Onnxruntime Object Detection YOLO Ios

ONNX Runtime Mobile object detection using yolov8 iOS sample application

AI & Machine LearningSwiftMIT

Abstract

Onnxruntime Object Detection YOLO Ios is an open-source AI & Machine Learning project. ONNX Runtime Mobile object detection using yolov8 iOS sample application. This sample application makes use of Yolov8 object recognition to identify objects in images and provides bounding boxes, class, and score information about the identified class. It is built using Swift. 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

This sample application makes use of Yolov8 object recognition to identify objects in images and provides bounding boxes, class, and score information about the identified class.

We use pre-trained yolov8 model in this sample app. The original yolov8n.pt model can be downloaded. Here

2. Objective

ONNX Runtime Mobile object detection using yolov8 iOS sample application

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

4. Technology Stack

Swift

5. System Requirements

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

  • Xcode on macOS
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/eumentis/onnxruntime-object-detection-yolo-ios.git
cd onnxruntime-object-detection-yolo-ios

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 eumentis 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

Want to build this as your internship project?

Work on an AI & Machine Learning project like this with mentor guidance, weekly reviews and an internship certificate from Training Trains, Erode — online or offline.

Apply for AI & Machine Learning Internship