Training and Deploying Models

Overview

The Autoware offers a comprehensive array of machine learning models, tailored for a wide range of tasks including 2D and 3D object detection, traffic light recognition and more. These models have been meticulously trained utilizing open-mmlab's extensive repositories. By leveraging the provided scripts and following the training steps, you have the capability to train these models using your own dataset, tailoring them to your specific needs.

Furthermore, you will find the essential conversion scripts to deploy your trained models into Autoware using the mmdeploy repository.

Training traffic light classifier model

The traffic light classifier model within the Autoware has been trained using the mmlab/pretrained repository. The Autoware offers pretrained models based on EfficientNet-b1 and MobileNet-v2 architectures. To fine-tune these models, a total of 83,400 images were employed, comprising 58,600 for training, 14,800 for evaluation, and 10,000 for testing. These images represent Japanese traffic lights and were trained using TIER IV's internal dataset.

Name	Input Size	Test Accuracy
EfficientNet-b1	128 x 128	99.76%
MobileNet-v2	224 x 224	99.81%

Comprehensive training instructions for the traffic light classifier model are detailed within the readme file accompanying "traffic_light_classifier" package. These instructions will guide you through the process of training the model using your own dataset. To facilitate your training, we have also provided an example dataset containing three distinct classes (green, yellow, red), which you can leverage during the training process.

Detailed instructions for training the traffic light classifier model can be found here.

Training CenterPoint 3D object detection model

The CenterPoint 3D object detection model within the Autoware has been trained using the autowarefoundation/mmdetection3d repository.

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 Autoware's 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.

You can utilize this example dataset to facilitate your training efforts.