Microsoft.ML.OnnxTransformer 2.0.1

The ID prefix of this package has been reserved for one of the owners of this package by Prefix Reserved
.NET Standard 2.0
There is a newer prerelease version of this package available.
See the version list below for details.
dotnet add package Microsoft.ML.OnnxTransformer --version 2.0.1
NuGet\Install-Package Microsoft.ML.OnnxTransformer -Version 2.0.1
This command is intended to be used within the Package Manager Console in Visual Studio, as it uses the NuGet module's version of Install-Package.
<PackageReference Include="Microsoft.ML.OnnxTransformer" Version="2.0.1" />
For projects that support PackageReference, copy this XML node into the project file to reference the package.
paket add Microsoft.ML.OnnxTransformer --version 2.0.1
#r "nuget: Microsoft.ML.OnnxTransformer, 2.0.1"
#r directive can be used in F# Interactive and Polyglot Notebooks. Copy this into the interactive tool or source code of the script to reference the package.
// Install Microsoft.ML.OnnxTransformer as a Cake Addin
#addin nuget:?package=Microsoft.ML.OnnxTransformer&version=2.0.1

// Install Microsoft.ML.OnnxTransformer as a Cake Tool
#tool nuget:?package=Microsoft.ML.OnnxTransformer&version=2.0.1

ML.NET component for Microsoft.ML.OnnxRuntime.Managed library

Product Compatible and additional computed target framework versions.
.NET net5.0 was computed.  net5.0-windows was computed.  net6.0 was computed.  net6.0-android was computed.  net6.0-ios was computed.  net6.0-maccatalyst was computed.  net6.0-macos was computed.  net6.0-tvos was computed.  net6.0-windows was computed.  net7.0 was computed.  net7.0-android was computed.  net7.0-ios was computed.  net7.0-maccatalyst was computed.  net7.0-macos was computed.  net7.0-tvos was computed.  net7.0-windows was computed. 
.NET Core netcoreapp2.0 was computed.  netcoreapp2.1 was computed.  netcoreapp2.2 was computed.  netcoreapp3.0 was computed.  netcoreapp3.1 was computed. 
.NET Standard netstandard2.0 is compatible.  netstandard2.1 was computed. 
.NET Framework net461 was computed.  net462 was computed.  net463 was computed.  net47 was computed.  net471 was computed.  net472 was computed.  net48 was computed.  net481 was computed. 
MonoAndroid monoandroid was computed. 
MonoMac monomac was computed. 
MonoTouch monotouch was computed. 
Tizen tizen40 was computed.  tizen60 was computed. 
Xamarin.iOS xamarinios was computed. 
Xamarin.Mac xamarinmac was computed. 
Xamarin.TVOS xamarintvos was computed. 
Xamarin.WatchOS xamarinwatchos was computed. 
Compatible target framework(s)
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Learn more about Target Frameworks and .NET Standard.

NuGet packages (13)

Showing the top 5 NuGet packages that depend on Microsoft.ML.OnnxTransformer:

Package Downloads
Microsoft.ML.AutoML The ID prefix of this package has been reserved for one of the owners of this package by

ML.NET AutoML: Optimizes an ML pipeline for your dataset, by automatically locating the best feature engineering, model, and hyperparameters

Microsoft.ML.DnnImageFeaturizer.AlexNet The ID prefix of this package has been reserved for one of the owners of this package by

ML.NET component for pretrained AlexNet image featurization

Microsoft.ML.DnnImageFeaturizer.ResNet101 The ID prefix of this package has been reserved for one of the owners of this package by

ML.NET component for pretrained ResNet101 image featurization

Microsoft.ML.DnnImageFeaturizer.ResNet50 The ID prefix of this package has been reserved for one of the owners of this package by

ML.NET component for pretrained ResNet50 image featurization

Microsoft.ML.DnnImageFeaturizer.ResNet18 The ID prefix of this package has been reserved for one of the owners of this package by

ML.NET component for pretrained ResNet18 image featurization

GitHub repositories (4)

Showing the top 4 popular GitHub repositories that depend on Microsoft.ML.OnnxTransformer:

Repository Stars
ML.NET is an open source and cross-platform machine learning framework for .NET.
Platform for Situated Intelligence
CryptoNets is a demonstration of the use of Neural-Networks over data encrypted with Homomorphic Encryption. Homomorphic Encryptions allow performing operations such as addition and multiplication over data while it is encrypted. Therefore, it allows keeping data private while outsourcing computation (see here and here for more about Homomorphic Encryptions and its applications). This project demonstrates the use of Homomorphic Encryption for outsourcing neural-network predictions. The scenario in mind is a provider that would like to provide Prediction as a Service (PaaS) but the data for which predictions are needed may be private. This may be the case in fields such as health or finance. By using CryptoNets, the user of the service can encrypt their data using Homomorphic Encryption and send only the encrypted message to the service provider. Since Homomorphic Encryptions allow the provider to operate on the data while it is encrypted, the provider can make predictions using a pre-trained Neural-Network while the data remains encrypted throughout the process and finaly send the prediction to the user who can decrypt the results. During the process the service provider does not learn anything about the data that was used, the prediction that was made or any intermediate result since everything is encrypted throughout the process. This project uses the Simple Encrypted Arithmetic Library SEAL version 3.2.1 implementation of Homomorphic Encryption developed in Microsoft Research.
Azure Stream Analytics
Version Downloads Last updated
3.0.0-preview.23266.6 3,636 5/17/2023
3.0.0-preview.22621.2 1,993 12/22/2022
2.0.1 99,852 2/1/2023
2.0.1-preview.22573.9 1,193 11/24/2022
2.0.0 29,921 11/8/2022
2.0.0-preview.22551.1 165 11/1/2022
2.0.0-preview.22313.1 4,422 6/14/2022
1.7.1 98,395 3/9/2022
1.7.0 25,631 11/9/2021 253 10/22/2021
1.6.0 24,614 7/15/2021
1.5.5 17,431 3/4/2021
1.5.4 8,380 12/17/2020
1.5.2 19,500 9/11/2020
1.5.1 13,977 7/11/2020
1.5.0 25,881 5/26/2020
1.5.0-preview2 9,874 3/12/2020
1.5.0-preview 9,056 12/26/2019
1.4.0 142,984 11/5/2019
1.4.0-preview2 3,858 10/8/2019
1.4.0-preview 3,729 8/30/2019
1.3.1 11,361 8/6/2019
1.2.0 22,345 7/3/2019
0.13.0 5,384 6/4/2019
0.12.0 3,121 5/2/2019
0.12.0-preview 1,588 4/2/2019
0.11.0 4,209 3/6/2019