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Linnea May 9c0ebc74ad Merge remote-tracking branch 'origin/master' into user/linneamay/stream-readme		2022-09-30 15:47:55 -07:00
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WinMLSamplesGallery	Merge remote-tracking branch 'origin/master' into user/linneamay/stream-readme	2022-09-30 15:47:55 -07:00
WinMLSamplesGallery (Package)	Add back arm64	2022-09-27 09:55:06 -07:00
WinMLSamplesGalleryNative	Fix DXResrouceBinding publishing crash	2022-08-31 13:59:02 -07:00
WinMLSamplesGalleryNative.Interop	Update to use dotnet 6	2022-09-26 10:00:56 -07:00
docs	Move Screenshot	2021-11-02 15:42:17 -07:00
.editorconfig	ui fixes and code cleanup	2021-12-03 08:56:47 -08:00
.gitignore	Resolve merge conflicts	2022-08-23 14:19:07 -07:00
README.md	Merge remote-tracking branch 'origin/master' into user/linneamay/stream-readme	2022-09-30 15:47:55 -07:00
WinMLSamplesGallery.sln	launch from samples gallery	2022-05-17 14:33:00 -07:00

README.md

Windows ML Samples Gallery

Demonstrates different machine learning scenarios and features using Windows ML in an interactive format. This app is the interactive companion shows the integration of Windows Machine Learning Library APIs into a desktop WinUI 3 application.

Getting Started
Build From Source
Samples
Feedback
External Links

Getting Started

The WinML Samples Gallery is available in the Microsoft Store. Click here to download.
Check out the source for each sample.

Build From Source

To learn how to implement these features in your application, or unlock additional functionality that may not be available in the Store Application, you may need to build the WinML Samples Gallery from source. Follow these instructions to build from source.

Prerequisites

Build

Clone this repository.
Navigate to Windows-Machine-Learning/Samples/WinMLSamplesGallery.
- (optional) Make any changes to the *.csproj files needed to enable functionality.
Launch WinMLSamplesGallery.sln
Build and deploy the WinMLSamplesGallery (Package) project.

Samples

Image Classifiation: This sample demonstrates image classification using a large number of models taken from the ONNX Model Zoo.
Image Effects: See how to apply preprocessing and postprocessing effects using platform and hardware agnostic ONNX Models and chaining in Windows ML.
Batched Inputs: See how to speed up inference with batched inputs.
OpenCV: See how to integrate Windows ML with OpenCV.
ImageSharp: See how to integrate Windows ML with ImageSharp.
Encrypted Model: how to use Windows ML to load encrypted models from embedded resources.
Adapter Selection: Learn how to use Windows ML with different adapters based on your power and performance needs.
DX Resource Binding in ORT: Learn how to bind and evaluate DirectX Resources using ONNX Runtime.
Stream Effect: Learn how to use Windows ML with real-time inference on video streams.

Feedback

Please file an issue here if you encounter any issues with the WinML Samples Gallery or wish to request a new sample.