> For the complete documentation index, see [llms.txt](https://docs.valohai.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.valohai.com/project-gallery/computer-vision/yolo-example.md).

# Object Detection with YOLO

***

### Overview

This project demonstrates:

* Training YOLOv5 and YOLOv5-Seg models
* Validating trained models
* Running YOLOv8 for inference and ONNX export
* Using datasets stored in S3 with Valohai inputs and outputs

***

### Steps

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

Store datasets in Amazon S3 and configure Valohai inputs for retrieval and processing in training.
{% endstep %}

{% step %}
**Configuration in Valohai**

Set up Valohai pipelines to automate the training, validation, and inference stages.
{% endstep %}

{% step %}
**Training Execution**

Execute training runs on YOLOv5 and YOLOv5-Seg with predefined configurations to start building robust models for object detection.
{% endstep %}

{% step %}
**Validation Process**

Conduct validation on trained models to compare predicted results against benchmark datasets, refining model performance accordingly.
{% endstep %}

{% step %}
**Inference Implementation**

Deploy YOLOv8 for inference tasks, focusing on optimizing speed and accuracy. Transition the model to ONNX format when necessary for enhanced compatibility.
{% endstep %}
{% endstepper %}

***

### GitHub repository

The repository walks you through how to go through the above steps:

{% embed url="<https://github.com/valohai/yolo-example>" %}


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