NboxTrainer 1.0.0.1

There is a newer version of this package available.
See the version list below for details.
Install-Package NboxTrainer -Version 1.0.0.1
dotnet add package NboxTrainer --version 1.0.0.1
<PackageReference Include="NboxTrainer" Version="1.0.0.1" />
For projects that support PackageReference, copy this XML node into the project file to reference the package.
paket add NboxTrainer --version 1.0.0.1
The NuGet Team does not provide support for this client. Please contact its maintainers for support.
#r "nuget: NboxTrainer, 1.0.0.1"
#r directive can be used in F# Interactive, C# scripting and .NET Interactive. Copy this into the interactive tool or source code of the script to reference the package.
// Install NboxTrainer as a Cake Addin
#addin nuget:?package=NboxTrainer&version=1.0.0.1

// Install NboxTrainer as a Cake Tool
#tool nuget:?package=NboxTrainer&version=1.0.0.1
The NuGet Team does not provide support for this client. Please contact its maintainers for support.

NBox Trainer

Nbox Trainer is library-wrapper for easy creating trainer ml models of image classification with GPU acceleration. Used ML.Net and Tensorflow

Packages
  • Microsoft.ML [1.5.4]
  • Microsoft.ML.ImageAnalytics [1.5.4]
  • Microsoft.ML.Vision [1.5.4]
  • SciSharp.TensorFlow.Redist-Windows-GPU [1.5.1]
  • CUDA 10.0;
  • Cudnn 7.6.4;
Example struct dataset
 +---flower_photos
 |   +---daisy
 |   +---dandelion
 |   +---roses
 |   +---sunflowers
 |   +---tulips
 |--------------------

All top subfolders of dataset catalog will use as label of categories

Example using with fluent builder
 string dirDatasets = "D:\\Downloads\\flower_photos\\";

            TrainerService trainer = new TrainerService(AppContext.BaseDirectory, "flower")
                .setArchitecture(ImageClassificationTrainer.Architecture.MobilenetV2)
                .setEpoch(1000)
                .setBatchSize(5)
                .setLearningRate(0.01F)
                .setReuseTrainBottleneckCache(true)
                .setReuseValidationBottleneckCache(true)
                .setCriteriaEarlyStopping(new ImageClassificationTrainer.EarlyStopping(0.1F, 500,
                    ImageClassificationTrainer.EarlyStoppingMetric.Accuracy))
                .setPathToDataset(dirDatasets)
                .setSplitPercent(0.3F);

            trainer.onStateChanged += state => { Console.Title = $"Current Stage: {state}"; };
            trainer.onTrainMetrics += Trainer_onTrainMetrics;

            await trainer.Train();
            var _metrics = await trainer.GetMetrics();
            await trainer.SaveModel();

NuGet packages

This package is not used by any NuGet packages.

GitHub repositories

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Version Downloads Last updated
1.0.0.2 190 1/10/2021
1.0.0.1 179 1/10/2021