yoloEYE

Description

This project utilizes YOLO (You Only Look Once) models for object detection tasks. It provides a user-friendly interface built with Streamlit, allowing users to easily upload images or video streams to see object detections in real-time. The application supports various YOLO models, including YOLOv8, YOLOv9, and YOLOv10; offering flexibility and accuracy in detecting objects across different scenarios.

Screenshots

Here are some screenshots of the app showcasing its key features and design:

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

What things you need to install the software and how to install them.

pip install -r requirements.txt

Installing

A step by step series of examples that tell you how to get a development environment running

Say what the code already does and you don’t need to do a thing like this.

cd your_project_directory
pip install -r requirements.txt

And repeat

streamlit run app.py

End with an example of getting some data regarding the system. It may be a good idea to describe the table structure.

Running the Tests

Explain how to run the automated tests for this system

pytest

Break down into end to end.

Deployment

Add additional notes about how to deploy this on a live system

Built With

• Python - Programming Language
• Streamlit - Framework for Building Machine Learning and Data Science Web Apps
• Ultralytics - Implementation of YOLO Models

Contributing

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/fooBar)
3. Commit your Changes (git commit -m 'Add some fooBar')
4. Push to the Branch (git push origin feature/fooBar)
5. Open a Pull Request