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# 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.
## 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.
```bash
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.
```bash
cd your_project_directory
pip install -r requirements.txt
```
And repeat
```bash
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
```bash
pytest
```
Break down into end to end.
## Deployment
Add additional notes about how to deploy this on a live system
## Built With
* [Python](https://www.python.org/) - Programming Language
* [Streamlit](https://streamlit.io/) - Framework for Building Machine Learning and Data Science Web Apps
* [Ultralytics](https://github.com/ultralytics/yolov5) - 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
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