Add face detection or face recognition to a Python application using dlib's pre-built models with a single pip install.
Integrate machine learning algorithms into a C++ desktop or embedded application without pulling in a large ML framework.
Use dlib's computer vision tools to analyze images or video in a Python script for research or rapid prototyping.
| davisking/dlib | sogou/workflow | m2team/nanazip | |
|---|---|---|---|
| Stars | 14,379 | 14,349 | 14,306 |
| Language | C++ | C++ | C++ |
| Setup difficulty | moderate | hard | easy |
| Complexity | 4/5 | 5/5 | 2/5 |
| Audience | developer | developer | general |
Figures from each repo's GitHub metadata at analysis time.
Python users install via pip, C++ users need CMake and a C++11 compiler, optional GPU acceleration requires CUDA.
Dlib is a C++ library that provides tools for building machine learning applications and doing data analysis. It is aimed at software developers who want to integrate machine learning, computer vision, or data processing capabilities into their own programs. While the library is written in C++, it also has a Python interface, which means Python developers can use many of its features through the pip package manager without writing any C++ themselves. Machine learning, in this context, refers to software that learns patterns from data rather than following a fixed set of rules. Computer vision refers to software that analyzes and interprets images or video. Dlib packages a range of algorithms in both categories into a single library so developers do not have to implement them from scratch. To use dlib in a C++ project, you typically compile it along with your own code using a build tool called CMake, which automates the process of configuring and compiling large software projects. The repository includes a collection of example programs that demonstrate how to use different parts of the library, along with instructions for building them. For Python users, installation is a single pip command. The library is released under the Boost Software License, which is permissive: it can be used in open-source projects, closed-source products, and commercial software without restriction. Development has received partial support from the Intelligence Advanced Research Projects Activity (IARPA), a US government research organization, though the library is publicly available and not restricted to government use.
A C++ library with Python bindings providing machine learning and computer vision algorithms so developers can add AI capabilities to their own applications without building them from scratch.
Mainly C++. The stack also includes C++, Python, CMake.
Use freely in any project, open-source or commercial, with no restriction beyond keeping the copyright notice (Boost Software License).
Setup difficulty is rated moderate, with roughly 30min to a first successful run.
Mainly developer.
This repo across BitVibe Labs
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