Dlib
Dlib
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Basic data
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Current version |
19.20 ( June 6, 2020 ) |
operating system | Platform independent |
programming language | C ++ |
License | Boost |
dlib.net |
Dlib is a free software - library with algorithms for machine learning , image processing and machine vision . It is written in the C ++ programming language and is available as free software under the terms of the Boost license. There is a connection for Python . Dlib is written in ISO standard C ++, is supplied in the source code and can be translated with CMake . It is therefore highly portable and can run on a wide variety of operating systems such as MS Windows, Linux or OS X. No other libraries are required to use the library. Only APIs that are provided by the respective operating system are required.
Since development began in 2002, Dlib has been expanded to include a large number of tools. From 2019, it will contain software components for dealing with networks , threads , graphical user interfaces , data structures , linear algebra , machine learning with artificial neural networks and deep learning , face recognition, face recognition with landmarks, image processing with object recognition via Speeded Up Robust Features (SURF) and HOG , Support vector machine , data mining , XML and text parsing, numerical optimization, Bayesian networks and many other tasks. In 2009 an article about Dlib was published in the Journal of Machine Learning Research .
Individual evidence
- ↑ Release 19.20 . June 6, 2020 (accessed June 15, 2020).
- ^ Matthew Mayo: 5 Machine Learning Projects You Can No Longer Overlook, January. KDnuggets, January 2017, accessed January 8, 2019 .
- ↑ Vikas Gupta: Face Detection - OpenCV, Dlib and Deep Learning | Learn OpenCV. October 22, 2018, accessed January 8, 2019 .
- ^ Adrian Rosebrock: (Faster) Facial landmark detector with dlib. In: PyImageSearch. April 2, 2018, Retrieved January 8, 2019 (American English).
- ↑ Arun Ponnusamy: CNN based face detector from dlib. Towards Data Science, April 17, 2018, accessed January 8, 2019 .
- ↑ Davis E. King: Dlib ml: A Machine Learning Toolkit. In: Journal of Machine Learning Research. July 2009, accessed January 8, 2019 .