add tut samples

Sheil Kumar · Sheil Kumar · commit 7abea32159a5 · 2021-10-22T10:54:17.000-07:00
diff --git a/README.md b/README.md
@@ -46,32 +46,32 @@ Windows ML offers machine learning inferencing via the inbox Windows SDK as well
 Learn mode [here](https://docs.microsoft.com/en-us/windows/ai/windows-ml/get-started).
 
 ## Model Samples
-In this section you will find various model samples for a variety of scenarios across the different Windows ML API offerings. 
+In this section you will find various model samples for a variety of scenarios across the different Windows ML API offerings.
 
 **Image Classification**
 
 A subdomain of computer vision in which an algorithm looks at an image and assigns it a tag from a collection of predefined tags or categories that it has been trained on.
 
 | Windows App Type <br/>Distribution | UWP<br/>In-Box |  UWP<br/>NuGet | Desktop<br/>In-Box | Desktop<br/>NuGet |
 |------------|------------------------------------|--------------------------------------|------------------------------------|--------------------------------------|
-| [AlexNet](https://github.com/onnx/models/raw/master/vision/classification/alexnet/model/bvlcalexnet-9.onnx)                                | | ||[✔️C# .NET5 - Samples Gallery](https://github.com/microsoft/Windows-Machine-Learning/tree/user/sheilk/readme-updates/Samples/WinMLSamplesGallery)<br/>|
-| [CaffeNet](https://github.com/onnx/models/raw/master/vision/classification/caffenet/model/caffenet-9.onnx)                                  | | ||[✔️C# .NET5 - Samples Gallery](https://github.com/microsoft/Windows-Machine-Learning/tree/user/sheilk/readme-updates/Samples/WinMLSamplesGallery)<br/>|
-| [DenseNet](https://github.com/onnx/models/raw/master/vision/classification/densenet-121/model/densenet-9.onnx)                              | | ||[✔️C# .NET5 - Samples Gallery](https://github.com/microsoft/Windows-Machine-Learning/tree/user/sheilk/readme-updates/Samples/WinMLSamplesGallery)<br/>|
-| [EfficientNet](https://github.com/onnx/models/raw/master/vision/classification/efficientnet-lite4/model/efficientnet-lite4-11.onnx)             | | ||[✔️C# .NET5 - Samples Gallery](https://github.com/microsoft/Windows-Machine-Learning/tree/user/sheilk/readme-updates/Samples/WinMLSamplesGallery)<br/>|
+| [AlexNet](https://github.com/onnx/models/raw/master/vision/classification/alexnet/model/bvlcalexnet-9.onnx)                                | | ||[✔️C# .NET5 - Samples Gallery](https://github.com/microsoft/Windows-Machine-Learning/tree/master/Samples/WinMLSamplesGallery)<br/>|
+| [CaffeNet](https://github.com/onnx/models/raw/master/vision/classification/caffenet/model/caffenet-9.onnx)                                  | | ||[✔️C# .NET5 - Samples Gallery](https://github.com/microsoft/Windows-Machine-Learning/tree/master/Samples/WinMLSamplesGallery)<br/>|
+| [DenseNet](https://github.com/onnx/models/raw/master/vision/classification/densenet-121/model/densenet-9.onnx)                              | | ||[✔️C# .NET5 - Samples Gallery](https://github.com/microsoft/Windows-Machine-Learning/tree/master/Samples/WinMLSamplesGallery)<br/>|
+| [EfficientNet](https://github.com/onnx/models/raw/master/vision/classification/efficientnet-lite4/model/efficientnet-lite4-11.onnx)             | | ||[✔️C# .NET5 - Samples Gallery](https://github.com/microsoft/Windows-Machine-Learning/tree/master/Samples/WinMLSamplesGallery)<br/>|
 | Emoji8     | [✔️C#](https://github.com/microsoft/Windows-Machine-Learning/tree/master/Samples/Emoji8/UWP/cs) |                                                 |
-| [GoogleNet](https://github.com/onnx/models/raw/master/vision/classification/inception_and_googlenet/googlenet/model/googlenet-9.onnx)        | | ||[✔️C# .NET5 - Samples Gallery](https://github.com/microsoft/Windows-Machine-Learning/tree/user/sheilk/readme-updates/Samples/WinMLSamplesGallery)<br/>|
-| [InceptionV1](https://github.com/onnx/models/raw/master/vision/classification/inception_and_googlenet/inception_v1/model/inception-v1-9.)      | | ||[✔️C# .NET5 - Samples Gallery](https://github.com/microsoft/Windows-Machine-Learning/tree/user/sheilk/readme-updates/Samples/WinMLSamplesGallery)<br/>|
-| [InceptionV2](https://github.com/onnx/models/raw/master/vision/classification/inception_and_googlenet/inception_v2/model/inception-v2-9.)      | | ||[✔️C# .NET5 - Samples Gallery](https://github.com/microsoft/Windows-Machine-Learning/tree/user/sheilk/readme-updates/Samples/WinMLSamplesGallery)<br/>|
+| [GoogleNet](https://github.com/onnx/models/raw/master/vision/classification/inception_and_googlenet/googlenet/model/googlenet-9.onnx)        | | ||[✔️C# .NET5 - Samples Gallery](https://github.com/microsoft/Windows-Machine-Learning/tree/master/Samples/WinMLSamplesGallery)<br/>|
+| [InceptionV1](https://github.com/onnx/models/raw/master/vision/classification/inception_and_googlenet/inception_v1/model/inception-v1-9.)      | | ||[✔️C# .NET5 - Samples Gallery](https://github.com/microsoft/Windows-Machine-Learning/tree/master/Samples/WinMLSamplesGallery)<br/>|
+| [InceptionV2](https://github.com/onnx/models/raw/master/vision/classification/inception_and_googlenet/inception_v2/model/inception-v2-9.)      | | ||[✔️C# .NET5 - Samples Gallery](https://github.com/microsoft/Windows-Machine-Learning/tree/master/Samples/WinMLSamplesGallery)<br/>|
 | [MNIST](https://github.com/onnx/models/tree/master/vision/classification/mnist)      | [✔️C++/CX](https://github.com/Microsoft/Windows-Machine-Learning/tree/master/Samples/MNIST/UWP)<br/>[✔️C#](https://github.com/Microsoft/Windows-Machine-Learning/tree/master/Samples/MNIST/Tutorial/cs)<br/>    |                                                 |
-| [MobileNetV2](https://github.com/onnx/models/raw/master/vision/classification/mobilenet/model/mobilenetv2-7.onnx)                              | | ||[✔️C# .NET5 - Samples Gallery](https://github.com/microsoft/Windows-Machine-Learning/tree/user/sheilk/readme-updates/Samples/WinMLSamplesGallery)<br/>|
-| [RCNN](https://github.com/onnx/models/raw/master/vision/classification/rcnn_ilsvrc13/model/rcnn-ilsvrc13-9.onnx)                        | | ||[✔️C# .NET5 - Samples Gallery](https://github.com/microsoft/Windows-Machine-Learning/tree/user/sheilk/readme-updates/Samples/WinMLSamplesGallery)<br/>|
-| [ResNet50](https://github.com/onnx/models/raw/master/vision/classification/resnet/model/resnet50-caffe2-v1-9.onnx)                          | | ||[✔️C# .NET5 - Samples Gallery](https://github.com/microsoft/Windows-Machine-Learning/tree/user/sheilk/readme-updates/Samples/WinMLSamplesGallery)<br/>|
-| [ShuffleNetV1](https://github.com/onnx/models/raw/master/vision/classification/shufflenet/model/shufflenet-9.onnx)                              | | ||[✔️C# .NET5 - Samples Gallery](https://github.com/microsoft/Windows-Machine-Learning/tree/user/sheilk/readme-updates/Samples/WinMLSamplesGallery)<br/>|
-| [ShuffleNetV2](https://github.com/onnx/models/raw/master/vision/classification/shufflenet/model/shufflenet-v2-10.onnx)                          | | ||[✔️C# .NET5 - Samples Gallery](https://github.com/microsoft/Windows-Machine-Learning/tree/user/sheilk/readme-updates/Samples/WinMLSamplesGallery)<br/>|
-| [SqueezeNet](https://github.com/onnx/models/raw/master/vision/classification/squeezenet/model/squeezenet1.1-7.onnx) | [✔️C#](https://github.com/Microsoft/Windows-Machine-Learning/tree/master/Samples/SqueezeNetObjectDetection/UWP/cs)<br/>[✔️JavaScript](https://github.com/Microsoft/Windows-Machine-Learning/tree/master/Samples/SqueezeNetObjectDetection/UWP/cs)<br/>        |                     |[✔️C++/WinRT](https://github.com/Microsoft/Windows-Machine-Learning/tree/master/Samples/SqueezeNetObjectDetection/Desktop/cpp)<br/> [✔️C# .NET5](https://github.com/Microsoft/Windows-Machine-Learning/tree/master/Samples/SqueezeNetObjectDetection/NET5)<br/>[✔️C# .NET Core 2](https://github.com/microsoft/Windows-Machine-Learning/tree/master/Samples/SqueezeNetObjectDetection/NETCore/cs)<br/>|[✔️C++/WinRT](https://github.com/Microsoft/Windows-Machine-Learning/tree/master/Samples/SqueezeNetObjectDetection/Desktop/cpp)<br/>[✔️C# .NET5 - Samples Gallery](https://github.com/microsoft/Windows-Machine-Learning/tree/user/sheilk/readme-updates/Samples/WinMLSamplesGallery)<br/>[✔️Rust](https://github.com/microsoft/Windows-Machine-Learning/tree/user/sheilk/readme-updates/Samples/RustSqueezenet)<br/>|
-| [VGG19](https://github.com/onnx/models/raw/master/vision/classification/vgg/model/vgg19-7.onnx)                                          | | ||[✔️C# .NET5 - Samples Gallery](https://github.com/microsoft/Windows-Machine-Learning/tree/user/sheilk/readme-updates/Samples/WinMLSamplesGallery)<br/>|
-| [VGG19bn](https://github.com/onnx/models/raw/master/vision/classification/vgg/model/vgg19-bn-7.onnx)                                       | | ||[✔️C# .NET5 - Samples Gallery](https://github.com/microsoft/Windows-Machine-Learning/tree/user/sheilk/readme-updates/Samples/WinMLSamplesGallery)<br/>|
-| [ZFNet512](https://github.com/onnx/models/raw/master/vision/classification/zfnet-512/model/zfnet512-9.onnx)                                 | | ||[✔️C# .NET5 - Samples Gallery](https://github.com/microsoft/Windows-Machine-Learning/tree/user/sheilk/readme-updates/Samples/WinMLSamplesGallery)<br/>|
+| [MobileNetV2](https://github.com/onnx/models/raw/master/vision/classification/mobilenet/model/mobilenetv2-7.onnx)                              | | ||[✔️C# .NET5 - Samples Gallery](https://github.com/microsoft/Windows-Machine-Learning/tree/master/Samples/WinMLSamplesGallery)<br/>|
+| [RCNN](https://github.com/onnx/models/raw/master/vision/classification/rcnn_ilsvrc13/model/rcnn-ilsvrc13-9.onnx)                        | | ||[✔️C# .NET5 - Samples Gallery](https://github.com/microsoft/Windows-Machine-Learning/tree/master/Samples/WinMLSamplesGallery)<br/>|
+| [ResNet50](https://github.com/onnx/models/raw/master/vision/classification/resnet/model/resnet50-caffe2-v1-9.onnx)                          | | ||[✔️C# .NET5 - Samples Gallery](https://github.com/microsoft/Windows-Machine-Learning/tree/master/Samples/WinMLSamplesGallery)<br/>|
+| [ShuffleNetV1](https://github.com/onnx/models/raw/master/vision/classification/shufflenet/model/shufflenet-9.onnx)                              | | ||[✔️C# .NET5 - Samples Gallery](https://github.com/microsoft/Windows-Machine-Learning/tree/master/Samples/WinMLSamplesGallery)<br/>|
+| [ShuffleNetV2](https://github.com/onnx/models/raw/master/vision/classification/shufflenet/model/shufflenet-v2-10.onnx)                          | | ||[✔️C# .NET5 - Samples Gallery](https://github.com/microsoft/Windows-Machine-Learning/tree/master/Samples/WinMLSamplesGallery)<br/>|
+| [SqueezeNet](https://github.com/onnx/models/raw/master/vision/classification/squeezenet/model/squeezenet1.1-7.onnx) | [✔️C#](https://github.com/Microsoft/Windows-Machine-Learning/tree/master/Samples/SqueezeNetObjectDetection/UWP/cs)<br/>[✔️JavaScript](https://github.com/Microsoft/Windows-Machine-Learning/tree/master/Samples/SqueezeNetObjectDetection/UWP/cs)<br/>        |                     |[✔️C++/WinRT](https://github.com/Microsoft/Windows-Machine-Learning/tree/master/Samples/SqueezeNetObjectDetection/Desktop/cpp)<br/> [✔️C# .NET5](https://github.com/Microsoft/Windows-Machine-Learning/tree/master/Samples/SqueezeNetObjectDetection/NET5)<br/>[✔️C# .NET Core 2](https://github.com/microsoft/Windows-Machine-Learning/tree/master/Samples/SqueezeNetObjectDetection/NETCore/cs)<br/>|[✔️C++/WinRT](https://github.com/Microsoft/Windows-Machine-Learning/tree/master/Samples/SqueezeNetObjectDetection/Desktop/cpp)<br/>[✔️C# .NET5 - Samples Gallery](https://github.com/microsoft/Windows-Machine-Learning/tree/master/Samples/WinMLSamplesGallery)<br/>[✔️Rust](https://github.com/microsoft/Windows-Machine-Learning/tree/master/Samples/RustSqueezenet)<br/>|
+| [VGG19](https://github.com/onnx/models/raw/master/vision/classification/vgg/model/vgg19-7.onnx)                                          | | ||[✔️C# .NET5 - Samples Gallery](https://github.com/microsoft/Windows-Machine-Learning/tree/master/Samples/WinMLSamplesGallery)<br/>|
+| [VGG19bn](https://github.com/onnx/models/raw/master/vision/classification/vgg/model/vgg19-bn-7.onnx)                                       | | ||[✔️C# .NET5 - Samples Gallery](https://github.com/microsoft/Windows-Machine-Learning/tree/master/Samples/WinMLSamplesGallery)<br/>|
+| [ZFNet512](https://github.com/onnx/models/raw/master/vision/classification/zfnet-512/model/zfnet512-9.onnx)                                 | | ||[✔️C# .NET5 - Samples Gallery](https://github.com/microsoft/Windows-Machine-Learning/tree/master/Samples/WinMLSamplesGallery)<br/>|
 
 **Style Transfer**
 
@@ -87,7 +87,7 @@ A computer vision technique that allows us to recompose the content of an image
 
 |            | Store App<br/>Inbox API |  Store App<br/>NuGet API | Desktop App<br/>Inbox API |  Desktop App<br/>NuGet API |
 |------------|------------------------------------|--------------------------------------|------------------------------------|--------------------------------------|
-| [YoloV4](https://github.com/onnx/models/raw/master/vision/object_detection_segmentation/yolov4/model/yolov4.onnx)                         | | ||[✔️C# .NET5 - Samples Gallery](https://github.com/microsoft/Windows-Machine-Learning/tree/user/sheilk/readme-updates/Samples/WinMLSamplesGallery)<br/>|
+| [YoloV4](https://github.com/onnx/models/raw/master/vision/object_detection_segmentation/yolov4/model/yolov4.onnx)                         | | ||[✔️C# .NET5 - Samples Gallery](https://github.com/microsoft/Windows-Machine-Learning/tree/master/Samples/WinMLSamplesGallery)<br/>|
 
 -->
 
@@ -99,6 +99,11 @@ These advanced samples show how to use various binding and evaluation features i
 - **[Custom Operator (CPU)](https://github.com/Microsoft/Windows-Machine-Learning/tree/master/Samples/CustomOperator)**: a desktop app that defines multiple custom cpu operators. One of these is a debug operator which we invite you to integrate into your own workflow.
 - **[Adapter Selection](https://github.com/Microsoft/Windows-Machine-Learning/tree/master/Samples/AdapterSelection)**: a desktop app that demonstrates how to choose a specific device adapter for running your model
 - **[Plane Identifier](https://github.com/Microsoft/Windows-AppConsult-Samples-UWP/tree/master/PlaneIdentifier)**: a UWP app and a WPF app packaged with the Desktop Bridge, sharing the same model trained using [Azure Custom Vision service](https://customvision.ai/). For step-by-step instructions for this sample, please see the blog post [Upgrade your WinML application to the latest bits](https://blogs.msdn.microsoft.com/appconsult/2018/11/06/upgrade-your-winml-application-to-the-latest-bits/).
+- **[Custom Vision and Windows ML](https://github.com/Microsoft/Windows-AppConsult-Samples-UWP/tree/master/PlaneIdentifier)**: The tutorial shows how to train a neural network model to classify images of food using Azure Custom Vision service, export the model to ONNX format, and deploy the model in a Windows Machine Learning application running locally on Windows device. 
+- **[ML.NET and Windows ML](https://github.com/Microsoft/Windows-AppConsult-Samples-UWP/tree/master/PlaneIdentifier)**: This tutorial shows you how to train a neural network model to classify images of food using ML.NET Model Builder, export the model to ONNX format, and deploy the model in a Windows Machine Learning application running locally on a Windows device. 
+- **[PyTorch Data Analysis](https://github.com/Microsoft/Windows-AppConsult-Samples-UWP/tree/master/PlaneIdentifier)**: The tutorial shows how to solve a classification task with a neural network using the PyTorch library, export the model to ONNX format and deploy the model with the Windows Machine Learning application that can run on any Windows device.
+- **[PyTorch Image Classification](https://github.com/Microsoft/Windows-AppConsult-Samples-UWP/tree/master/PlaneIdentifier)**: The tutorial shows how to train an image classification neural network model using PyTorch, export the model to the ONNX format, and deploy it in a Windows Machine Learning application running locally on your Windows device.
+- **[YoloV4 Object Detection](https://github.com/Microsoft/Windows-AppConsult-Samples-UWP/tree/master/PlaneIdentifier)**: This tutorial shows how to build a UWP C# app that uses the YOLOv4 model to detect objects in video streams.
 
 ## Developer Tools
 
@@ -129,7 +134,10 @@ These advanced samples show how to use various binding and evaluation features i
 
     Download for [VS 2017](https://marketplace.visualstudio.com/items?itemName=WinML.mlgen), [VS 2019](https://marketplace.visualstudio.com/items?itemName=WinML.MLGenV2)
 
-  - Check out the [Model Samples](#model-samples) and [Advanced Scenario Samples](#advanced-scenarios).
+  - **[WinML Samples Gallery](https://github.com/microsoft/Windows-Machine-Learning/tree/master/Samples/WinMLSamplesGallery):** explore a variety of ML integration scenarios and models.
+
+  - Check out the [Model Samples](#model-samples) and [Advanced Scenario Samples](#advanced-scenarios) to learn how to use Windows ML in your application.
+  
 
 
 
