software/HzTaB8E9">open-source software</a> released under a <a href="/facts/Boost_(C%252B%252B_libraries)/vGLGG51A">Boost Software License</a>.
Since development began in 2002, Dlib has grown to include a wide variety of tools. As of 2016, it contains software components for dealing with <a href="/facts/Computer_network/3w5RM99p">networking</a>, <a href="/facts/Thread_(computing)/hh8WHyPd">threads</a>, <a href="/facts/Graphical_user_interface/wBlQFWDP">graphical user interfaces</a>, <a href="/facts/Data_structure/yNsNvL1I">data structures</a>, <a href="/facts/Linear_algebra/8VaB4VWk">linear algebra</a>, <a href="/facts/Machine_learning/e0w0XJTu">machine learning</a>, <a href="/facts/Image_processing/zenalNCf">image processing</a>, <a href="/facts/Data_mining/AH53q5ac">data mining</a>, <a href="/facts/XML/clNgUfWk">XML</a> and text parsing, <a href="/facts/Numerical_optimization/oRn8Iv5I">numerical optimization</a>, <a href="/facts/Bayesian_network/Ry31vq6T">Bayesian networks</a>, and many other tasks. In recent years, much of the development has been focused on creating a broad set of statistical machine learning tools and in 2009 Dlib was published in the <a href="/facts/Journal_of_Machine_Learning_Research/DK49PLRF">Journal of Machine Learning Research</a>. Since then it has been used in a wide range of domains.[excessive citations]

Dlib open-in-new

Dlib