Update tutorial samples to 1.6.0 (dotnet#919)

* Update BikeDemandForecasting * Updated ObjectDetection * Update image classification sample * Added sample explorer metadata * Fix version * Fixing build errors
ylu3 · Aug 9, 2021 · 90f2a8d · 90f2a8d
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diff --git a/samples/Directory.Build.props b/samples/Directory.Build.props
@@ -1,8 +1,8 @@
 <Project>
 
   <PropertyGroup>
-    <MicrosoftMLVersion>1.5.4</MicrosoftMLVersion>
-    <MicrosoftMLPreviewVersion>0.17.4</MicrosoftMLPreviewVersion>
+    <MicrosoftMLVersion>1.6.0</MicrosoftMLVersion>
+    <MicrosoftMLPreviewVersion>0.18.0</MicrosoftMLPreviewVersion>
   </PropertyGroup>
 
 </Project>
diff --git a/samples/csharp/end-to-end-apps/ObjectDetection-Onnx/README.md b/samples/csharp/end-to-end-apps/ObjectDetection-Onnx/README.md
@@ -2,7 +2,7 @@
 
 | ML.NET version | API type    | Status     | App Type    | Data type   | Scenario         | ML Task       | Algorithms                        |
 |----------------|-------------|------------|-------------|-------------|------------------|---------------|-----------------------------------|
-| v1.5.0         | Dynamic API | Up-to-date | End-End app | image files | Object Detection | Deep Learning | ONNX: Tiny YOLOv2 & Custom Vision |
+| v1.6.0         | Dynamic API | Up-to-date | End-End app | image files | Object Detection | Deep Learning | ONNX: Tiny YOLOv2 & Custom Vision |
 
 ## Problem
 

diff --git a/...audDetection/CreditCardFraudDetection.Predictor/CreditCardFraudDetection.Predictor.csproj b/...audDetection/CreditCardFraudDetection.Predictor/CreditCardFraudDetection.Predictor.csproj
@@ -2,7 +2,7 @@
 
   <PropertyGroup>
     <OutputType>Exe</OutputType>
-    <TargetFramework>netcoreapp2.1</TargetFramework>
+    <TargetFramework>net5</TargetFramework>
     <LangVersion>7.2</LangVersion>
   </PropertyGroup>
 

diff --git a/...rdFraudDetection/CreditCardFraudDetection.Trainer/CreditCardFraudDetection.Trainer.csproj b/...rdFraudDetection/CreditCardFraudDetection.Trainer/CreditCardFraudDetection.Trainer.csproj
@@ -2,7 +2,7 @@
 
   <PropertyGroup>
     <OutputType>Exe</OutputType>
-    <TargetFramework>netcoreapp2.1</TargetFramework>
+    <TargetFramework>net5</TargetFramework>
     <LangVersion>7.2</LangVersion>
   </PropertyGroup>
 

diff --git a/...ification_Binary/DeepLearning_ImageClassification/DeepLearning_ImageClassification.csproj b/...ification_Binary/DeepLearning_ImageClassification/DeepLearning_ImageClassification.csproj
@@ -2,7 +2,7 @@
 
   <PropertyGroup>
     <OutputType>Exe</OutputType>
-    <TargetFramework>netcoreapp3.1</TargetFramework>
+    <TargetFramework>net5</TargetFramework>
   </PropertyGroup>
 
   <ItemGroup>

diff --git a/samples/csharp/getting-started/DeepLearning_ImageClassification_Binary/README.md b/samples/csharp/getting-started/DeepLearning_ImageClassification_Binary/README.md
@@ -9,13 +9,14 @@ products:
 - dotnet
 - dotnet-core
 - vs
+- mlnet
 ---
 
 # Image Classification Model Training (two categories) - Preferred API (Based on native TensorFlow transfer learning)
 
 | ML.NET version | API type          | Status                        | App Type    | Data type | Scenario            | ML Task                   | Algorithms                  |
 |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.5.0 | Dynamic API | Up-to-date | Console app | Image files | Image classification | Image classification with TensorFlow model retrain based on transfer learning  | InceptionV3 or ResNetV2 |
+| v1.6.0 | Dynamic API | Up-to-date | Console app | Image files | Image classification | Image classification with TensorFlow model retrain based on transfer learning  | InceptionV3 or ResNetV2 |
 
 For a detailed explanation of how to build this application, see the accompanying [tutorial](https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/image-classification-api-transfer-learning) on the Microsoft Docs site.
 

diff --git a/...tarted/DeepLearning_ObjectDetection_Onnx/ObjectDetectionConsoleApp/ObjectDetection.csproj b/...tarted/DeepLearning_ObjectDetection_Onnx/ObjectDetectionConsoleApp/ObjectDetection.csproj
@@ -2,7 +2,7 @@
 
   <PropertyGroup>
     <OutputType>Exe</OutputType>
-    <TargetFramework>netcoreapp2.1</TargetFramework>
+    <TargetFramework>net5</TargetFramework>
   </PropertyGroup>
 
   <ItemGroup>
@@ -28,10 +28,10 @@
   </ItemGroup>
 
   <ItemGroup>
-    <PackageReference Include="Microsoft.ML" Version="1.5.0" />
-    <PackageReference Include="Microsoft.ML.ImageAnalytics" Version="1.5.0" />
-    <PackageReference Include="Microsoft.ML.OnnxRuntime" Version="1.3.0" />
-    <PackageReference Include="Microsoft.ML.OnnxTransformer" Version="1.5.0" />
+    <PackageReference Include="Microsoft.ML" Version="$(MicrosoftMLVersion)" />
+    <PackageReference Include="Microsoft.ML.ImageAnalytics" Version="$(MicrosoftMLVersion)" />
+    <PackageReference Include="Microsoft.ML.OnnxRuntime" Version="1.8.1" />
+    <PackageReference Include="Microsoft.ML.OnnxTransformer" Version="$(MicrosoftMLVersion)" />
   </ItemGroup>
 
   <ItemGroup>

diff --git a/samples/csharp/getting-started/DeepLearning_ObjectDetection_Onnx/README.md b/samples/csharp/getting-started/DeepLearning_ObjectDetection_Onnx/README.md
@@ -1,8 +1,22 @@
+---
+page_type: sample
+name: "Detect objects in images using a pretrained ONNX model and ML.NET"
+description: "Use ML.NET and a pretrained YOLOv2 object detection ONNX model to identify objects in an image"
+urlFragment: "mlnet-onnx-object-detection"
+languages:
+- csharp
+products:
+- dotnet
+- dotnet-core
+- vs
+- mlnet
+---
+
 # Object Detection
 
 | ML.NET version | API type          | Status                        | App Type    | Data type | Scenario            | ML Task                   | Algorithms                  |
 |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.4           | Dynamic API | Up-to-date | Console app | image files | Object Detection | Deep Learning  | Tiny Yolo2 ONNX model |
+| v1.6.0           | Dynamic API | Up-to-date | Console app | image files | Object Detection | Deep Learning  | Tiny Yolo2 ONNX model |
 
 
 For a detailed explanation of how to build this application, see the accompanying [tutorial](https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/object-detection-onnx) on the Microsoft Docs site. 

diff --git a/...-started/Forecasting_BikeSharingDemand/BikeDemandForecasting/BikeDemandForecasting.csproj b/...-started/Forecasting_BikeSharingDemand/BikeDemandForecasting/BikeDemandForecasting.csproj
@@ -2,12 +2,12 @@
 
   <PropertyGroup>
     <OutputType>Exe</OutputType>
-    <TargetFramework>netcoreapp3.1</TargetFramework>
+    <TargetFramework>net5</TargetFramework>
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Microsoft.ML" Version="1.5.0" />
-    <PackageReference Include="Microsoft.ML.TimeSeries" Version="1.5.0" />
+    <PackageReference Include="Microsoft.ML" Version="$(MicrosoftMLVersion)" />
+    <PackageReference Include="Microsoft.ML.TimeSeries" Version="$(MicrosoftMLVersion)" />
     <PackageReference Include="System.Data.SqlClient" Version="4.8.1" />
   </ItemGroup>
 

diff --git a/samples/csharp/getting-started/Forecasting_BikeSharingDemand/README.md b/samples/csharp/getting-started/Forecasting_BikeSharingDemand/README.md
@@ -1,8 +1,22 @@
+---
+page_type: sample
+name: "Forecast bike rental demand with ML.NET"
+description: "Use ML.NET's Single Spectrum Analysis (SSA) time-series forecasting algorithm to predict demand for bike rentals"
+urlFragment: "mlnet-time-series-demand-forecasting"
+languages:
+- csharp
+products:
+- dotnet
+- dotnet-core
+- vs
+- mlnet
+---
+
 # Bike Sharing Demand - Forecasting
 
 | ML.NET version | API type          | Status                        | App Type    | Data type | Scenario            | ML Task                   | Algorithms                  |
 |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.4.0-preview2 | Dynamic API | Up-to-date | Console app | SQL Server | Demand prediction | Forecasting | Single Spectrum Analysis |
+| v1.6.0 | Dynamic API | Up-to-date | Console app | SQL Server | Demand prediction | Forecasting | Single Spectrum Analysis |
 
 In this sample, you can see how to load data from a relational database using the Database Loader to train a forecasting model that predicts bike rental demand.