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Original author(s) | Andrew Kirillov |
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Initial release | December 21, 2006 [1] |
Stable release | 2.2.5 / July 17, 2013 |
Written in | C# |
Operating system | Cross-platform |
Type | Framework |
License | LGPLv3 and partly GPLv3 |
Website | www |
AForge.NET is a computer vision and artificial intelligence library originally developed by Andrew Kirillov for the .NET Framework. [2]
The source code and binaries of the project are available under the terms of the Lesser GPL and the GPL (GNU General Public License).[ citation needed ]
Another (unaffiliated) project called Accord.NET was created to extend the features of the original AForge.NET library. [3]
On April 1, 2012, Andrew Kirillov announced the end of the public support for the library, temporarily closing the discussion forums. The last release of the AForge.NET Framework was made available on July 17, 2013. However, since its release 3.0 in 2015, the Accord.NET project started to incorporate most of the original AForge.NET source code in its codebase, continuing its support and development under the Accord.NET name. [3]
The framework's API includes support for:
The framework is provided not only with different libraries and their sources, but with many sample applications, which demonstrate the use of this framework, and with documentation help files, which are provided in HTML Help format. A number of software applications [5] [6] [7] [8] and research works [9] [10] [11] utilized the framework.
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OpenCV is a library of programming functions mainly for real-time computer vision. Originally developed by Intel, it was later supported by Willow Garage, then Itseez. The library is cross-platform and licensed as free and open-source software under Apache License 2. Starting in 2011, OpenCV features GPU acceleration for real-time operations.
This is an alphabetical list of articles pertaining specifically to software engineering.
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Accord.NET is a framework for scientific computing in .NET. The source code of the project is available under the terms of the Gnu Lesser Public License, version 2.1.
This glossary of artificial intelligence is a list of definitions of terms and concepts relevant to the study of artificial intelligence, its sub-disciplines, and related fields. Related glossaries include Glossary of computer science, Glossary of robotics, and Glossary of machine vision.
Chainer is an open source deep learning framework written purely in Python on top of NumPy and CuPy Python libraries. The development is led by Japanese venture company Preferred Networks in partnership with IBM, Intel, Microsoft, and Nvidia.
The following outline is provided as an overview of and topical guide to machine learning:
Caffe is a deep learning framework, originally developed at University of California, Berkeley. It is open source, under a BSD license. It is written in C++, with a Python interface.
ML.NET is a free software machine learning library for the C# and F# programming languages. It also supports Python models when used together with NimbusML. The preview release of ML.NET included transforms for feature engineering like n-gram creation, and learners to handle binary classification, multi-class classification, and regression tasks. Additional ML tasks like anomaly detection and recommendation systems have since been added, and other approaches like deep learning will be included in future versions.
In computer vision, SqueezeNet is the name of a deep neural network for image classification that was released in 2016. SqueezeNet was developed by researchers at DeepScale, University of California, Berkeley, and Stanford University. In designing SqueezeNet, the authors' goal was to create a smaller neural network with fewer parameters while achieving competitive accuracy.
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