docs(how-to-guides): small fix

Signed-off-by: Kaan Çolak <[email protected]>
autowarefoundation · Dec 7, 2023 · 6039d45 · 6039d45
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Showing 1 changed file with 3 additions and 3 deletions.
diff --git a/docs/how-to-guides/training-machine-learning-models/training-models.md b/docs/how-to-guides/training-machine-learning-models/training-models.md
@@ -31,11 +31,11 @@ Detailed instructions for training the traffic light classifier model can be fou
 ## Training CenterPoint 3D object detection model
 
 The CenterPoint 3D object detection model within the Autoware has been trained using the **[open-mmlab/mmdetection3d](https://github.com/open-mmlab/mmdetection3d)** repository. CenterPoint
-implementation of mmdetection3d uses 10 input feature for PointPillars voxel encoder. However, Autoware employs 9 pillar features for CenterPoint, in accordance with the original research paper. Therefore, we have
-fork the original repository and made the necessary code modifications to support the use of 9 PointPillar input features. The forked repository can be found **[here](https://github.com/autowarefoundation/mmdetection3d)**.
+implementation of mmdetection3d uses 10 input features for the PointPillars voxel encoder. However, Autoware employs 9 pillar features for CenterPoint, in accordance with the original research paper. Therefore, we have
+forked the original repository and made the necessary code modifications to support the use of 9 PointPillar input features. The forked repository can be found **[here](https://github.com/autowarefoundation/mmdetection3d)**.
 
 To train custom CenterPoint models and convert them into ONNX format for deployment in Autoware, please refer to the instructions provided in the README file included with the
-**"lidar_centerpoint"** package. These instructions will provide a step-by-step guide for training CenterPoint model.
+**"lidar_centerpoint"** package. These instructions will provide a step-by-step guide for training the CenterPoint model.
 
 In order to assist you with your training process, we have also included an example dataset in the TIER IV dataset format.
 This dataset contains 600 lidar frames and covers 5 classes, including 6905 cars, 3951 pedestrians, 75 cyclists, 162 buses, and 326 trucks.