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docs(how-to-guides): add training docs for centerpoint
Signed-off-by: Kaan Çolak <kaancolak95@gmail.com>
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docs/how-to-guides/training-machine-learning-models/training-models.md

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Detailed instructions for training the traffic light classifier model can be found **[here](https://github.com/autowarefoundation/autoware.universe/blob/main/perception/traffic_light_classifier/README.md)**.
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## Training CenterPoint 3D object detection model
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Training traffic light detection model and lidar CenterPoint model will be added there.
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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
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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
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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)**.
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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
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**"lidar_centerpoint"** package. These instructions will provide a step-by-step guide for training CenterPoint model.
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In order to assist you with your training process, we have also included an example dataset in the Tier4 dataset format.
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This dataset contains 600 lidar frames and covers 5 classes, including 6905 cars, 3951 pedestrians, 75 cyclists, 162 buses, and 326 trucks.
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You can utilize this example dataset to facilitate your training efforts.
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Detailed instructions for training the CenterPoint model can be found **[here](https://github.com/autowarefoundation/autoware.universe/blob/main/perception/lidar_centerpoint/README.md)**.

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