Release Notes: This release contains a number of new features and bugfixes. Some highlights are a structural support vector machine method for learning to detect objects in images, and two new general purpose tools for solving the MAP problem in graphical models.
Release Notes: The major new feature in this release is a Python API for training histogram-of-oriented-gradient based object detectors and examples showing how to use this type of detector to perform real-time face detection. Simpler interfaces were provided for learning to solve assignment and multi-target tracking problems.
Release Notes: This release added a tool for training histogram-of-oriented-gradient based object detectors and examples showing how to use this type of detector to perform real-time face detection. It also added multi-threaded training options for the multiclass classifiers as well as numerous other usability improvements.
Release Notes: This release adds bound constrained non-linear optimizers using the BFGS and L-BFGS methods. It also includes a new tool for learning a max-margin Mahalanobis distance metric as well as routines for easily computing Felzenszwalb's 31 channel HOG image representation.
Release Notes: This release focused on improving the speed and usability of dlib's structural support vector machine solver. Two new tutorial style example programs showing how to use the solver from either C++ or Python were included.
Release Notes: This release added a tool for solving large scale support vector regression problems to the library as well as a structural SVM tool for learning BIO or BILOU style sequence tagging models. It also added Python interfaces to a number of dlib's machine learning tools.