Object Detection YOLOv8

An object detection system built using YOLOv8 and OpenCV, capable of detecting and tracking multiple objects

AI & Machine LearningPythonMIT

Abstract

Object Detection YOLOv8 is an open-source AI & Machine Learning project. An object detection system built using YOLOv8 and OpenCV, capable of detecting and tracking multiple objects. It is built using Python. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.

1. Introduction

An object detection system built using YOLOv8 and OpenCV, capable of detecting and tracking multiple objects in images and videos with high accuracy and real-time performance.

▶ Usage Run on an image python detect.py --image images/sample.jpg

Bounding boxes with labels are displayed in real time.

2. Objective

An object detection system built using YOLOv8 and OpenCV, capable of detecting and tracking multiple objects

This project demonstrates how Python can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

Python

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

  • Python 3.8 or later
  • pip / virtualenv for dependencies
  • VS Code, PyCharm or Jupyter Notebook
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/shazimjaved/Object-Detection-YOLOv8.git
cd Object-Detection-YOLOv8

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

  • Deploy the model as a web app with Streamlit, Flask or FastAPI
  • Compare against an additional model and report the metric difference
  • Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

  1. What dataset does the project use and how was it pre-processed?
  2. Which algorithm / model architecture is used and why was it chosen over alternatives?
  3. How are training and testing data split, and how is overfitting avoided?
  4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
  5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by shazimjaved and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.

View Source on GitHubDownload ZIP

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