awesome-dotnet-machine-learning

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awesome-dotnet-machine-learning

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A collection of some awesome public machine learning framework, tutorial, blogs, library and applications for .NET.

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# Awesome-Dotnet-Machine-Learning
[![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)

🔥🔥🔥 This repository lists some awesome public machine learning framework, tutorial, blogs, library and applications for .NET.

## Contents
- [Awesome-Dotnet-Machine-Learning](#awesome-dotnet-machine-learning)
  - [Framework](#framework)
  - [Tutorial](#tutorial)
  - [Library](#library)
    - [FFI Bindings](#ffi-bindings)
    - [GPU Integration](#gpu-integration)
    - [Image and Video Processing](#image-and-video-processing)
    - [Scientific Computation](#scientific-computation)
    - [Data Analysis](#data-analysis)
    - [Data Visualization](#data-visualization)
  - [Applications](#applications)
    - [Robotics AI](#robotics-ai)
    - [Simulation Engine](#simulation-engine)
    - [Chatbot Platform](#chatbot-platform)
    - [Natural Language Processing](#natural-language-processing)
    - [Object Classification](#object-classification)
    - [Object Detection](#object-detection)
    - [Face Detection](#face-detection)
    - [Face Recognition](#face-recognition)
    - [Game Field](#game-field)

- ## Framework

- [SynapseML](https://github.com/microsoft/SynapseML) <img src="https://img.shields.io/github/stars/microsoft/SynapseML?style=social"/> : 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.

- [ML.NET](https://github.com/dotnet/machinelearning) <img src="https://img.shields.io/github/stars/dotnet/machinelearning?style=social"/> : ML.NET is an open source and cross-platform machine learning framework for .NET.

- [TorchSharp](https://github.com/dotnet/TorchSharp) <img src="https://img.shields.io/github/stars/dotnet/TorchSharp?style=social"/> : A .NET library that provides access to the library that powers PyTorch.

- [TensorFlow.NET](https://github.com/SciSharp/TensorFlow.NET) <img src="https://img.shields.io/github/stars/SciSharp/TensorFlow.NET?style=social"/> : .NET Standard bindings for Google's TensorFlow for developing, training and deploying Machine Learning models in C# and F#.

- [DlibDotNet](https://github.com/takuya-takeuchi/DlibDotNet) <img src="https://img.shields.io/github/stars/takuya-takeuchi/DlibDotNet?style=social"/> : Dlib .NET wrapper written in C++ and C# for Windows, MacOS, Linux and iOS.
  
  - [DiffSharp](https://github.com/DiffSharp/DiffSharp) <img src="https://img.shields.io/github/stars/DiffSharp/DiffSharp?style=social"/> : DiffSharp: Differentiable Functional Programming.

- [KelpNet](https://github.com/harujoh/KelpNet) <img src="https://img.shields.io/github/stars/harujoh/KelpNet?style=social"/> : KelpNet : Pure C# machine learning framework.

- [Bright Wire](https://github.com/jdermody/brightwire) <img src="https://img.shields.io/github/stars/jdermody/brightwire?style=social"/> : Bright Wire is an open source machine learning library for .NET with GPU support (via CUDA).

- [SharpNet](https://github.com/FranckZibi/SharpNet) <img src="https://img.shields.io/github/stars/FranckZibi/SharpNet?style=social"/> : Open-source Deep Learning library in C# with CUDA and BLAS support.

- [MyCaffe](https://github.com/MyCaffe/MyCaffe) <img src="https://img.shields.io/github/stars/MyCaffe/MyCaffe?style=social"/> : A complete deep learning platform written almost entirely in C# for Windows developers! Now you can write your own layers in C#!

- [Torch.NET](https://github.com/SciSharp/Torch.NET) <img src="https://img.shields.io/github/stars/SciSharp/Torch.NET?style=social"/> : .NET bindings for PyTorch. Machine Learning with C# / F# with Multi-GPU/CPU support.

- [Keras.NET](https://github.com/SciSharp/Keras.NET) <img src="https://img.shields.io/github/stars/SciSharp/Keras.NET?style=social"/> : 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.

- [MxNet.Sharp](https://github.com/deepakkumar1984/MxNet.Sharp) <img src="https://img.shields.io/github/stars/deepakkumar1984/MxNet.Sharp?style=social"/> : .NET Standard bindings for Apache MxNet with Imperative, Symbolic and Gluon Interface for developing, training and deploying Machine Learning models in C#.

- [ConvNetSharp](https://github.com/cbovar/ConvNetSharp) <img src="https://img.shields.io/github/stars/cbovar/ConvNetSharp?style=social"/> : Started initially as C# port of [ConvNetJS](https://github.com/karpathy/convnetjs). You can use ConvNetSharp to train and evaluate convolutional neural networks (CNN).

- [xin-pu/Machine-Learning](https://github.com/xin-pu/Machine-Learning) <img src="https://img.shields.io/github/stars/xin-pu/Machine-Learning?style=social"/> : Deep learning Tool by C#.

- [LibSvmDotNet](https://github.com/takuya-takeuchi/LibSvmDotNet) <img src="https://img.shields.io/github/stars/takuya-takeuchi/LibSvmDotNet?style=social"/> : .NET wrapper for LIBSVM written in C#.

- [System.AI](https://github.com/ColorfulSoft/System.AI) <img src="https://img.shields.io/github/stars/ColorfulSoft/System.AI?style=social"/> : Machine Learning and Data Analysis stack for .NET ecosystem.

- [DeOldify.NET](https://github.com/ColorfulSoft/DeOldify.NET) <img src="https://img.shields.io/github/stars/ColorfulSoft/DeOldify.NET?style=social"/> : C# implementation of Jason Antic's DeOldify.

- [NeuralNetwork.NET](https://github.com/Sergio0694/NeuralNetwork.NET) <img src="https://img.shields.io/github/stars/Sergio0694/NeuralNetwork.NET?style=social"/> : 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#.

- [AForge.NET](https://github.com/andrewkirillov/AForge.NET) <img src="https://img.shields.io/github/stars/andrewkirillov/AForge.NET?style=social"/> : 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/

- [Accord.NET](https://github.com/accord-net/framework) <img src="https://img.shields.io/github/stars/accord-net/framework?style=social"/> : Machine learning, computer vision, statistics and general scientific computing for .NET. http://accord-framework.net/

- [MLOps.NET](https://github.com/aslotte/MLOps.NET) <img src="https://img.shields.io/github/stars/aslotte/MLOps.NET?style=social"/> : A machine learning model operations and management tool for ML.NET

- ## Tutorial

- [Microsoft AI Lab](https://github.com/microsoft/ailab) <img src="https://img.shields.io/github/stars/microsoft/ailab?style=social"/> : Experience, Learn and Code the latest breakthrough innovations with Microsoft AI.

- [Microsoft SynapseML](https://microsoft.github.io/SynapseML) : Simple and Distributed Machine Learning.

- [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。

- [Microsoft ML.NET Samples](https://github.com/dotnet/machinelearning-samples) <img src="https://img.shields.io/github/stars/dotnet/machinelearning-samples?style=social"/> : Samples for ML.NET, an open source and cross-platform machine learning framework for .NET.

- [TorchSharp Examples](https://github.com/dotnet/TorchSharpExamples) <img src="https://img.shields.io/github/stars/dotnet/TorchSharpExamples?style=social"/> : Repository for TorchSharp examples and tutorials.

- [.NET Interactive Notebooks](https://github.com/dotnet/csharp-notebooks) <img src="https://img.shields.io/github/stars/dotnet/csharp-notebooks?style=social"/> : .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)

- [Hands-On-Machine-Learning-With-ML.NET](https://github.com/PacktPublishing/Hands-On-Machine-Learning-With-ML.NET) <img src="https://img.shields.io/github/stars/PacktPublishing/Hands-On-Machine-Learning-With-ML.NET?style=social"/> : Hands-On Machine Learning with ML.NET, published by Packt.

- [jeffprosise/ML.NET](https://github.com/jeffprosise/ML.NET) <img src="https://img.shields.io/github/stars/jeffprosise/ML.NET?style=social"/> : Code samples utilizing Microsoft's ML.NET machine-learning library.

- ## Library

- ### FFI Bindings

- [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).

- [PInvoke](https://github.com/dotnet/pinvoke) <img src="https://img.shields.io/github/stars/dotnet/pinvoke?style=social"/> : 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.

- [CppSharp](https://github.com/mono/CppSharp) <img src="https://img.shields.io/github/stars/mono/CppSharp?style=social"/> : CppSharp is a tool and set of libraries which facilitates the usage of native C/C++ code with the .NET ecosystem.

- [ClangSharp](https://github.com/dotnet/ClangSharp) <img src="https://img.shields.io/github/stars/dotnet/ClangSharp?style=social"/> : Clang bindings for .NET written in C#.

- [Python.NET](https://github.com/pythonnet/pythonnet) <img src="https://img.shields.io/github/stars/pythonnet/pythonnet?style=social"/> : 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.

- [C2CS](https://github.com/bottlenoselabs/c2cs) <img src="https://img.shields.io/github/stars/bottlenoselabs/c2cs?style=social"/> : Generate C# bindings from a C header.

- [Diplomat](https://github.com/rust-diplomat/diplomat) <img src="https://img.shields.io/github/stars/rust-diplomat/diplomat?style=social"/> : Experimental Rust tool for generating FFI definitions allowing many other languages to call Rust code.

- ### GPU Integration

- [Silk.NET](https://github.com/dotnet/Silk.NET) <img src="https://img.shields.io/github/stars/dotnet/Silk.NET?style=social"/> : The high-speed OpenGL, OpenCL, OpenAL, OpenXR, GLFW, SDL, Vulkan, Assimp, and DirectX bindings library your mother warned you about.

- [Vortice.Vulkan](https://github.com/amerkoleci/Vortice.Vulkan) <img src="https://img.shields.io/github/stars/amerkoleci/Vortice.Vulkan?style=social"/> : Cross platform .NET bindings for Vulkan, VMA, SPIRV-Cross and shaderc.

- [OpenTK](https://github.com/opentk/opentk) <img src="https://img.shields.io/github/stars/opentk/opentk?style=social"/> : The Open Toolkit library is a fast, low-level C# wrapper for OpenGL, OpenAL & 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.

- [ComputeSharp](https://github.com/Sergio0694/ComputeSharp) <img src="https://img.shields.io/github/stars/Sergio0694/ComputeSharp?style=social"/> : 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! 🚀

- [ILGPU](https://github.com/m4rs-mt/ILGPU) <img src="https://img.shields.io/github/stars/m4rs-mt/ILGPU?style=social"/> : ILGPU is a JIT (just-in-time) compiler for high-performance GPU programs written in .Net-based languages.

- [Barracuda](https://github.com/Unity-Technologies/barracuda-release) <img src="https://img.shields.io/github/stars/Unity-Technologies/barracuda-release?style=social"/> : Unity Barracuda is a lightweight cross-platform Neural Networks inference library for Unity. Barracuda can run Neural Networks both on GPU and CPU.

- [ManagedCUDA](https://github.com/kunzmi/managedCuda) <img src="https://img.shields.io/github/stars/kunzmi/managedCuda?style=social"/> : ManagedCUDA aims an easy integration of NVidia's CUDA in .net applications written in C#, Visual Basic or any other .net language.

- [Amplifier.NET](https://github.com/deepakkumar1984/Amplifier.NET) <img src="https://img.shields.io/github/stars/deepakkumar1984/Amplifier.NET?style=social"/> : 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.

- [vk](https://github.com/mellinoe/vk) <img src="https://img.shields.io/github/stars/mellinoe/vk?style=social"/> : Low-level Vulkan bindings for .NET.

- [VulkanCore](https://github.com/discosultan/VulkanCore) <img src="https://img.shields.io/github/stars/discosultan/VulkanCore?style=social"/> : Vulkan 1.0 graphics and compute API bindings for .NET Standard.

- ### Image and Video Processing

- [ImageSharp](https://github.com/SixLabors/ImageSharp) <img src="https://img.shields.io/github/stars/SixLabors/ImageSharp?style=social"/> : ImageSharp is a new, fully featured, fully managed, cross-platform, 2D graphics API.

- [OpenCvSharp](https://github.com/shimat/opencvsharp) <img src="https://img.shields.io/github/stars/shimat/opencvsharp?style=social"/> : OpenCV wrapper for .NET.

- [SharpCV](https://github.com/SciSharp/SharpCV) <img src="https://img.shields.io/github/stars/SciSharp/SharpCV?style=social"/> : A Computer Vision library for C# and F# that combines OpenCV and NDArray together in .NET Standard.

- [FFmpeg.AutoGen](https://github.com/Ruslan-B/FFmpeg.AutoGen) <img src="https://img.shields.io/github/stars/Ruslan-B/FFmpeg.AutoGen?style=social"/> : FFmpeg auto generated unsafe bindings for C#/.NET and Core (Linux, MacOS and Mono).

- [Sdcb.FFmpeg](https://github.com/sdcb/Sdcb.FFmpeg) <img src="https://img.shields.io/github/stars/sdcb/Sdcb.FFmpeg?style=social"/> : FFmpeg basic .NET API generated by CppSharp.

- ### Scientific Computation

- [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.

- [Math.NET Numerics](https://github.com/mathnet/mathnet-numerics) <img src="https://img.shields.io/github/stars/mathnet/mathnet-numerics?style=social"/> : 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.

- [Math.NET Spatial](https://github.com/mathnet/mathnet-spatial) <img src="https://img.shields.io/github/stars/mathnet/mathnet-spatial?style=social"/> : Math.NET Spatial is an opensource geometry library for .Net, Silverlight and Mono.

- [Math.NET Filtering](https://github.com/mathnet/mathnet-filtering) <img src="https://img.shields.io/github/stars/mathnet/mathnet-filtering?style=social"/> : 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.

- [Numpy.NET](https://github.com/SciSharp/Numpy.NET) <img src="https://img.shields.io/github/stars/SciSharp/Numpy.NET?style=social"/> : C#/F# bindings for NumPy - a fundamental library for scientific computing, machine learning and AI.

- [NumSharp](https://github.com/SciSharp/NumSharp) <img src="https://img.shields.io/github/stars/SciSharp/NumSharp?style=social"/> : High Performance Computation for N-D Tensors in .NET, similar API to NumPy.

- [AngouriMath](https://github.com/asc-community/AngouriMath) <img src="https://img.shields.io/github/stars/asc-community/AngouriMath?style=social"/> : New open-source cross-platform symbolic algebra library for C# and F#. Can be used for both production and research purposes.

- ### Data Analysis

- [Deedle](https://github.com/fslaborg/Deedle) <img src="https://img.shields.io/github/stars/fslaborg/Deedle?style=social"/> : Deedle is an easy to use library for data and time series manipulation and for scientific programming.

- [Pandas.NET](https://github.com/SciSharp/Pandas.NET) <img src="https://img.shields.io/github/stars/SciSharp/Pandas.NET?style=social"/> : Pandas port for C# and F#, data analysis tool, process multi-dim array in DataFrame.

- ### Data Visualization

- [LiveCharts2](https://github.com/beto-rodriguez/LiveCharts2) <img src="https://img.shields.io/github/stars/beto-rodriguez/LiveCharts2?style=social"/> : Simple, flexible, interactive & powerful charts, maps and gauges for .Net, LiveCharts2 can now practically run everywhere Maui, Uno Platform, Blazor-wasm, WPF, WinForms, Xamarin, Avalonia, WinUI, UWP.

- [OxyPlot](https://github.com/oxyplot/oxyplot) <img src="https://img.shields.io/github/stars/oxyplot/oxyplot?style=social"/> : OxyPlot is a cross-platform plotting library for .NET.

- [ScottPlot](https://github.com/ScottPlot/ScottPlot) <img src="https://img.shields.io/github/stars/ScottPlot/ScottPlot?style=social"/> : ScottPlot is a free and open-source plotting library for .NET that makes it easy to interactively display large datasets.

- [Plotly.NET](https://github.com/plotly/Plotly.NET) <img src="https://img.shields.io/github/stars/plotly/Plotly.NET?style=social"/> : Plotly.NET provides functions for generating and rendering plotly.js charts in .NET programming languages 📈🚀.

- [swharden/Csharp-Data-Visualization](https://github.com/swharden/Csharp-Data-Visualization) <img src="https://img.shields.io/github/stars/swharden/Csharp-Data-Visualization?style=social"/> : Resources for visualizing data using C# and the .NET platform.

- ## Applications

- ### Robotics AI

- [ROS2 for .NET](https://github.com/ros2-dotnet/ros2_dotnet) <img src="https://img.shields.io/github/stars/ros2-dotnet/ros2_dotnet?style=social"/> : This is a collection of projects (bindings, code generator, examples and more) for writing ROS2 applications for .NET Core and .NET Standard.

- [Ros2 For Unity](https://github.com/RobotecAI/ros2-for-unity) <img src="https://img.shields.io/github/stars/RobotecAI/ros2-for-unity?style=social"/> : ROS2 For Unity is a high-performance communication solution to connect Unity3D and ROS2 ecosystem in a ROS2 "native" way.

- [Ros2cs](https://github.com/RobotecAI/ros2cs) <img src="https://img.shields.io/github/stars/RobotecAI/ros2cs?style=social"/> : A C# .NET library for ROS2, including C# implementation of rcl APIs, message generation, tests and examples.

- [dotnet-state-machine/stateless](https://github.com/dotnet-state-machine/stateless) <img src="https://img.shields.io/github/stars/dotnet-state-machine/stateless?style=social"/> : A simple library for creating state machines in C# code.

- ### Simulation Engine

- [Unity ML-Agents Toolkit](https://github.com/Unity-Technologies/ml-agents) <img src="https://img.shields.io/github/stars/Unity-Technologies/ml-agents?style=social"/> : 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.

- [AR Foundation Samples](https://github.com/Unity-Technologies/arfoundation-samples) <img src="https://img.shields.io/github/stars/Unity-Technologies/arfoundation-samples?style=social"/> : Example content for Unity projects based on AR Foundation.

- [MRTK-Unity](https://github.com/microsoft/MixedRealityToolkit-Unity) <img src="https://img.shields.io/github/stars/microsoft/MixedRealityToolkit-Unity?style=social"/> : Mixed Reality Toolkit (MRTK) provides a set of components and features to accelerate cross-platform MR app development in Unity.

- [SVL Simulator](https://github.com/lgsvl/simulator) <img src="https://img.shields.io/github/stars/lgsvl/simulator?style=social"/> : SVL Simulator: An Autonomous Vehicle Simulator. A ROS/ROS2 Multi-robot Simulator for Autonomous Vehicles. LG Electronics America R&D Lab has developed an HDRP Unity-based multi-robot simulator for autonomous vehicle developers.

- [NatML](https://github.com/natmlx/NatML) <img src="https://img.shields.io/github/stars/natmlx/NatML?style=social"/> : High performance, cross-platform machine learning for Unity Engine. Register at https://hub.natml.ai

- ### Chatbot Platform

- [BotSharp](https://github.com/SciSharp/BotSharp) <img src="https://img.shields.io/github/stars/SciSharp/BotSharp?style=social"/> : The Open Source AI Chatbot Platform Builder in 100% C# Running in .NET Core with Machine Learning algorithm.

- ### Natural Language Processing

- [chatGPTLineBot](https://github.com/isdaviddong/chatGPTLineBot) <img src="https://img.shields.io/github/stars/isdaviddong/chatGPTLineBot?style=social"/> : ChatGPT LINE Bot.

- [Kengxxiao/Himari.ChatGPT](https://github.com/Kengxxiao/Himari.ChatGPT) <img src="https://img.shields.io/github/stars/Kengxxiao/Himari.ChatGPT?style=social"/> : 使用ChatGPT的QQ机器人的简单实现。

- [wieslawsoltes/ChatGPT](https://github.com/wieslawsoltes/ChatGPT) <img src="https://img.shields.io/github/stars/wieslawsoltes/ChatGPT?style=social"/> : A ChatGPT C# client for console and graphical user interface.

- [IronWarrior/ChatGPT-2DPhysics](https://github.com/IronWarrior/ChatGPT-2DPhysics) <img src="https://img.shields.io/github/stars/IronWarrior/ChatGPT-2DPhysics?style=social"/> : This repository contains a simple 2D Physics engine in C#, with code written by ChatGPT. It uses the console for graphics.

- [PolarisAI](https://github.com/MeiFagundes/PolarisAI) <img src="https://img.shields.io/github/stars/MeiFagundes/PolarisAI?style=social"/> : Personal Assistant Engine built with ML.NET.

- [Stanford.NLP.NET](https://github.com/sergey-tihon/Stanford.NLP.NET) <img src="https://img.shields.io/github/stars/sergey-tihon/Stanford.NLP.NET?style=social"/> : Stanford NLP for .NET.

- [Catalyst](https://github.com/curiosity-ai/catalyst) <img src="https://img.shields.io/github/stars/curiosity-ai/catalyst?style=social"/> : 🚀 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.

- ### Object Classification

- [doughtmw/HoloLens2-Machine-Learning](https://github.com/doughtmw/HoloLens2-Machine-Learning) <img src="https://img.shields.io/github/stars/doughtmw/HoloLens2-Machine-Learning?style=social"/> : Using deep learning models for image classification directly on the HoloLens 2.

- [NsfwSpy](https://github.com/d00ML0rDz/NsfwSpy) <img src="https://img.shields.io/github/stars/d00ML0rDz/NsfwSpy?style=social"/> : A .NET image and video classifier used to identify explicit/pornographic content written in C#.

- [ClassifyBot](https://github.com/allisterb/ClassifyBot) <img src="https://img.shields.io/github/stars/allisterb/ClassifyBot?style=social"/> : 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.

- ### Object Detection

- [Microsoft-Rocket-Video-Analytics-Platform](https://github.com/microsoft/Microsoft-Rocket-Video-Analytics-Platform) <img src="https://img.shields.io/github/stars/microsoft/Microsoft-Rocket-Video-Analytics-Platform?style=social"/> : 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.

- [Alturos.Yolo](https://github.com/AlturosDestinations/Alturos.Yolo) <img src="https://img.shields.io/github/stars/AlturosDestinations/Alturos.Yolo?style=social"/> : C# Yolo Darknet Wrapper (real-time object detection).

- [mentalstack/yolov5-net](https://github.com/mentalstack/yolov5-net) <img src="https://img.shields.io/github/stars/mentalstack/yolov5-net?style=social"/> : YOLOv5 object detection with C#, ML.NET, ONNX.

- [sstainba/YoloNet](https://github.com/sstainba/YoloNet) <img src="https://img.shields.io/github/stars/sstainba/YoloNet?style=social"/> : A .net 6 implementation to use Yolov5 and Yolov8 models via the ONNX Runtime.
    
    - [ivilson/Yolov7net](https://github.com/ivilson/Yolov7net) <img src="https://img.shields.io/github/stars/ivilson/Yolov7net?style=social"/> : Yolov7 Detector for .Net 6.

- [sangyuxiaowu/ml_yolov7](https://github.com/sangyuxiaowu/ml_yolov7) <img src="https://img.shields.io/github/stars/sangyuxiaowu/ml_yolov7?style=social"/> : 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)》"

- [keijiro/TinyYOLOv2Barracuda](https://github.com/keijiro/TinyYOLOv2Barracuda) <img src="https://img.shields.io/github/stars/keijiro/TinyYOLOv2Barracuda?style=social"/> : Tiny YOLOv2 on Unity Barracuda.

- [derenlei/Unity_Detection2AR](https://github.com/derenlei/Unity_Detection2AR) <img src="https://img.shields.io/github/stars/derenlei/Unity_Detection2AR?style=social"/> : Localize 2D image object detection in 3D Scene with Yolo in Unity Barracuda and ARFoundation.

- [died/YOLO3-With-OpenCvSharp4](https://github.com/died/YOLO3-With-OpenCvSharp4) <img src="https://img.shields.io/github/stars/died/YOLO3-With-OpenCvSharp4?style=social"/> : Demo of implement YOLO v3 with OpenCvSharp v4 on C#.

- [mbaske/yolo-unity](https://github.com/mbaske/yolo-unity) <img src="https://img.shields.io/github/stars/mbaske/yolo-unity?style=social"/> : YOLO In-Game Object Detection for Unity (Windows).

- [BobLd/YOLOv4MLNet](https://github.com/BobLd/YOLOv4MLNet) <img src="https://img.shields.io/github/stars/BobLd/YOLOv4MLNet?style=social"/> : Use the YOLO v4 and v5 (ONNX) models for object detection in C# using ML.Net.

- [keijiro/YoloV4TinyBarracuda](https://github.com/keijiro/YoloV4TinyBarracuda) <img src="https://img.shields.io/github/stars/keijiro/YoloV4TinyBarracuda?style=social"/> : YoloV4TinyBarracuda is an implementation of the YOLOv4-tiny object detection model on the Unity Barracuda neural network inference library.

- [zhang8043/YoloWrapper](https://github.com/zhang8043/YoloWrapper) <img src="https://img.shields.io/github/stars/zhang8043/YoloWrapper?style=social"/> : C#封装YOLOv4算法进行目标检测。

- [maalik0786/FastYolo](https://github.com/maalik0786/FastYolo) <img src="https://img.shields.io/github/stars/maalik0786/FastYolo?style=social"/> : Fast Yolo for fast initializing, object detection and tracking.

- [Uehwan/CSharp-Yolo-Video](https://github.com/Uehwan/CSharp-Yolo-Video) <img src="https://img.shields.io/github/stars/Uehwan/CSharp-Yolo-Video?style=social"/> : C# Yolo for Video.

- [HTTP123-A/HumanDetection_Yolov5NET](https://github.com/https://github.com/HTTP123-A/HumanDetection_Yolov5NET) <img src="https://img.shields.io/github/stars/HTTP123-A/HumanDetection_Yolov5NET?style=social"/> : YOLOv5 object detection with ML.NET, ONNX.

- [Celine-Hsieh/Hand_Gesture_Training--yolov4](https://github.com/Celine-Hsieh/Hand_Gesture_Training--yolov4) <img src="https://img.shields.io/github/stars/Celine-Hsieh/Hand_Gesture_Training--yolov4?style=social"/> : Recognize the gestures' features using the YOLOv4 algorithm.

- [lin-tea/YOLOv5DetectionWithCSharp](https://github.com/lin-tea/YOLOv5DetectionWithCSharp) <img src="https://img.shields.io/github/stars/lin-tea/YOLOv5DetectionWithCSharp?style=social"/> : YOLOv5s inference In C# and Training In Python.

- [MirCore/Unity-Object-Detection-and-Localization-with-VR](https://github.com/MirCore/Unity-Object-Detection-and-Localization-with-VR) <img src="https://img.shields.io/github/stars/MirCore/Unity-Object-Detection-and-Localization-with-VR?style=social"/> : Detect and localize objects from the front-facing camera image of a VR Headset in a 3D Scene in Unity using Yolo and Barracuda.

- [CarlAreDHopen-eaton/YoloObjectDetection](https://github.com/CarlAreDHopen-eaton/YoloObjectDetection) <img src="https://img.shields.io/github/stars/CarlAreDHopen-eaton/YoloObjectDetection?style=social"/> : Yolo Object Detection Application for RTSP streams.

- [TimothyMeadows/Yolo6.NetCore](https://github.com/TimothyMeadows/Yolo6.NetCore) <img src="https://img.shields.io/github/stars/TimothyMeadows/Yolo6.NetCore?style=social"/> : You Only Look Once (v6) for .NET Core LTS.

- [mwetzko/EasyYoloDarknet](https://github.com/mwetzko/EasyYoloDarknet) <img src="https://img.shields.io/github/stars/mwetzko/EasyYoloDarknet?style=social"/> : EasyYoloDarknet.

- [mwetzko/EasyYoloDarknet](https://github.com/mwetzko/EasyYoloDarknet) <img src="https://img.shields.io/github/stars/mwetzko/EasyYoloDarknet?style=social"/> : Windows optimized Yolo / Darknet Compile, Train and Detect.

- [cj-mills/Unity-OpenVINO-YOLOX](https://github.com/cj-mills/Unity-OpenVINO-YOLOX) <img src="https://img.shields.io/github/stars/cj-mills/Unity-OpenVINO-YOLOX?style=social"/> : This tutorial series covers how to perform object detection in the Unity game engine with the OpenVINO™ Toolkit.

- [natml-hub/YOLOX](https://github.com/natml-hub/YOLOX) <img src="https://img.shields.io/github/stars/natml-hub/YOLOX?style=social"/> : High performance object detector based on YOLO series.

- [thisistherealdiana/YOLO_project](https://github.com/thisistherealdiana/YOLO_project) <img src="https://img.shields.io/github/stars/thisistherealdiana/YOLO_project?style=social"/> : YOLO project made by Diana Kereselidze.

- [oujunke/Yolo5Net](https://github.com/oujunke/Yolo5Net) <img src="https://img.shields.io/github/stars/oujunke/Yolo5Net?style=social"/> : Yolo5实现于TensorFlow.Net.

- [wojciechp6/YOLO-UnityBarracuda](https://github.com/wojciechp6/YOLO-UnityBarracuda) <img src="https://img.shields.io/github/stars/wojciechp6/YOLO-UnityBarracuda?style=social"/> : Object detection app build on Unity Barracuda and YOLOv2 Tiny.

- [RaminAbbaszadi/YoloWrapper-WPF](https://github.com/RaminAbbaszadi/YoloWrapper-WPF) <img src="https://img.shields.io/github/stars/RaminAbbaszadi/YoloWrapper-WPF?style=social"/> : WPF (C#) Yolo Darknet Wrapper.

- [fengyhack/YoloWpf](https://github.com/fengyhack/YoloWpf) <img src="https://img.shields.io/github/stars/fengyhack/YoloWpf?style=social"/> : GUI demo for Object Detection with YOLO and OpenCVSharp.

- [hanzhuang111/Yolov5Wpf](https://github.com/hanzhuang111/Yolov5Wpf) <img src="https://img.shields.io/github/stars/hanzhuang111/Yolov5Wpf?style=social"/> : 使用ML.NET部署YOLOV5 的ONNX模型。

- [quangdungluong/object-detection-form](https://github.com/quangdungluong/object-detection-form) <img src="https://img.shields.io/github/stars/quangdungluong/object-detection-form?style=social"/> : YOLOv5 using ML.Net, C# and WinForm.

- [MaikoKingma/yolo-winforms-test](https://github.com/MaikoKingma/yolo-winforms-test) <img src="https://img.shields.io/github/stars/MaikoKingma/yolo-winforms-test?style=social"/> : 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.

- [SeanAnd/WebcamObjectDetection](https://github.com/SeanAnd/WebcamObjectDetection) <img src="https://img.shields.io/github/stars/SeanAnd/WebcamObjectDetection?style=social"/> : YOLO object detection using webcam in winforms.

- [Devmawi/BlazorObjectDetection-Sample](https://github.com/Devmawi/BlazorObjectDetection-Sample) <img src="https://img.shields.io/github/stars/Devmawi/BlazorObjectDetection-Sample?style=social"/> : Simple project for demonstrating how to embed a continuously object detection with Yolo on a video in a hybrid Blazor app (WebView2).

- [Soju06/yolov5-annotation-viewer](https://github.com/Soju06/yolov5-annotation-viewer) <img src="https://img.shields.io/github/stars/Soju06/yolov5-annotation-viewer?style=social"/> : yolov5 annotation viewer.

- [0Kirby/ThrowObjectDetectionWinUI](https://github.com/0Kirby/ThrowObjectDetectionWinUI) <img src="https://img.shields.io/github/stars/0Kirby/ThrowObjectDetectionWinUI?style=social"/> : 高空抛物检测的WinUI实现。
    
    - [davide-cas/HoloHelp](https://github.com/davide-cas/HoloHelp) <img src="https://img.shields.io/github/stars/davide-cas/HoloHelp?style=social"/> : HoloHelp: HoloLens Object Detection for a Guided Interaction.

- [vladkol/CustomVision.COCO](https://github.com/vladkol/CustomVision.COCO) <img src="https://img.shields.io/github/stars/vladkol/CustomVision.COCO?style=social"/> : Traning Azure [Custom Vision](https://www.customvision.ai/) projects using [COCO](https://cocodataset.org/) dataset.

- [aliardan/RoadMarkingDetection](https://github.com/aliardan/RoadMarkingDetection) <img src="https://img.shields.io/github/starsaliardan/RoadMarkingDetection?style=social"/> : Road markings detection using yolov5 model based on ONNX.

- ### Face Detection

- [yangzhongke/ApplyMasksForWorldCup](https://github.com/yangzhongke/ApplyMasksForWorldCup) <img src="https://img.shields.io/github/stars/yangzhongke/ApplyMasksForWorldCup?style=social"/> : ApplyMasksForWorldCup.

- ### Face Recognition

- [takuya-takeuchi/FaceRecognitionDotNet](https://github.com/takuya-takeuchi/FaceRecognitionDotNet) <img src="https://img.shields.io/github/stars/takuya-takeuchi/FaceRecognitionDotNet?style=social"/> : The world's simplest facial recognition api for .NET on Windows, MacOS and Linux.

- [ViewFaceCore/ViewFaceCore](https://github.com/ViewFaceCore/ViewFaceCore) <img src="https://img.shields.io/github/stars/ViewFaceCore/ViewFaceCore?style=social"/> : C# 超简单的离线人脸识别库。( 基于 SeetaFace6 )

- [FaceONNX](https://github.com/FaceONNX/FaceONNX) <img src="https://img.shields.io/github/stars/FaceONNX/FaceONNX?style=social"/> : Face analytics library based on deep neural networks and ONNX runtime.

- [mesutpiskin/face-detection-and-recognition](https://github.com/mesutpiskin/face-detection-and-recognition) <img src="https://img.shields.io/github/stars/mesutpiskin/face-detection-and-recognition?style=social"/> : C# Face detection and recognition with EmguCV. Eigenfaces, Fisherfaces and LBPH algorithms.

- [georg-jung/FaceAiSharp](https://github.com/georg-jung/FaceAiSharp) <img src="https://img.shields.io/github/stars/georg-jung/FaceAiSharp?style=social"/> : State-of-the-art face detection and face recoginition. Cross-platform, local, no cloud dependencies, MIT-licensed, onnx-based.

- [georgj-jung/ExplainFaceRecognition](https://github.com/georg-jung/explain-face-rec) - <img src="https://img.shields.io/github/stars/georg-jung/explain-face-rec?style=social"/> Beginner friendly interactive face detection & recognition tutorial with hands-on code samples. State-of-the-art local face AI showcase. Blazor Server & Hybrid - Web, Windows, Android.

- ### Game Field

- [Neurogame Fighters](https://github.com/Kacpu/NeurogameFighters) <img src="https://img.shields.io/github/stars/Kacpu/NeurogameFighters?style=social"/> : Shooter game with elements of machine learning made with WPF.

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