cusvis freshness

Author: PatrickFarley

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

@@ -9,7 +9,7 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: custom-vision
 ms.topic: conceptual
-ms.date: 03/21/2019
+ms.date: 10/27/2021
 ms.author: pafarley
 ---
 

articles/cognitive-services/Custom-Vision-Service/index.yml

@@ -12,7 +12,7 @@ metadata:
   ms.topic: landing-page # Required
   author: PatrickFarley #Required; your GitHub user alias, with correct capitalization.
   ms.author: pafarley #Required; microsoft alias of author; optional team alias.
-  ms.date: 09/05/2019 #Required; mm/dd/yyyy format.
+  ms.date: 10/27/2021 #Required; mm/dd/yyyy format.
 
 # linkListType: architecture | concept | deploy | download | get-started | how-to-guide | learn | overview | quickstart | reference | tutorial | video | whats-new
 

articles/cognitive-services/Custom-Vision-Service/select-domain.md

@@ -46,6 +46,8 @@ From the settings tab of your Custom Vision project, you can select a domain for
 
 The models generated by compact domains can be exported to run locally. In the Custom Vision 3.4 public preview API, you can get a list of the exportable platforms for compact domains by calling the GetDomains API.
 
+All of the following domains support export in ONNX, TensorFlow,TensorFlowLite, TensorFlow.js, CoreML, and VAIDK formats, with the exception that the **Object Detection General (compact)** domain does not support VAIDK.
+
 Model performance varies by selected domain. In the table below, we report the model size and inference time on Intel Desktop CPU and NVidia GPU \[1\]. These numbers don't include preprocessing and postprocessing time.
 
 |Task|Domain|ID|Model Size|CPU inference time|GPU inference time|

articles/cognitive-services/Custom-Vision-Service/suggested-tags.md

@@ -9,13 +9,13 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: custom-vision
 ms.topic: conceptual
-ms.date: 10/29/2019
+ms.date: 10/27/2021
 ms.author: pafarley
 ---
 
 # Label images faster with Smart Labeler
 
-In this guide, you'll learn how to use Smart Labeler to generate suggested tags for images. This lets you label a large number of images more quickly when training a Custom Vision model.
+In this guide, you'll learn how to use Smart Labeler to generate suggested tags for images. This lets you label a large number of images more quickly when you're training a Custom Vision model.
 
 When you tag images for a Custom Vision model, the service uses the latest trained iteration of the model to predict the labels of untagged images. It then shows these predictions as suggested tags, based on the selected confidence threshold and prediction uncertainty. You can then either confirm or change the suggestions, speeding up the process of manually tagging the images for training.
 

articles/cognitive-services/Custom-Vision-Service/test-your-model.md

@@ -1,21 +1,21 @@
 ---
 title: Test and retrain a model - Custom Vision Service
 titleSuffix: Azure Cognitive Services
-description: This article will show you how to test an image and then use it to retrain your model in the Custom Vision service.
+description: Learn how to test an image and then use it to re-train your model in the Custom Vision service.
 services: cognitive-services
 author: PatrickFarley
 manager: nitinme
 
 ms.service: cognitive-services
 ms.subservice: custom-vision
 ms.topic: conceptual
-ms.date: 03/21/2019
+ms.date: 10/27/2021
 ms.author: pafarley
 ---
 
 # Test and retrain a model with Custom Vision Service
 
-After you train your model, you can quickly test it using a locally stored image or an online image. The test uses the most recently trained iteration.
+After you train your model, you can quickly test it using a locally stored image or an online image. The test uses the most recently trained iteration of your model.
 
 ## Test your model
 

articles/cognitive-services/Custom-Vision-Service/use-prediction-api.md

@@ -9,14 +9,14 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: custom-vision
 ms.topic: conceptual
-ms.date: 04/02/2019
+ms.date: 10/27/2021
 ms.author: pafarley
 ms.custom: devx-track-csharp
 ---
 
 # Use your model with the prediction API
 
-After you've train your model, you can test images programmatically by submitting them to the Prediction API endpoint.
+After you've train your model, you can test it programmatically by submitting images to the Prediction API endpoint.
 
 > [!NOTE]
 > This document demonstrates using C# to submit an image to the Prediction API. For more information and examples, see the [Prediction API reference](https://southcentralus.dev.cognitive.microsoft.com/docs/services/Custom_Vision_Prediction_3.0/operations/5c82db60bf6a2b11a8247c15).