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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. 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. 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. All operating system specific code is isolated inside the OS abstraction layers which are kept as small as possible.
Features
- Conventional SMO based Support Vector Machines for classification and regression
- Reduced-rank methods for large-scale classification and regression
- A tool for solving the optimization problem associated with structural support vector machines
- Structural SVM tools for object detection in images as well as more powerful deep learning tools for object detection
- An online kernelized centroid estimator/novelty detector and offline support vector one-class classification
- Clustering algorithms, linear or kernel k-means, Chinese Whispers, and Newman clustering
Project Samples
