Object Detection with NVIDIA TAO Toolkit

Train and evaluate an object detection model with the NVIDIA TAO Toolkit on Valohai, using the KITTI dataset.

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Overview

This project shows how to:

• Preprocess and convert KITTI data into TFRecords

• Train a DetectNet_v2 model using TAO Toolkit

• Evaluate and visualize model performance

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Steps

1

Data Preparation

Preprocess the KITTI dataset and convert it to TFRecords for compatibility with the training pipeline.

2

Environment Setup

Set up the TAO Toolkit environment to allow for seamless model training and evaluation.

3

Training Execution

Train the DetectNet_v2 model using the TAO Toolkit to build a robust model for object detection.

4

Validation Process

Evaluate the trained model's performance on the validation dataset to ensure accuracy and reliability.

5

Visualization and Analysis

Visualize the model's predictions and results to assess performance and make necessary adjustments.

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GitHub Repository

The repository walks you through the steps above:

GitHub - valohai/Valohai-NVIDIA-TAOGitHub