# Object Detection with NVIDIA TAO Toolkit

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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

{% stepper %}
{% step %}
**Data Preparation**

Preprocess the KITTI dataset and convert it to TFRecords for compatibility with the training pipeline.
{% endstep %}

{% step %}
**Environment Setup**

Set up the TAO Toolkit environment to allow for seamless model training and evaluation.
{% endstep %}

{% step %}
**Training Execution**

Train the DetectNet\_v2 model using the TAO Toolkit to build a robust model for object detection.
{% endstep %}

{% step %}
**Validation Process**

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

{% step %}
**Visualization and Analysis**

Visualize the model's predictions and results to assess performance and make necessary adjustments.
{% endstep %}
{% endstepper %}

***

### GitHub Repository

The repository walks you through the steps above:

{% embed url="<https://github.com/valohai/Valohai-NVIDIA-TAO>" %}


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