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Readme.md

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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