accord-net/framework

★ 4,544C#Audience · developerComplexity · 3/5LicenseSetup · moderate

Mindmap

mindmap
  root((Accord.NET))
    What it does
      Machine learning
      Statistics
      Computer vision
    Tech stack
      C# and .NET
      NuGet packages
      Mono for Linux
    Key patterns
      Learn method
      Trained model
      NuGet sub-packages
    Audience
      .NET developers
      Data scientists

mindmap root((Accord.NET)) What it does Machine learning Statistics Computer vision Tech stack C# and .NET NuGet packages Mono for Linux Key patterns Learn method Trained model NuGet sub-packages Audience .NET developers Data scientists

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Things people build with this

USE CASE 1

Train a machine learning model in C# for classification or prediction without switching to Python.

USE CASE 2

Process images and detect objects in a .NET app using built-in computer vision algorithms.

USE CASE 3

Run statistical tests and analyze data distributions in a Windows or cross-platform .NET application.

USE CASE 4

Build a Unity3D game that uses machine learning or image processing by adding Accord.NET compiled libraries to the Plugins folder.

Tech stack

C#.NETNuGetMonoVisual Studio

Getting it running

Difficulty · moderate Time to first run · 30min

Requires Visual Studio for Windows builds, Linux and macOS builds use Mono, an open-source .NET runtime.

LGPL 2.1, you can use this in open-source and commercial apps, but if you modify the library itself you must share those changes under the same license.

In plain English

Accord.NET is a broad scientific computing library for the .NET ecosystem, which is Microsoft's platform for building software in languages like C#. It covers machine learning, statistics, computer vision, and image processing, all packaged so that Windows and cross-platform .NET developers can use them without switching to Python or another language typically used for this kind of work. The library formed by merging two earlier projects, Accord.NET and AForge.NET, which means it carries a long history of contributions and algorithms. The core design follows a consistent pattern: pick a learning algorithm, call its Learn method with your data, and get back a trained model. You then call methods on that model to make predictions, calculate probabilities, or score new inputs. This pattern stays the same whether you are training a support vector machine, a neural network, a Bayesian classifier, or another method from the library's large catalog. Installation is done through NuGet, the standard package manager for .NET projects. The library is split into focused sub-packages such as Accord.MachineLearning, Accord.Math, Accord.Statistics, and Accord.Imaging, so you only install what your project needs. For Unity3D game projects, the README provides a separate path using compiled library files placed in the Unity Plugins folder. Building the framework from source requires Visual Studio (2015 or 2017 are both described) with a few additional tools for documentation and testing. Linux and macOS builds are supported through Mono, which is an open-source implementation of the .NET runtime. The repository includes a large set of sample applications that demonstrate the library's features and serve as a starting point. The project is licensed under the LGPL 2.1 license, which allows use in both open-source and commercial applications under certain conditions. Contributions are welcomed in the form of documentation edits, bug reports, pull requests, or financial support.

Copy-paste prompts

Prompt 1

Using Accord.NET in C#, write a support vector machine classifier that trains on a small dataset and predicts labels for new inputs.

Prompt 2

I want to do image processing in a C# .NET project with Accord.Imaging, show me how to detect edges in a bitmap.

Prompt 3

Help me set up Accord.MachineLearning via NuGet and train a naive Bayes text classifier in C#.

Prompt 4

Using Accord.NET statistical methods, calculate a t-test p-value for two sample groups in C#.

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