CusVis freshness

Author: PatrickFarley

articles/cognitive-services/Custom-Vision-Service/csharp-tutorial-od.md

@@ -3,14 +3,14 @@ title: "Quickstart: Create an object detection project with the SDK for C# - Cus
 titleSuffix: Azure Cognitive Services
 description: Create a project, add tags, upload images, train your project, and detect objects using the .NET SDK with C#.
 services: cognitive-services
-author: areddish
+author: PatrickFarley
 manager: nitinme
 
 ms.service: cognitive-services
 ms.subservice: custom-vision
 ms.topic: quickstart
-ms.date: 12/05/2019
-ms.author: areddish
+ms.date: 04/14/2020
+ms.author: pafarley
 ---
 
 # Quickstart: Create an object detection project with the Custom Vision .NET SDK

articles/cognitive-services/Custom-Vision-Service/csharp-tutorial.md

@@ -3,15 +3,16 @@ title: "Quickstart: Create an image classification project with the Custom Visio
 titleSuffix: Azure Cognitive Services
 description: Create a project, add tags, upload images, train your project, and make a prediction using the .NET SDK with C#.
 services: cognitive-services
-author: anrothMSFT
+author: PatrickFarley
 manager: nitinme
 
 ms.service: cognitive-services
 ms.subservice: custom-vision
 ms.topic: quickstart
-ms.date: 12/05/2019
-ms.author: anroth
+ms.date: 04/14/2020
+ms.author: pafarley
 ---
+
 # Quickstart: Create an image classification project with the Custom Vision .NET SDK
 
 This article provides information and sample code to help you get started using the Custom Vision SDK with C# to build an image classification model. After it's created, you can add tags, upload images, train the project, obtain the project's default prediction endpoint URL, and use the endpoint to programmatically test an image. Use this example as a template for building your own .NET application. If you want to go through the process of building and using a classification model _without_ code, see the [browser-based guidance](getting-started-build-a-classifier.md) instead.

articles/cognitive-services/Custom-Vision-Service/custom-vision-onnx-windows-ml.md

@@ -9,10 +9,11 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: custom-vision
 ms.topic: tutorial
-ms.date: 12/05/2019
+ms.date: 04/14/2020
 ms.author: pafarley
 # As a developer, I want to use a custom vision model with Windows ML.
 ---
+
 # Tutorial: Use an ONNX model from Custom Vision with Windows ML (preview)
 
 Learn how to use an ONNX model exported from the Custom Vision service with Windows ML (preview).

articles/cognitive-services/Custom-Vision-Service/export-model-python.md

@@ -3,14 +3,14 @@ title: "Tutorial: Run TensorFlow model in Python - Custom Vision Service"
 titleSuffix: Azure Cognitive Services
 description: Run a TensorFlow model in Python. This article only applies to models exported from image classification projects in the Custom Vision service.
 services: cognitive-services
-author: areddish
+author: PatrickFarley
 manager: nitinme
 
 ms.service: cognitive-services
 ms.subservice: custom-vision
 ms.topic: tutorial
-ms.date: 12/05/2019
-ms.author: areddish
+ms.date: 04/14/2020
+ms.author: pafarley
 ---
 
 # Tutorial: Run TensorFlow model in Python

articles/cognitive-services/Custom-Vision-Service/get-started-build-detector.md

@@ -3,14 +3,14 @@ title: "Quickstart Build an object detector - Custom Vision Service"
 titleSuffix: Azure Cognitive Services
 description: In this quickstart, you'll learn how to use the Custom Vision website to create an image classification model.
 services: cognitive-services
-author: anrothMSFT
+author: PatrickFarley
 manager: nitinme
 
 ms.service: cognitive-services
 ms.subservice: custom-vision
 ms.topic: quickstart
-ms.date: 12/05/2019
-ms.author: anroth
+ms.date: 04/14/2020
+ms.author: pafarley
 ---
 
 # Quickstart: How to build an object detector with Custom Vision

articles/cognitive-services/Custom-Vision-Service/getting-started-build-a-classifier.md

@@ -3,14 +3,14 @@ title: "Quickstart Build a classifier - Custom Vision Service"
 titleSuffix: Azure Cognitive Services
 description: In this quickstart, you'll learn how to use the Custom Vision website to create an image classification model.
 services: cognitive-services
-author: anrothMSFT
+author: PatrickFarley
 manager: nitinme
 
 ms.service: cognitive-services
 ms.subservice: custom-vision
 ms.topic: quickstart
-ms.date: 12/05/2019
-ms.author: anroth
+ms.date: 04/14/2020
+ms.author: pafarley
 ---
 
 # Quickstart: How to build a classifier with Custom Vision
@@ -41,7 +41,7 @@ In your web browser, navigate to the [Custom Vision web page](https://customvisi
 1. Enter a name and a description for the project. Then select a Resource Group. If your signed-in account is associated with an Azure account, the Resource Group dropdown will display all of your Azure Resource Groups that include a Custom Vision Service Resource. 
 
    > [!NOTE]
-   > If no resource group is available, please confirm that you have logged into [customvision.ai](https://customvision.ai) with the same account as you used to log into the [Azure portal](https://portal.azure.com/). Also, please confirm you have selected the same “Directory” in the Custom Vision portal as the directory in the Azure portal where your Custom Vision resources are located. In both sites, you may select your directory from the drop down account menu at the top right corner of the screen. 
+   > If no resource group is available, please confirm that you have logged into [customvision.ai](https://customvision.ai) with the same account as you used to log into the [Azure portal](https://portal.azure.com/). Also, please confirm you have selected the same "Directory" in the Custom Vision portal as the directory in the Azure portal where your Custom Vision resources are located. In both sites, you may select your directory from the drop down account menu at the top right corner of the screen. 
 
 1. Select __Classification__ under __Project Types__. Then, under __Classification Types__, choose either **Multilabel** or **Multiclass**, depending on your use case. Multilabel classification applies any number of your tags to an image (zero or more), while multiclass classification sorts images into single categories (every image you submit will be sorted into the most likely tag). You'll be able to change the classification type later if you want to.
 

articles/cognitive-services/Custom-Vision-Service/go-tutorial-object-detection.md

@@ -9,7 +9,7 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: custom-vision
 ms.topic: quickstart
-ms.date: 12/05/2019
+ms.date: 04/14/2020
 ---
 
 # Quickstart: Create an object detection project with the Custom Vision Go SDK
@@ -280,7 +280,7 @@ The output of the application should appear in the console. You can then verify
 
 ## Next steps
 
-Now you have seen how every step of the object detection process can be done in code. This sample executes a single training iteration, but often you will need to train and test your model multiple times in order to make it more accurate. The following guide deals with image classification, but its principles are similar to object detection.
+Now you've seen how every step of the object detection process can be done in code. This sample executes a single training iteration, but often you'll need to train and test your model multiple times in order to make it more accurate. The following training guide deals with image classification, but its principles are similar to object detection.
 
 > [!div class="nextstepaction"]
 > [Test and retrain a model](test-your-model.md)

articles/cognitive-services/Custom-Vision-Service/go-tutorial.md

@@ -9,7 +9,7 @@ manager: daauld
 ms.service: cognitive-services
 ms.subservice: custom-vision
 ms.topic: quickstart
-ms.date: 12/05/2019
+ms.date: 04/14/2020
 ms.author: areddish
 ---
 
@@ -159,7 +159,7 @@ To send an image to the prediction endpoint and retrieve the prediction, add the
     testImageData, _ := ioutil.ReadFile(path.Join(sampleDataDirectory, "Test", "test_image.jpg"))
     results, _ := predictor.ClassifyImage(ctx, *project.ID, iteration_publish_name, ioutil.NopCloser(bytes.NewReader(testImageData)), "")
 
-    for _, prediction := range *results.Predictions	{
+    for _, prediction := range *results.Predictions    {
         fmt.Printf("\t%s: %.2f%%", *prediction.TagName, *prediction.Probability * 100)
         fmt.Println("")
     }
@@ -195,7 +195,7 @@ You can then verify that the test image (found in **<base_image_url>/Images/Test
 
 ## Next steps
 
-Now you have seen how every step of the image classification process can be done in code. This sample executes a single training iteration, but often you will need to train and test your model multiple times in order to make it more accurate.
+Now you've seen how every step of the object detection process can be done in code. This sample executes a single training iteration, but often you'll need to train and test your model multiple times in order to make it more accurate.
 
 > [!div class="nextstepaction"]
 > [Test and retrain a model](test-your-model.md)

articles/cognitive-services/Custom-Vision-Service/home.md

@@ -9,7 +9,7 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: custom-vision
 ms.topic: overview
-ms.date: 12/05/2019
+ms.date: 04/14/2020
 ms.author: pafarley
 #Customer intent: As a data scientist/developer, I want to understand what the Custom Vision service does so that I can determine if it's suitable for my project.
 ---
@@ -18,7 +18,7 @@ ms.author: pafarley
 
 [!INCLUDE [TLS 1.2 enforcement](../../../includes/cognitive-services-tls-announcement.md)]
 
-Custom Vision is a cognitive service that lets you build, deploy, and improve your own image classifiers. An image classifier is an AI service that applies labels (which represent _classes_) to images, according to their visual characteristics. Unlike the [Computer Vision](https://docs.microsoft.com/azure/cognitive-services/computer-vision/home) service, Custom Vision allows you to determine the labels to apply.
+Azure Custom Vision is a cognitive service that lets you build, deploy, and improve your own image classifiers. An image classifier is an AI service that applies labels (which represent _classes_) to images, according to their visual characteristics. Unlike the [Computer Vision](https://docs.microsoft.com/azure/cognitive-services/computer-vision/home) service, Custom Vision allows you to specify the labels to apply.
 
 ## What it does
 

articles/cognitive-services/Custom-Vision-Service/iot-visual-alerts-tutorial.md

@@ -9,15 +9,15 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: custom-vision
 ms.topic: tutorial
-ms.date: 12/05/2019
+ms.date: 04/14/2020
 ms.author: pafarley
 ---
 
 # Tutorial: Use Custom Vision with an IoT device to report visual states
 
-This sample app illustrates how to use Custom Vision to train a device with a camera to detect visual states. You can run this detection scenario on an IoT device by using an ONNX model exported from the Custom Vision service.
+This sample app illustrates how to use Custom Vision to train a device with a camera to detect visual states. You can run this detection scenario on an IoT device by using an exported ONNX model.
 
-A visual state describes the content of an image: an empty room or a room with people, an empty driveway or a driveway with a truck, and so on. In the image below, you can see the app detect when a banana or an apple is placed in front of the camera.
+A visual state describes the content of an image: an empty room or a room with people, an empty driveway with a truck, and so on. In the image below, you can see the app detect when a banana or an apple is placed in front of the camera.
 
 ![Animation of a UI labeling fruit in front of the camera](./media/iot-visual-alerts-tutorial/scoring.gif)
 
@@ -37,7 +37,7 @@ If you don't have an Azure subscription, create a [free account](https://azure.m
 * You'll also need to [create an IoT Hub resource](https://ms.portal.azure.com/#create/Microsoft.IotHub) on Azure.
 * [Visual Studio 2015 or later](https://www.visualstudio.com/downloads/)
 * Optionally, an IoT device running Windows 10 IoT Core version 17763 or higher. You can also run the app directly from your PC.
-   * For Raspberry Pi 2 and 3, you can set up Windows 10 directly from the IoT Dashboard app. For other devices such as DrangonBoard, you'll need to flash it using the [eMMC method](https://docs.microsoft.com/windows/iot-core/tutorials/quickstarter/devicesetup#flashing-with-emmc-for-dragonboard-410c-other-qualcomm-devices). If you need help setting up a new device, see [Setting up your device](https://docs.microsoft.com/windows/iot-core/tutorials/quickstarter/devicesetup) in the Windows IoT documentation.
+   * For Raspberry Pi 2 and 3, you can set up Windows 10 directly from the IoT Dashboard app. For other devices such as DrangonBoard, you'll need to flash it using the [eMMC method](https://docs.microsoft.com/windows/iot-core/tutorials/quickstarter/devicesetup#flashing-with-emmc-for-dragonboard-410c-other-qualcomm-devices). If you need help with setting up a new device, see [Setting up your device](https://docs.microsoft.com/windows/iot-core/tutorials/quickstarter/devicesetup) in the Windows IoT documentation.
 
 ## About the Visual Alerts app
 
@@ -88,7 +88,7 @@ To set up a model, you need to put the app in the **Capturing Training Images**
 * If you're running the app on PC, use the button on the top-right corner of the UI.
 * If you're running the app on an IoT device, call the `EnterLearningMode` method on the device through the IoT Hub. You can call it through the device entry in the IoT Hub menu on the Azure portal, or with a tool such as [IoT Hub Device Explorer](https://github.com/Azure/azure-iot-sdk-csharp/tree/master/tools/DeviceExplorer).
 
-When the app enters the **Capturing Training Images** state, it will capture about two images every second until it has reached the target number of images. By default, this is 30 images, but you can set this parameter by passing the desired number as an argument to the `EnterLearningMode` IoT Hub method. 
+When the app enters the **Capturing Training Images** state, it will capture about two images every second until it has reached the target number of images. By default, the target is 30 images, but you can set this parameter by passing the desired number as an argument to the `EnterLearningMode` IoT Hub method. 
 
 While the app is capturing images, you must expose the camera to the types of visual states that you want to detect (for example, an empty room, a room with
 people, an empty desk, a desk with a toy truck, and so on).