AForge.NET VS ML.NET

Compare AForge.NET vs ML.NET and see what are their differences.

AForge.NET

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. (by andrewkirillov)

ML.NET

ML.NET is an open source and cross-platform machine learning framework for .NET. (by dotnet)
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AForge.NET ML.NET
1 17
1,050 8,838
- 0.8%
0.0 8.9
3 months ago 6 days ago
C# C#
GNU General Public License v3.0 or later MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

AForge.NET

Posts with mentions or reviews of AForge.NET. We have used some of these posts to build our list of alternatives and similar projects.

ML.NET

Posts with mentions or reviews of ML.NET. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-02-25.

What are some alternatives?

When comparing AForge.NET and ML.NET you can also consider the following projects:

Accord.NET

TensorFlow.NET - .NET Standard bindings for Google's TensorFlow for developing, training and deploying Machine Learning models in C# and F#.

encog-dotnet-core

Accord.NET Extensions

FaceRecognitionDotNet - The world's simplest facial recognition api for .NET on Windows, MacOS and Linux

OpenCvSharp - OpenCV wrapper for .NET

Deedle - Easy to use .NET library for data and time series manipulation and for scientific programming

Catalyst - 🚀 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.

F# Data - F# Data: Library for Data Access