{"id":46443,"url":"https://github.com/coderonion/awesome-dotnet-machine-learning","name":"awesome-dotnet-machine-learning","description":"A collection of some awesome public machine learning framework, tutorial, blogs, library and applications for .NET.","projects_count":134,"last_synced_at":"2026-06-10T11:00:19.481Z","repository":{"id":129483875,"uuid":"522114289","full_name":"coderonion/awesome-dotnet-machine-learning","owner":"coderonion","description":"A collection of some awesome public machine learning framework, tutorial, blogs, library and applications for .NET.","archived":false,"fork":false,"pushed_at":"2023-09-26T14:26:26.000Z","size":22,"stargazers_count":56,"open_issues_count":1,"forks_count":7,"subscribers_count":3,"default_branch":"main","last_synced_at":"2026-05-04T21:49:33.411Z","etag":null,"topics":["ai","chatgpt","computer-vision","csharp","cuda","deep-learning","donet-core","dotnet","linux","machine-learning","maui","microsoft","object-detection","opencv","pytorch","tensorflow","unity","wpf","yolo","yolov5"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/coderonion.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2022-08-07T04:20:40.000Z","updated_at":"2026-04-17T14:12:40.000Z","dependencies_parsed_at":"2023-11-14T15:40:29.948Z","dependency_job_id":null,"html_url":"https://github.com/coderonion/awesome-dotnet-machine-learning","commit_stats":null,"previous_names":["codingonion/awesome-dotnet-machine-learning","coderonion/awesome-dotnet-machine-learning","sjinzh/awesome-dotnet-machine-learning"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/coderonion/awesome-dotnet-machine-learning","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/coderonion%2Fawesome-dotnet-machine-learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/coderonion%2Fawesome-dotnet-machine-learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/coderonion%2Fawesome-dotnet-machine-learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/coderonion%2Fawesome-dotnet-machine-learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/coderonion","download_url":"https://codeload.github.com/coderonion/awesome-dotnet-machine-learning/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/coderonion%2Fawesome-dotnet-machine-learning/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":34149132,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-10T02:00:07.152Z","response_time":89,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"created_at":"2024-01-14T11:14:57.760Z","updated_at":"2026-06-10T11:00:19.481Z","primary_language":"C#","list_of_lists":false,"displayable":true,"categories":["Uncategorized"],"sub_categories":["Uncategorized"],"readme":"# Awesome-Dotnet-Machine-Learning\n[![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)\n\n🔥🔥🔥 This repository lists some awesome public machine learning framework, tutorial, blogs, library and applications for .NET.\n\n\n## Contents\n- [Awesome-Dotnet-Machine-Learning](#awesome-dotnet-machine-learning)\n  - [Framework](#framework)\n  - [Tutorial](#tutorial)\n  - [Library](#library)\n    - [FFI Bindings](#ffi-bindings)\n    - [GPU Integration](#gpu-integration)\n    - [Image and Video Processing](#image-and-video-processing)\n    - [Scientific Computation](#scientific-computation)\n    - [Data Analysis](#data-analysis)\n    - [Data Visualization](#data-visualization)\n  - [Applications](#applications)\n    - [Robotics AI](#robotics-ai)\n    - [Simulation Engine](#simulation-engine)\n    - [Chatbot Platform](#chatbot-platform)\n    - [Natural Language Processing](#natural-language-processing)\n    - [Object Classification](#object-classification)\n    - [Object Detection](#object-detection)\n    - [Face Detection](#face-detection)\n    - [Face Recognition](#face-recognition)\n    - [Game Field](#game-field)\n \n\n- ## Framework\n\n  - [SynapseML](https://github.com/microsoft/SynapseML) \u003cimg src=\"https://img.shields.io/github/stars/microsoft/SynapseML?style=social\"/\u003e : SynapseML (previously known as MMLSpark), is an open-source library that simplifies the creation of massively scalable machine learning (ML) pipelines. SynapseML provides simple, composable, and distributed APIs for a wide variety of different machine learning tasks such as text analytics, vision, anomaly detection, and many others.  \n\n  - [ML.NET](https://github.com/dotnet/machinelearning) \u003cimg src=\"https://img.shields.io/github/stars/dotnet/machinelearning?style=social\"/\u003e : ML.NET is an open source and cross-platform machine learning framework for .NET. \n\n  - [TorchSharp](https://github.com/dotnet/TorchSharp) \u003cimg src=\"https://img.shields.io/github/stars/dotnet/TorchSharp?style=social\"/\u003e : A .NET library that provides access to the library that powers PyTorch.    \n\n  - [TensorFlow.NET](https://github.com/SciSharp/TensorFlow.NET) \u003cimg src=\"https://img.shields.io/github/stars/SciSharp/TensorFlow.NET?style=social\"/\u003e : .NET Standard bindings for Google's TensorFlow for developing, training and deploying Machine Learning models in C# and F#.   \n\n  - [DlibDotNet](https://github.com/takuya-takeuchi/DlibDotNet) \u003cimg src=\"https://img.shields.io/github/stars/takuya-takeuchi/DlibDotNet?style=social\"/\u003e : Dlib .NET wrapper written in C++ and C# for Windows, MacOS, Linux and iOS.\n  \n  - [DiffSharp](https://github.com/DiffSharp/DiffSharp) \u003cimg src=\"https://img.shields.io/github/stars/DiffSharp/DiffSharp?style=social\"/\u003e : DiffSharp: Differentiable Functional Programming.\n\n  - [KelpNet](https://github.com/harujoh/KelpNet) \u003cimg src=\"https://img.shields.io/github/stars/harujoh/KelpNet?style=social\"/\u003e : KelpNet : Pure C# machine learning framework.\n\n  - [Bright Wire](https://github.com/jdermody/brightwire) \u003cimg src=\"https://img.shields.io/github/stars/jdermody/brightwire?style=social\"/\u003e : Bright Wire is an open source machine learning library for .NET with GPU support (via CUDA).\n\n  - [SharpNet](https://github.com/FranckZibi/SharpNet) \u003cimg src=\"https://img.shields.io/github/stars/FranckZibi/SharpNet?style=social\"/\u003e : Open-source Deep Learning library in C# with CUDA and BLAS support. \n\n  - [MyCaffe](https://github.com/MyCaffe/MyCaffe) \u003cimg src=\"https://img.shields.io/github/stars/MyCaffe/MyCaffe?style=social\"/\u003e : A complete deep learning platform written almost entirely in C# for Windows developers! Now you can write your own layers in C#! \n\n  - [Torch.NET](https://github.com/SciSharp/Torch.NET) \u003cimg src=\"https://img.shields.io/github/stars/SciSharp/Torch.NET?style=social\"/\u003e : .NET bindings for PyTorch. Machine Learning with C# / F# with Multi-GPU/CPU support.\n\n  - [Keras.NET](https://github.com/SciSharp/Keras.NET) \u003cimg src=\"https://img.shields.io/github/stars/SciSharp/Keras.NET?style=social\"/\u003e : Keras.NET is a high-level neural networks API for C# and F#, with Python Binding and capable of running on top of TensorFlow, CNTK, or Theano. \n\n  - [MxNet.Sharp](https://github.com/deepakkumar1984/MxNet.Sharp) \u003cimg src=\"https://img.shields.io/github/stars/deepakkumar1984/MxNet.Sharp?style=social\"/\u003e : .NET Standard bindings for Apache MxNet with Imperative, Symbolic and Gluon Interface for developing, training and deploying Machine Learning models in C#. \n\n  - [ConvNetSharp](https://github.com/cbovar/ConvNetSharp) \u003cimg src=\"https://img.shields.io/github/stars/cbovar/ConvNetSharp?style=social\"/\u003e : Started initially as C# port of [ConvNetJS](https://github.com/karpathy/convnetjs). You can use ConvNetSharp to train and evaluate convolutional neural networks (CNN).\n\n  - [xin-pu/Machine-Learning](https://github.com/xin-pu/Machine-Learning) \u003cimg src=\"https://img.shields.io/github/stars/xin-pu/Machine-Learning?style=social\"/\u003e : Deep learning Tool by C#.\n\n  - [LibSvmDotNet](https://github.com/takuya-takeuchi/LibSvmDotNet) \u003cimg src=\"https://img.shields.io/github/stars/takuya-takeuchi/LibSvmDotNet?style=social\"/\u003e : .NET wrapper for LIBSVM written in C#.\n\n  - [System.AI](https://github.com/ColorfulSoft/System.AI) \u003cimg src=\"https://img.shields.io/github/stars/ColorfulSoft/System.AI?style=social\"/\u003e : Machine Learning and Data Analysis stack for .NET ecosystem.\n\n  - [DeOldify.NET](https://github.com/ColorfulSoft/DeOldify.NET) \u003cimg src=\"https://img.shields.io/github/stars/ColorfulSoft/DeOldify.NET?style=social\"/\u003e : C# implementation of Jason Antic's DeOldify.\n\n  - [NeuralNetwork.NET](https://github.com/Sergio0694/NeuralNetwork.NET) \u003cimg src=\"https://img.shields.io/github/stars/Sergio0694/NeuralNetwork.NET?style=social\"/\u003e : NeuralNetwork.NET is a .NET Standard 2.0 library that implements sequential and computation graph neural networks with customizable layers, built from scratch with C#.\n\n  - [AForge.NET](https://github.com/andrewkirillov/AForge.NET) \u003cimg src=\"https://img.shields.io/github/stars/andrewkirillov/AForge.NET?style=social\"/\u003e : AForge.NET Framework is a C# framework designed for developers and researchers in the fields of Computer Vision and Artificial Intelligence - image processing, neural networks, genetic algorithms, machine learning, robotics, etc. http://www.aforgenet.com/framework/\n\n  - [Accord.NET](https://github.com/accord-net/framework) \u003cimg src=\"https://img.shields.io/github/stars/accord-net/framework?style=social\"/\u003e : Machine learning, computer vision, statistics and general scientific computing for .NET. http://accord-framework.net/\n\n  - [MLOps.NET](https://github.com/aslotte/MLOps.NET) \u003cimg src=\"https://img.shields.io/github/stars/aslotte/MLOps.NET?style=social\"/\u003e : A machine learning model operations and management tool for ML.NET \n\n\n- ## Tutorial\n\n  - [Microsoft AI Lab](https://github.com/microsoft/ailab) \u003cimg src=\"https://img.shields.io/github/stars/microsoft/ailab?style=social\"/\u003e : Experience, Learn and Code the latest breakthrough innovations with Microsoft AI.\n\n  - [Microsoft SynapseML](https://microsoft.github.io/SynapseML) : Simple and Distributed Machine Learning.\n\n  - [Microsoft ML.NET](https://dotnet.microsoft.com/en-us/apps/machinelearning-ai/ml-dotnet) : ML.NET An open source and cross-platform machine learning framework. ML.NET 开放源代码的跨平台机器学习框架。\n\n  - [Microsoft ML.NET Samples](https://github.com/dotnet/machinelearning-samples) \u003cimg src=\"https://img.shields.io/github/stars/dotnet/machinelearning-samples?style=social\"/\u003e : Samples for ML.NET, an open source and cross-platform machine learning framework for .NET.  \n\n  - [TorchSharp Examples](https://github.com/dotnet/TorchSharpExamples) \u003cimg src=\"https://img.shields.io/github/stars/dotnet/TorchSharpExamples?style=social\"/\u003e : Repository for TorchSharp examples and tutorials. \n\n  - [.NET Interactive Notebooks](https://github.com/dotnet/csharp-notebooks) \u003cimg src=\"https://img.shields.io/github/stars/dotnet/csharp-notebooks?style=social\"/\u003e : .NET Interactive Notebooks for C#. Get started learning C# with C# notebooks powered by .NET Interactive and VS Code. [kinfey/csharp-notebookss](https://github.com/kinfey/csharp-notebookss)\n\n  - [Hands-On-Machine-Learning-With-ML.NET](https://github.com/PacktPublishing/Hands-On-Machine-Learning-With-ML.NET) \u003cimg src=\"https://img.shields.io/github/stars/PacktPublishing/Hands-On-Machine-Learning-With-ML.NET?style=social\"/\u003e : Hands-On Machine Learning with ML.NET, published by Packt.\n\n  - [jeffprosise/ML.NET](https://github.com/jeffprosise/ML.NET) \u003cimg src=\"https://img.shields.io/github/stars/jeffprosise/ML.NET?style=social\"/\u003e : Code samples utilizing Microsoft's ML.NET machine-learning library.\n\n\n\n- ## Library\n\n  - ### FFI Bindings \n\n    - [PInvoke.net](https://pinvoke.net/) : PInvoke.net is primarily a wiki, allowing developers to find, edit and add PInvoke* signatures, user-defined types, and any other information related to calling Win32 and other unmanaged APIs from managed code (written in languages such as C# or VB.NET).\n\n    - [PInvoke](https://github.com/dotnet/pinvoke) \u003cimg src=\"https://img.shields.io/github/stars/dotnet/pinvoke?style=social\"/\u003e : A library containing all P/Invoke code so you don't have to import it every time. Maintained and updated to support the latest Windows OS.\n\n    - [CppSharp](https://github.com/mono/CppSharp) \u003cimg src=\"https://img.shields.io/github/stars/mono/CppSharp?style=social\"/\u003e : CppSharp is a tool and set of libraries which facilitates the usage of native C/C++ code with the .NET ecosystem.\n\n    - [ClangSharp](https://github.com/dotnet/ClangSharp) \u003cimg src=\"https://img.shields.io/github/stars/dotnet/ClangSharp?style=social\"/\u003e : Clang bindings for .NET written in C#.\n\n    - [Python.NET](https://github.com/pythonnet/pythonnet) \u003cimg src=\"https://img.shields.io/github/stars/pythonnet/pythonnet?style=social\"/\u003e : Python.NET is a package that gives Python programmers nearly seamless integration with the .NET Common Language Runtime (CLR) and provides a powerful application scripting tool for .NET developers.\n\n    - [C2CS](https://github.com/bottlenoselabs/c2cs) \u003cimg src=\"https://img.shields.io/github/stars/bottlenoselabs/c2cs?style=social\"/\u003e : Generate C# bindings from a C header. \n\n    - [Diplomat](https://github.com/rust-diplomat/diplomat) \u003cimg src=\"https://img.shields.io/github/stars/rust-diplomat/diplomat?style=social\"/\u003e : Experimental Rust tool for generating FFI definitions allowing many other languages to call Rust code.\n\n\n\n  - ### GPU Integration \n\n    - [Silk.NET](https://github.com/dotnet/Silk.NET) \u003cimg src=\"https://img.shields.io/github/stars/dotnet/Silk.NET?style=social\"/\u003e : The high-speed OpenGL, OpenCL, OpenAL, OpenXR, GLFW, SDL, Vulkan, Assimp, and DirectX bindings library your mother warned you about. \n\n    - [Vortice.Vulkan](https://github.com/amerkoleci/Vortice.Vulkan) \u003cimg src=\"https://img.shields.io/github/stars/amerkoleci/Vortice.Vulkan?style=social\"/\u003e : Cross platform .NET bindings for Vulkan, VMA, SPIRV-Cross and shaderc.\n\n    - [OpenTK](https://github.com/opentk/opentk) \u003cimg src=\"https://img.shields.io/github/stars/opentk/opentk?style=social\"/\u003e : The Open Toolkit library is a fast, low-level C# wrapper for OpenGL, OpenAL \u0026 OpenCL. It also includes windowing, mouse, keyboard and joystick input and a robust and fast math library, giving you everything you need to write your own renderer or game engine. OpenTK can be used standalone or inside a GUI on Windows, Linux, Mac. \n\n    - [ComputeSharp](https://github.com/Sergio0694/ComputeSharp) \u003cimg src=\"https://img.shields.io/github/stars/Sergio0694/ComputeSharp?style=social\"/\u003e : A .NET library to run C# code in parallel on the GPU through DX12, D2D1 and dynamically generated HLSL compute shaders, with the goal of making GPU computing easy to use for all .NET developers! 🚀\n\n    - [ILGPU](https://github.com/m4rs-mt/ILGPU) \u003cimg src=\"https://img.shields.io/github/stars/m4rs-mt/ILGPU?style=social\"/\u003e : ILGPU is a JIT (just-in-time) compiler for high-performance GPU programs written in .Net-based languages. \n\n    - [Barracuda](https://github.com/Unity-Technologies/barracuda-release) \u003cimg src=\"https://img.shields.io/github/stars/Unity-Technologies/barracuda-release?style=social\"/\u003e : Unity Barracuda is a lightweight cross-platform Neural Networks inference library for Unity. Barracuda can run Neural Networks both on GPU and CPU.\n\n    - [ManagedCUDA](https://github.com/kunzmi/managedCuda) \u003cimg src=\"https://img.shields.io/github/stars/kunzmi/managedCuda?style=social\"/\u003e : ManagedCUDA aims an easy integration of NVidia's CUDA in .net applications written in C#, Visual Basic or any other .net language. \n\n    - [Amplifier.NET](https://github.com/deepakkumar1984/Amplifier.NET) \u003cimg src=\"https://img.shields.io/github/stars/deepakkumar1984/Amplifier.NET?style=social\"/\u003e : Amplifier allows .NET developers to easily run complex applications with intensive mathematical computation on Intel CPU/GPU, NVIDIA, AMD without writing any additional C kernel code. Write your function in .NET and Amplifier will take care of running it on your favorite hardware.  \n\n    - [vk](https://github.com/mellinoe/vk) \u003cimg src=\"https://img.shields.io/github/stars/mellinoe/vk?style=social\"/\u003e : Low-level Vulkan bindings for .NET.\n\n    - [VulkanCore](https://github.com/discosultan/VulkanCore) \u003cimg src=\"https://img.shields.io/github/stars/discosultan/VulkanCore?style=social\"/\u003e : Vulkan 1.0 graphics and compute API bindings for .NET Standard.\n\n\n\n\n  - ### Image and Video Processing\n\n    - [ImageSharp](https://github.com/SixLabors/ImageSharp) \u003cimg src=\"https://img.shields.io/github/stars/SixLabors/ImageSharp?style=social\"/\u003e : ImageSharp is a new, fully featured, fully managed, cross-platform, 2D graphics API. \n\n    - [OpenCvSharp](https://github.com/shimat/opencvsharp) \u003cimg src=\"https://img.shields.io/github/stars/shimat/opencvsharp?style=social\"/\u003e : OpenCV wrapper for .NET. \n\n    - [SharpCV](https://github.com/SciSharp/SharpCV) \u003cimg src=\"https://img.shields.io/github/stars/SciSharp/SharpCV?style=social\"/\u003e : A Computer Vision library for C# and F# that combines OpenCV and NDArray together in .NET Standard. \n\n    - [FFmpeg.AutoGen](https://github.com/Ruslan-B/FFmpeg.AutoGen) \u003cimg src=\"https://img.shields.io/github/stars/Ruslan-B/FFmpeg.AutoGen?style=social\"/\u003e : FFmpeg auto generated unsafe bindings for C#/.NET and Core (Linux, MacOS and Mono).\n\n    - [Sdcb.FFmpeg](https://github.com/sdcb/Sdcb.FFmpeg) \u003cimg src=\"https://img.shields.io/github/stars/sdcb/Sdcb.FFmpeg?style=social\"/\u003e : FFmpeg basic .NET API generated by CppSharp.\n\n\n\n\n\n  - ### Scientific Computation\n\n    - [Math.NET](https://www.mathdotnet.com/) : Math.NET is an opensource initiative to build and maintain toolkits covering fundamental mathematics, targetting advanced but also every day needs of .Net developers. \n\n    - [Math.NET Numerics](https://github.com/mathnet/mathnet-numerics) \u003cimg src=\"https://img.shields.io/github/stars/mathnet/mathnet-numerics?style=social\"/\u003e : Math.NET Numerics is the numerical foundation of the Math.NET initiative, aiming to provide methods and algorithms for numerical computations in science, engineering and every day use. Covered topics include special functions, linear algebra, probability models, random numbers, statistics, interpolation, integration, regression, curve fitting, integral transforms (FFT) and more.\n\n    - [Math.NET Spatial](https://github.com/mathnet/mathnet-spatial) \u003cimg src=\"https://img.shields.io/github/stars/mathnet/mathnet-spatial?style=social\"/\u003e : Math.NET Spatial is an opensource geometry library for .Net, Silverlight and Mono. \n\n    - [Math.NET Filtering](https://github.com/mathnet/mathnet-filtering) \u003cimg src=\"https://img.shields.io/github/stars/mathnet/mathnet-filtering?style=social\"/\u003e : Math.NET Filtering is a digital signal processing toolkit, offering an infrastructure for digital filter design, applying those filters to data streams using data converters, as well as digital signal generators. \n\n    - [Numpy.NET](https://github.com/SciSharp/Numpy.NET) \u003cimg src=\"https://img.shields.io/github/stars/SciSharp/Numpy.NET?style=social\"/\u003e : C#/F# bindings for NumPy - a fundamental library for scientific computing, machine learning and AI.\n\n    - [NumSharp](https://github.com/SciSharp/NumSharp) \u003cimg src=\"https://img.shields.io/github/stars/SciSharp/NumSharp?style=social\"/\u003e : High Performance Computation for N-D Tensors in .NET, similar API to NumPy. \n\n    - [AngouriMath](https://github.com/asc-community/AngouriMath) \u003cimg src=\"https://img.shields.io/github/stars/asc-community/AngouriMath?style=social\"/\u003e : New open-source cross-platform symbolic algebra library for C# and F#. Can be used for both production and research purposes. \n\n\n  - ### Data Analysis\n\n    - [Deedle](https://github.com/fslaborg/Deedle) \u003cimg src=\"https://img.shields.io/github/stars/fslaborg/Deedle?style=social\"/\u003e : Deedle is an easy to use library for data and time series manipulation and for scientific programming.\n\n    - [Pandas.NET](https://github.com/SciSharp/Pandas.NET) \u003cimg src=\"https://img.shields.io/github/stars/SciSharp/Pandas.NET?style=social\"/\u003e : Pandas port for C# and F#, data analysis tool, process multi-dim array in DataFrame. \n\n\n\n  - ### Data Visualization\n\n    - [LiveCharts2](https://github.com/beto-rodriguez/LiveCharts2) \u003cimg src=\"https://img.shields.io/github/stars/beto-rodriguez/LiveCharts2?style=social\"/\u003e : Simple, flexible, interactive \u0026 powerful charts, maps and gauges for .Net, LiveCharts2 can now practically run everywhere Maui, Uno Platform, Blazor-wasm, WPF, WinForms, Xamarin, Avalonia, WinUI, UWP. \n\n     - [OxyPlot](https://github.com/oxyplot/oxyplot) \u003cimg src=\"https://img.shields.io/github/stars/oxyplot/oxyplot?style=social\"/\u003e : OxyPlot is a cross-platform plotting library for .NET.\n\n    - [ScottPlot](https://github.com/ScottPlot/ScottPlot) \u003cimg src=\"https://img.shields.io/github/stars/ScottPlot/ScottPlot?style=social\"/\u003e : ScottPlot is a free and open-source plotting library for .NET that makes it easy to interactively display large datasets.\n\n    - [Plotly.NET](https://github.com/plotly/Plotly.NET) \u003cimg src=\"https://img.shields.io/github/stars/plotly/Plotly.NET?style=social\"/\u003e : Plotly.NET provides functions for generating and rendering plotly.js charts in .NET programming languages 📈🚀.\n\n    - [swharden/Csharp-Data-Visualization](https://github.com/swharden/Csharp-Data-Visualization) \u003cimg src=\"https://img.shields.io/github/stars/swharden/Csharp-Data-Visualization?style=social\"/\u003e : Resources for visualizing data using C# and the .NET platform.\n\n\n\n\n- ## Applications\n\n  - ### Robotics AI\n\n    - [ROS2 for .NET](https://github.com/ros2-dotnet/ros2_dotnet) \u003cimg src=\"https://img.shields.io/github/stars/ros2-dotnet/ros2_dotnet?style=social\"/\u003e : This is a collection of projects (bindings, code generator, examples and more) for writing ROS2 applications for .NET Core and .NET Standard.\n\n    - [Ros2 For Unity](https://github.com/RobotecAI/ros2-for-unity) \u003cimg src=\"https://img.shields.io/github/stars/RobotecAI/ros2-for-unity?style=social\"/\u003e : ROS2 For Unity is a high-performance communication solution to connect Unity3D and ROS2 ecosystem in a ROS2 \"native\" way.\n\n    - [Ros2cs](https://github.com/RobotecAI/ros2cs) \u003cimg src=\"https://img.shields.io/github/stars/RobotecAI/ros2cs?style=social\"/\u003e : A C# .NET library for ROS2, including C# implementation of rcl APIs, message generation, tests and examples.\n\n    - [dotnet-state-machine/stateless](https://github.com/dotnet-state-machine/stateless) \u003cimg src=\"https://img.shields.io/github/stars/dotnet-state-machine/stateless?style=social\"/\u003e : A simple library for creating state machines in C# code.\n\n\n     \n  - ### Simulation Engine\n\n    - [Unity ML-Agents Toolkit](https://github.com/Unity-Technologies/ml-agents) \u003cimg src=\"https://img.shields.io/github/stars/Unity-Technologies/ml-agents?style=social\"/\u003e : The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning. \n\n    - [AR Foundation Samples](https://github.com/Unity-Technologies/arfoundation-samples) \u003cimg src=\"https://img.shields.io/github/stars/Unity-Technologies/arfoundation-samples?style=social\"/\u003e : Example content for Unity projects based on AR Foundation.\n\n    - [MRTK-Unity](https://github.com/microsoft/MixedRealityToolkit-Unity) \u003cimg src=\"https://img.shields.io/github/stars/microsoft/MixedRealityToolkit-Unity?style=social\"/\u003e : Mixed Reality Toolkit (MRTK) provides a set of components and features to accelerate cross-platform MR app development in Unity. \n\n    - [SVL Simulator](https://github.com/lgsvl/simulator) \u003cimg src=\"https://img.shields.io/github/stars/lgsvl/simulator?style=social\"/\u003e : SVL Simulator: An Autonomous Vehicle Simulator. A ROS/ROS2 Multi-robot Simulator for Autonomous Vehicles. LG Electronics America R\u0026D Lab has developed an HDRP Unity-based multi-robot simulator for autonomous vehicle developers.\n\n    - [NatML](https://github.com/natmlx/NatML) \u003cimg src=\"https://img.shields.io/github/stars/natmlx/NatML?style=social\"/\u003e : High performance, cross-platform machine learning for Unity Engine. Register at https://hub.natml.ai \n\n\n\n  - ### Chatbot Platform \n\n    - [BotSharp](https://github.com/SciSharp/BotSharp) \u003cimg src=\"https://img.shields.io/github/stars/SciSharp/BotSharp?style=social\"/\u003e : The Open Source AI Chatbot Platform Builder in 100% C# Running in .NET Core with Machine Learning algorithm. \n\n\n\n  - ### Natural Language Processing\n\n    - [chatGPTLineBot](https://github.com/isdaviddong/chatGPTLineBot) \u003cimg src=\"https://img.shields.io/github/stars/isdaviddong/chatGPTLineBot?style=social\"/\u003e : ChatGPT LINE Bot.\n\n    - [Kengxxiao/Himari.ChatGPT](https://github.com/Kengxxiao/Himari.ChatGPT) \u003cimg src=\"https://img.shields.io/github/stars/Kengxxiao/Himari.ChatGPT?style=social\"/\u003e : 使用ChatGPT的QQ机器人的简单实现。\n\n    - [wieslawsoltes/ChatGPT](https://github.com/wieslawsoltes/ChatGPT) \u003cimg src=\"https://img.shields.io/github/stars/wieslawsoltes/ChatGPT?style=social\"/\u003e : A ChatGPT C# client for console and graphical user interface.\n\n    - [IronWarrior/ChatGPT-2DPhysics](https://github.com/IronWarrior/ChatGPT-2DPhysics) \u003cimg src=\"https://img.shields.io/github/stars/IronWarrior/ChatGPT-2DPhysics?style=social\"/\u003e : This repository contains a simple 2D Physics engine in C#, with code written by ChatGPT. It uses the console for graphics.\n\n    - [PolarisAI](https://github.com/MeiFagundes/PolarisAI) \u003cimg src=\"https://img.shields.io/github/stars/MeiFagundes/PolarisAI?style=social\"/\u003e : Personal Assistant Engine built with ML.NET. \n\n    - [Stanford.NLP.NET](https://github.com/sergey-tihon/Stanford.NLP.NET) \u003cimg src=\"https://img.shields.io/github/stars/sergey-tihon/Stanford.NLP.NET?style=social\"/\u003e : Stanford NLP for .NET. \n\n    - [Catalyst](https://github.com/curiosity-ai/catalyst) \u003cimg src=\"https://img.shields.io/github/stars/curiosity-ai/catalyst?style=social\"/\u003e : 🚀 Catalyst is a C# Natural Language Processing library built for speed. Inspired by spaCy's design, it brings pre-trained models, out-of-the box support for training word and document embeddings, and flexible entity recognition models. \n\n\n  - ### Object Classification\n\n    - [doughtmw/HoloLens2-Machine-Learning](https://github.com/doughtmw/HoloLens2-Machine-Learning) \u003cimg src=\"https://img.shields.io/github/stars/doughtmw/HoloLens2-Machine-Learning?style=social\"/\u003e : Using deep learning models for image classification directly on the HoloLens 2. \n\n    - [NsfwSpy](https://github.com/d00ML0rDz/NsfwSpy) \u003cimg src=\"https://img.shields.io/github/stars/d00ML0rDz/NsfwSpy?style=social\"/\u003e : A .NET image and video classifier used to identify explicit/pornographic content written in C#.  \n\n    - [ClassifyBot](https://github.com/allisterb/ClassifyBot) \u003cimg src=\"https://img.shields.io/github/stars/allisterb/ClassifyBot?style=social\"/\u003e : ClassifyBot is an open-source cross-platform .NET library that tries to automate and make reproducible the steps needed to create machine learning pipelines for object classification using different open-source ML and NLP components. \n\n\n  - ### Object Detection\n\n    - [Microsoft-Rocket-Video-Analytics-Platform](https://github.com/microsoft/Microsoft-Rocket-Video-Analytics-Platform) \u003cimg src=\"https://img.shields.io/github/stars/microsoft/Microsoft-Rocket-Video-Analytics-Platform?style=social\"/\u003e : A highly extensible software stack to empower everyone to build practical real-world live video analytics applications for object detection and counting with cutting edge machine learning algorithms. \n\n    - [Alturos.Yolo](https://github.com/AlturosDestinations/Alturos.Yolo) \u003cimg src=\"https://img.shields.io/github/stars/AlturosDestinations/Alturos.Yolo?style=social\"/\u003e : C# Yolo Darknet Wrapper (real-time object detection).\n\n    - [mentalstack/yolov5-net](https://github.com/mentalstack/yolov5-net) \u003cimg src=\"https://img.shields.io/github/stars/mentalstack/yolov5-net?style=social\"/\u003e : YOLOv5 object detection with C#, ML.NET, ONNX.\n\n    - [sstainba/YoloNet](https://github.com/sstainba/YoloNet) \u003cimg src=\"https://img.shields.io/github/stars/sstainba/YoloNet?style=social\"/\u003e : A .net 6 implementation to use Yolov5 and Yolov8 models via the ONNX Runtime.\n    \n    - [ivilson/Yolov7net](https://github.com/ivilson/Yolov7net) \u003cimg src=\"https://img.shields.io/github/stars/ivilson/Yolov7net?style=social\"/\u003e : Yolov7 Detector for .Net 6.\n\n    - [sangyuxiaowu/ml_yolov7](https://github.com/sangyuxiaowu/ml_yolov7) \u003cimg src=\"https://img.shields.io/github/stars/sangyuxiaowu/ml_yolov7?style=social\"/\u003e : ML.NET Yolov7. \"微信公众号「桑榆肖物」《[YOLOv7 在 ML.NET 中使用 ONNX 检测对象](https://mp.weixin.qq.com/s/vXz6gavYJR2mh5KuJO_slA)》\"\n\n    - [keijiro/TinyYOLOv2Barracuda](https://github.com/keijiro/TinyYOLOv2Barracuda) \u003cimg src=\"https://img.shields.io/github/stars/keijiro/TinyYOLOv2Barracuda?style=social\"/\u003e : Tiny YOLOv2 on Unity Barracuda.\n\n    - [derenlei/Unity_Detection2AR](https://github.com/derenlei/Unity_Detection2AR) \u003cimg src=\"https://img.shields.io/github/stars/derenlei/Unity_Detection2AR?style=social\"/\u003e : Localize 2D image object detection in 3D Scene with Yolo in Unity Barracuda and ARFoundation.\n\n    - [died/YOLO3-With-OpenCvSharp4](https://github.com/died/YOLO3-With-OpenCvSharp4) \u003cimg src=\"https://img.shields.io/github/stars/died/YOLO3-With-OpenCvSharp4?style=social\"/\u003e : Demo of implement YOLO v3 with OpenCvSharp v4 on C#.\n\n    - [mbaske/yolo-unity](https://github.com/mbaske/yolo-unity) \u003cimg src=\"https://img.shields.io/github/stars/mbaske/yolo-unity?style=social\"/\u003e : YOLO In-Game Object Detection for Unity (Windows).\n\n    - [BobLd/YOLOv4MLNet](https://github.com/BobLd/YOLOv4MLNet) \u003cimg src=\"https://img.shields.io/github/stars/BobLd/YOLOv4MLNet?style=social\"/\u003e : Use the YOLO v4 and v5 (ONNX) models for object detection in C# using ML.Net.\n\n    - [keijiro/YoloV4TinyBarracuda](https://github.com/keijiro/YoloV4TinyBarracuda) \u003cimg src=\"https://img.shields.io/github/stars/keijiro/YoloV4TinyBarracuda?style=social\"/\u003e : YoloV4TinyBarracuda is an implementation of the YOLOv4-tiny object detection model on the Unity Barracuda neural network inference library.\n\n    - [zhang8043/YoloWrapper](https://github.com/zhang8043/YoloWrapper) \u003cimg src=\"https://img.shields.io/github/stars/zhang8043/YoloWrapper?style=social\"/\u003e : C#封装YOLOv4算法进行目标检测。\n\n    - [maalik0786/FastYolo](https://github.com/maalik0786/FastYolo) \u003cimg src=\"https://img.shields.io/github/stars/maalik0786/FastYolo?style=social\"/\u003e : Fast Yolo for fast initializing, object detection and tracking.\n\n    - [Uehwan/CSharp-Yolo-Video](https://github.com/Uehwan/CSharp-Yolo-Video) \u003cimg src=\"https://img.shields.io/github/stars/Uehwan/CSharp-Yolo-Video?style=social\"/\u003e : C# Yolo for Video.\n\n    - [HTTP123-A/HumanDetection_Yolov5NET](https://github.com/https://github.com/HTTP123-A/HumanDetection_Yolov5NET) \u003cimg src=\"https://img.shields.io/github/stars/HTTP123-A/HumanDetection_Yolov5NET?style=social\"/\u003e : YOLOv5 object detection with ML.NET, ONNX.\n\n    - [Celine-Hsieh/Hand_Gesture_Training--yolov4](https://github.com/Celine-Hsieh/Hand_Gesture_Training--yolov4) \u003cimg src=\"https://img.shields.io/github/stars/Celine-Hsieh/Hand_Gesture_Training--yolov4?style=social\"/\u003e : Recognize the gestures' features using the YOLOv4 algorithm.\n\n    - [lin-tea/YOLOv5DetectionWithCSharp](https://github.com/lin-tea/YOLOv5DetectionWithCSharp) \u003cimg src=\"https://img.shields.io/github/stars/lin-tea/YOLOv5DetectionWithCSharp?style=social\"/\u003e : YOLOv5s inference In C# and Training In Python.\n\n    - [MirCore/Unity-Object-Detection-and-Localization-with-VR](https://github.com/MirCore/Unity-Object-Detection-and-Localization-with-VR) \u003cimg src=\"https://img.shields.io/github/stars/MirCore/Unity-Object-Detection-and-Localization-with-VR?style=social\"/\u003e : Detect and localize objects from the front-facing camera image of a VR Headset in a 3D Scene in Unity using Yolo and Barracuda.\n\n    - [CarlAreDHopen-eaton/YoloObjectDetection](https://github.com/CarlAreDHopen-eaton/YoloObjectDetection) \u003cimg src=\"https://img.shields.io/github/stars/CarlAreDHopen-eaton/YoloObjectDetection?style=social\"/\u003e : Yolo Object Detection Application for RTSP streams.\n\n    - [TimothyMeadows/Yolo6.NetCore](https://github.com/TimothyMeadows/Yolo6.NetCore) \u003cimg src=\"https://img.shields.io/github/stars/TimothyMeadows/Yolo6.NetCore?style=social\"/\u003e : You Only Look Once (v6) for .NET Core LTS.\n\n    - [mwetzko/EasyYoloDarknet](https://github.com/mwetzko/EasyYoloDarknet) \u003cimg src=\"https://img.shields.io/github/stars/mwetzko/EasyYoloDarknet?style=social\"/\u003e : EasyYoloDarknet.\n\n    - [mwetzko/EasyYoloDarknet](https://github.com/mwetzko/EasyYoloDarknet) \u003cimg src=\"https://img.shields.io/github/stars/mwetzko/EasyYoloDarknet?style=social\"/\u003e : Windows optimized Yolo / Darknet Compile, Train and Detect.\n\n    - [cj-mills/Unity-OpenVINO-YOLOX](https://github.com/cj-mills/Unity-OpenVINO-YOLOX) \u003cimg src=\"https://img.shields.io/github/stars/cj-mills/Unity-OpenVINO-YOLOX?style=social\"/\u003e : This tutorial series covers how to perform object detection in the Unity game engine with the OpenVINO™ Toolkit. \n\n    - [natml-hub/YOLOX](https://github.com/natml-hub/YOLOX) \u003cimg src=\"https://img.shields.io/github/stars/natml-hub/YOLOX?style=social\"/\u003e : High performance object detector based on YOLO series. \n\n    - [thisistherealdiana/YOLO_project](https://github.com/thisistherealdiana/YOLO_project) \u003cimg src=\"https://img.shields.io/github/stars/thisistherealdiana/YOLO_project?style=social\"/\u003e : YOLO project made by Diana Kereselidze. \n\n    - [oujunke/Yolo5Net](https://github.com/oujunke/Yolo5Net) \u003cimg src=\"https://img.shields.io/github/stars/oujunke/Yolo5Net?style=social\"/\u003e : Yolo5实现于TensorFlow.Net. \n\n    - [wojciechp6/YOLO-UnityBarracuda](https://github.com/wojciechp6/YOLO-UnityBarracuda) \u003cimg src=\"https://img.shields.io/github/stars/wojciechp6/YOLO-UnityBarracuda?style=social\"/\u003e : Object detection app build on Unity Barracuda and YOLOv2 Tiny.\n\n    - [RaminAbbaszadi/YoloWrapper-WPF](https://github.com/RaminAbbaszadi/YoloWrapper-WPF) \u003cimg src=\"https://img.shields.io/github/stars/RaminAbbaszadi/YoloWrapper-WPF?style=social\"/\u003e : WPF (C#) Yolo Darknet Wrapper.\n\n    - [fengyhack/YoloWpf](https://github.com/fengyhack/YoloWpf) \u003cimg src=\"https://img.shields.io/github/stars/fengyhack/YoloWpf?style=social\"/\u003e : GUI demo for Object Detection with YOLO and OpenCVSharp.\n\n    - [hanzhuang111/Yolov5Wpf](https://github.com/hanzhuang111/Yolov5Wpf) \u003cimg src=\"https://img.shields.io/github/stars/hanzhuang111/Yolov5Wpf?style=social\"/\u003e : 使用ML.NET部署YOLOV5 的ONNX模型。\n\n    - [quangdungluong/object-detection-form](https://github.com/quangdungluong/object-detection-form) \u003cimg src=\"https://img.shields.io/github/stars/quangdungluong/object-detection-form?style=social\"/\u003e : YOLOv5 using ML.Net, C# and WinForm.\n\n    - [MaikoKingma/yolo-winforms-test](https://github.com/MaikoKingma/yolo-winforms-test) \u003cimg src=\"https://img.shields.io/github/stars/MaikoKingma/yolo-winforms-test?style=social\"/\u003e : A Windows forms application that can execute pre-trained object detection models via ML.NET. In this instance the You Only Look Once version 4 (yolov4) is used.\n\n    - [SeanAnd/WebcamObjectDetection](https://github.com/SeanAnd/WebcamObjectDetection) \u003cimg src=\"https://img.shields.io/github/stars/SeanAnd/WebcamObjectDetection?style=social\"/\u003e : YOLO object detection using webcam in winforms.\n\n    - [Devmawi/BlazorObjectDetection-Sample](https://github.com/Devmawi/BlazorObjectDetection-Sample) \u003cimg src=\"https://img.shields.io/github/stars/Devmawi/BlazorObjectDetection-Sample?style=social\"/\u003e : Simple project for demonstrating how to embed a continuously object detection with Yolo on a video in a hybrid Blazor app (WebView2).\n\n    - [Soju06/yolov5-annotation-viewer](https://github.com/Soju06/yolov5-annotation-viewer) \u003cimg src=\"https://img.shields.io/github/stars/Soju06/yolov5-annotation-viewer?style=social\"/\u003e : yolov5 annotation viewer.\n\n    - [0Kirby/ThrowObjectDetectionWinUI](https://github.com/0Kirby/ThrowObjectDetectionWinUI) \u003cimg src=\"https://img.shields.io/github/stars/0Kirby/ThrowObjectDetectionWinUI?style=social\"/\u003e : 高空抛物检测的WinUI实现。\n    \n    - [davide-cas/HoloHelp](https://github.com/davide-cas/HoloHelp) \u003cimg src=\"https://img.shields.io/github/stars/davide-cas/HoloHelp?style=social\"/\u003e : HoloHelp: HoloLens Object Detection for a Guided Interaction.\n\n    - [vladkol/CustomVision.COCO](https://github.com/vladkol/CustomVision.COCO) \u003cimg src=\"https://img.shields.io/github/stars/vladkol/CustomVision.COCO?style=social\"/\u003e : Traning Azure [Custom Vision](https://www.customvision.ai/) projects using [COCO](https://cocodataset.org/) dataset.\n\n    - [aliardan/RoadMarkingDetection](https://github.com/aliardan/RoadMarkingDetection) \u003cimg src=\"https://img.shields.io/github/starsaliardan/RoadMarkingDetection?style=social\"/\u003e : Road markings detection using yolov5 model based on ONNX.\n\n\n\n\n\n  - ### Face Detection\n\n    - [yangzhongke/ApplyMasksForWorldCup](https://github.com/yangzhongke/ApplyMasksForWorldCup) \u003cimg src=\"https://img.shields.io/github/stars/yangzhongke/ApplyMasksForWorldCup?style=social\"/\u003e : ApplyMasksForWorldCup.\n\n\n\n  - ### Face Recognition \n\n    - [takuya-takeuchi/FaceRecognitionDotNet](https://github.com/takuya-takeuchi/FaceRecognitionDotNet) \u003cimg src=\"https://img.shields.io/github/stars/takuya-takeuchi/FaceRecognitionDotNet?style=social\"/\u003e : The world's simplest facial recognition api for .NET on Windows, MacOS and Linux.\n\n    - [ViewFaceCore/ViewFaceCore](https://github.com/ViewFaceCore/ViewFaceCore) \u003cimg src=\"https://img.shields.io/github/stars/ViewFaceCore/ViewFaceCore?style=social\"/\u003e : C# 超简单的离线人脸识别库。( 基于 SeetaFace6 ) \n\n    - [FaceONNX](https://github.com/FaceONNX/FaceONNX) \u003cimg src=\"https://img.shields.io/github/stars/FaceONNX/FaceONNX?style=social\"/\u003e : Face analytics library based on deep neural networks and ONNX runtime. \n\n    - [mesutpiskin/face-detection-and-recognition](https://github.com/mesutpiskin/face-detection-and-recognition) \u003cimg src=\"https://img.shields.io/github/stars/mesutpiskin/face-detection-and-recognition?style=social\"/\u003e : C# Face detection and recognition with EmguCV. Eigenfaces, Fisherfaces and LBPH algorithms.\n\n    - [georg-jung/FaceAiSharp](https://github.com/georg-jung/FaceAiSharp) \u003cimg src=\"https://img.shields.io/github/stars/georg-jung/FaceAiSharp?style=social\"/\u003e : State-of-the-art face detection and face recoginition. Cross-platform, local, no cloud dependencies, MIT-licensed, onnx-based.\n\n    - [georgj-jung/ExplainFaceRecognition](https://github.com/georg-jung/explain-face-rec) - \u003cimg src=\"https://img.shields.io/github/stars/georg-jung/explain-face-rec?style=social\"/\u003e Beginner friendly interactive face detection \u0026 recognition tutorial with hands-on code samples. State-of-the-art local face AI showcase. Blazor Server \u0026 Hybrid - Web, Windows, Android.\n\n\n  - ### Game Field\n\n    - [Neurogame Fighters](https://github.com/Kacpu/NeurogameFighters) \u003cimg src=\"https://img.shields.io/github/stars/Kacpu/NeurogameFighters?style=social\"/\u003e : Shooter game with elements of machine learning made with WPF.","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/coderonion%2Fawesome-dotnet-machine-learning/projects"}