Search Results for "net-simulator"
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Showing 34 open source projects for "net-simulator"
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CARLA Simulator
Open-source simulator for autonomous driving research.
CARLA has been developed from the ground up to support development, training, and validation of autonomous driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely. The simulation platform supports flexible specification of sensor suites, environmental conditions, full control of all static and dynamic actors, maps generation and much more. Multiple clients... -
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.NET for Apache Spark
A free, open-source, and cross-platform big data analytics framework
.NET for Apache Spark provides high-performance APIs for using Apache Spark from C# and F#. With these .NET APIs, you can access the most popular Dataframe and SparkSQL aspects of Apache Spark, for working with structured data, and Spark Structured Streaming, for working with streaming data. .NET for Apache Spark is compliant with .NET Standard - a formal specification of .NET APIs that are common across .NET implementations. -
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emgucv
Cross platform .Net wrapper to the OpenCV image processing library
Emgu CV is a cross platform .Net wrapper to the OpenCV image processing library. Allowing OpenCV functions to be called from .NET compatible languages. The wrapper can be compiled by Visual Studio and Unity, it can run on Windows, Linux, Mac OS, iOS and Android. -
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ML.NET
Open source and cross-platform machine learning framework for .NET
With ML.NET, you can create custom ML models using C# or F# without having to leave the .NET ecosystem. ML.NET lets you re-use all the knowledge, skills, code, and libraries you already have as a .NET developer so that you can easily integrate machine learning into your web, mobile, desktop, games, and IoT apps. ML.NET offers Model Builder (a simple UI tool) and ML.NET CLI to make it super easy to build custom ML Models. -
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NVIDIA FLARE
NVIDIA Federated Learning Application Runtime Environment
NVIDIA Federated Learning Application Runtime Environment NVIDIA FLARE is a domain-agnostic, open-source, extensible SDK that allows researchers and data scientists to adapt existing ML/DL workflows(PyTorch, TensorFlow, Scikit-learn, XGBoost etc.) to a federated paradigm. It enables platform developers to build a secure, privacy-preserving offering for a distributed multi-party collaboration. NVIDIA FLARE is built on a componentized architecture that allows you to take federated... -
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Linfa
A Rust machine learning framework
linfa aims to provide a comprehensive toolkit to build Machine Learning applications with Rust. Kin in spirit to Python's scikit-learn, it focuses on common preprocessing tasks and classical ML algorithms for your everyday ML tasks. -
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DeepCTR-Torch
Easy-to-use,Modular and Extendible package of deep-learning models
...Factorization-Machine and it’s variants are widely used to learn the low-order feature interaction. High-order Extractor learns feature combination through complex neural network functions like MLP, Cross Net, etc. -
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OpenAI-API-dotnet
An unofficial C#/.NET SDK for accessing the OpenAI GPT-3 API
A simple C# .NET wrapper library to use with OpenAI's API. More context on my blog. This is my original unofficial wrapper library around the OpenAI API. -
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Pytorch-toolbelt
PyTorch extensions for fast R&D prototyping and Kaggle farming
A pytorch-toolbelt is a Python library with a set of bells and whistles for PyTorch for fast R&D prototyping and Kaggle farming. Easy model building using flexible encoder-decoder architecture. Modules: CoordConv, SCSE, Hypercolumn, Depthwise separable convolution and more. GPU-friendly test-time augmentation TTA for segmentation and classification. GPU-friendly inference on huge (5000x5000) images. Every-day common routines (fix/restore random seed, filesystem utils, metrics). Losses:... -
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ML+
Machine Learning
Fast Machine Learning -
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Adaptive Intelligence
Adaptive Intelligence also known as "Artificial General Intelligence"
Adaptive Intelligence is the implementation of neural science, forensic psychology , behavioral science with machine-learning and artificial intelligence to provide advanced automated software platforms with the ability to adjust and thrive in dynamic environments by combining cognitive flexibility, emotional regulation, resilience, and practical problem-solving skills. -
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UnBBayes
Framework & GUI for Bayes Nets and other probabilistic models.
UnBBayes is a probabilistic network framework written in Java. It has both a GUI and an API with inference, sampling, learning and evaluation. It supports Bayesian networks, influence diagrams, MSBN, OOBN, HBN, MEBN/PR-OWL, PRM, structure, parameter and incremental learning. Please, visit our wiki (https://sourceforge.net/p/unbbayes/wiki/Home/) for more information. Check out the license section (https://sourceforge.net/p/unbbayes/wiki/License/) for our licensing policy. -
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MMDeploy
OpenMMLab Model Deployment Framework
...Models can be exported and run in several backends, and more will be compatible. All kinds of modules in the SDK can be extended, such as Transform for image processing, Net for Neural Network inference, Module for postprocessing and so on. Install and build your target backend. ONNX Runtime is a cross-platform inference and training accelerator compatible with many popular ML/DNN frameworks. Please read getting_started for the basic usage of MMDeploy. -
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TensorFlow.NET
.NET Standard bindings for Google's TensorFlow for developing models
TensorFlow.NET (TF.NET) provides a .NET Standard binding for TensorFlow. It aims to implement the complete Tensorflow API in C# which allows .NET developers to develop, train and deploy Machine Learning models with the cross-platform .NET Standard framework. TensorFlow.NET has built-in Keras high-level interface and is released as an independent package TensorFlow.Keras. -
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FlubuCore
A cross platform build and deployment automation system
...You can define your build and deployment scripts in C# using an intuitive fluent interface. This gives you code completion, IntelliSense, debugging, FlubuCore custom analyzers, and native access to the whole .NET ecosystem inside of your scripts. FlubuCore offers a .net (core) console application that uses power of roslyn to compile and execute scripts. Intuitive and easy to learn. C#, fluent interface, and IntelliSense make even the most complex script creation a breeze. Large number of often used built-in tasks like e.g. versioning, running tests, creating deployment packages, publishing NuGet packages, docker tasks, git tasts, sql tasks, npm tasks, executing PowerShell, managing IIS scripts and many more. -
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Bullet Physics SDK
Real-time collision detection and multi-physics simulation for VR
This is the official C++ source code repository of the Bullet Physics SDK: real-time collision detection and multi-physics simulation for VR, games, visual effects, robotics, machine learning etc. We are developing a new differentiable simulator for robotics learning, called Tiny Differentiable Simulator, or TDS. The simulator allows for hybrid simulation with neural networks. It allows different automatic differentiation backends, for forward and reverse mode gradients. TDS can be trained using Deep Reinforcement Learning, or using Gradient based optimization (for example LFBGS). ... -
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AirSim
A simulator for drones, cars and more, built on Unreal Engine
AirSim is an open-source, cross platform simulator for drones, cars and more vehicles, built on Unreal Engine with an experimental Unity release in the works. It supports software-in-the-loop simulation with popular flight controllers such as PX4 & ArduPilot and hardware-in-loop with PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped into any Unreal environment. -
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ML.NET Samples
Samples for ML.NET, an open source and cross-platform machine learning
ML.NET is a cross-platform open-source machine learning framework that makes machine learning accessible to .NET developers. In this GitHub repo, we provide samples that will help you get started with ML.NET and how to infuse ML into existing and new .NET apps. We're working on simplifying ML.NET usage with additional technologies that automate the creation of the model for you so you don't need to write the code by yourself to train a model, you simply need to provide your datasets. ... -
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NiftyNet
An open-source convolutional neural networks platform for research
An open-source convolutional neural networks platform for medical image analysis and image-guided therapy. NiftyNet is a TensorFlow-based open-source convolutional neural networks (CNNs) platform for research in medical image analysis and image-guided therapy. NiftyNet’s modular structure is designed for sharing networks and pre-trained models. Using this modular structure you can get started with established pre-trained networks using built-in tools. Adapt existing networks to your imaging... -
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Tensorpack
A Neural Net Training Interface on TensorFlow, with focus on speed
Tensorpack is a neural network training interface based on TensorFlow v1. Uses TensorFlow in the efficient way with no extra overhead. On common CNNs, it runs training 1.2~5x faster than the equivalent Keras code. Your training can probably gets faster if written with Tensorpack. Scalable data-parallel multi-GPU / distributed training strategy is off-the-shelf to use. Squeeze the best data loading performance of Python with tensorpack.dataflow. Symbolic programming (e.g. tf.data) does not... -
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When to use TensorFlowSharp
TensorFlow API for .NET languages
When to use TensorFlowSharp is a .NET binding for the TensorFlow machine learning framework that allows developers to run TensorFlow models directly from C# and other .NET languages. The project exposes TensorFlow’s native C API through a strongly typed interface designed to integrate naturally with the .NET ecosystem. Its primary purpose is to enable developers working in Microsoft-based environments to load trained TensorFlow models and perform inference or additional training within .NET applications. ... -
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mAP
Evaluates the performance of your neural net for object recognition
In practice, a higher mAP value indicates a better performance of your neural net, given your ground truth and set of classes. The performance of your neural net will be judged using the mAP criteria defined in the PASCAL VOC 2012 competition. We simply adapted the official Matlab code into Python (in our tests they both give the same results). First, your neural net detection-results are sorted by decreasing confidence and are assigned to ground-truth objects. ... -
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GPU Machine Learning Library. This library aims to provide machine learning researchers and practitioners with a high performance library by taking advantage of the GPU enormous computational power. The library is developed in C++ and CUDA.
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AWS IoT Arduino Yún SDK
SDK for connecting to AWS IoT from an Arduino Yún
The AWS-IoT-Arduino-Yún-SDK allows developers to connect their Arduino Yún compatible Board to AWS IoT. By connecting the device to the AWS IoT, users can securely work with the message broker, rules and the Thing Shadow provided by AWS IoT and with other AWS services like AWS Lambda, Amazon Kinesis, Amazon S3, etc. The AWS-IoT-Arduino-Yún-SDK consists of two parts, which take use of the resources of the two chips on Arduino Yún, one for native Arduino IDE API access and the other for... -
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Virtual Eyewear
An eyewear trying simulator
Try this software and find out the eyewear style that is the most suitable for you.