Update how-to-use-automl-small-object-detect.md

Author: ssalgadodev

articles/machine-learning/how-to-use-automl-small-object-detect.md

@@ -8,7 +8,7 @@ ms.reviewer: ssalgado
 ms.service: machine-learning
 ms.subservice: automl
 ms.topic: how-to
-ms.date: 10/13/2021
+ms.date: 03/25/2024
 ms.custom: sdkv2, devx-track-python
 ---
 
@@ -17,9 +17,9 @@ ms.custom: sdkv2, devx-track-python
 [!INCLUDE [dev v2](includes/machine-learning-dev-v2.md)]
 
 
-In this article, you'll learn how to train an object detection model to detect small objects in high-resolution images with [automated ML](concept-automated-ml.md) in Azure Machine Learning.
+In this article, you learn how to train an object detection model to detect small objects in high-resolution images with [automated ML](concept-automated-ml.md) in Azure Machine Learning.
 
-Typically, computer vision models for object detection work well for datasets with relatively large objects. However, due to memory and computational constraints, these models tend to under-perform when tasked to detect small objects in high-resolution images. Because high-resolution images are typically large, they are resized before input into the model, which limits their capability to detect smaller objects--relative to the initial image size.
+Typically, computer vision models for object detection work well for datasets with relatively large objects. However, due to memory and computational constraints, these models tend to under-perform when tasked to detect small objects in high-resolution images. Because high-resolution images are typically large, they're resized before input into the model, which limits their capability to detect smaller objects--relative to the initial image size.
 
 To help with this problem, automated ML supports tiling as part of the computer vision capabilities. The tiling capability in automated ML is based on the concepts in [The Power of Tiling for Small Object Detection](https://openaccess.thecvf.com/content_CVPRW_2019/papers/UAVision/Unel_The_Power_of_Tiling_for_Small_Object_Detection_CVPRW_2019_paper.pdf).
 
@@ -39,11 +39,11 @@ Small object detection using tiling is supported for all models supported by Aut
 
 ## Enable tiling during training
 
-To enable tiling, you can set the `tile_grid_size` parameter to a value like '3x2'; where 3 is the number of tiles along the width dimension and 2 is the number of tiles along the height dimension. When this parameter is set to '3x2', each image is split into a grid of 3 x 2 tiles. Each tile overlaps with the adjacent tiles, so that any objects that fall on the tile border are included completely in one of the tiles. This overlap can be controlled by the `tile_overlap_ratio` parameter, which defaults to 25%.
+To enable tiling, you can set the `tile_grid_size` parameter to a value like '3x2'; where 3 is the number of tiles along the width dimension and 2 is the number of tiles along the height dimension. When this parameter is set to '3x2'; each image is split into a grid of 3 x 2 tiles. Each tile overlaps with the adjacent tiles, so that any objects that fall on the tile border are included completely in one of the tiles. This overlap is controlled by the `tile_overlap_ratio` parameter, which defaults to 25%.
 
 When tiling is enabled, the entire image and the tiles generated from it are passed through the model. These images and tiles are resized according to the `min_size` and `max_size` parameters before feeding to the model. The computation time increases proportionally because of processing this extra data.
 
-For example, when the `tile_grid_size` parameter is '3x2', the computation time would be approximately seven times higher than without tiling.
+For example, when the `tile_grid_size` parameter is '3x2' the computation time would be approximately seven times higher than without tiling.
 
 You can specify the value for `tile_grid_size` in your training parameters as a string.
 
@@ -108,7 +108,7 @@ When a model trained with tiling is deployed, tiling also occurs during inferenc
 
 You also have the option to enable tiling only during inference without enabling it in training. To do so, set the `tile_grid_size` parameter only during inference, not for training.
 
-Doing so, may improve performance for some datasets, and won't incur the extra cost that comes with tiling at training time.
+Doing so, might improve performance for some datasets, and will not incur the extra cost that comes with tiling at training time.
 
 ## Tiling hyperparameters