Object Detection Yolov4Web

Abstract

1. Introduction

I am Rahul Arepaka, II year CompSci student at Ecole School of Engineering, Mahindra University

Object Detection using coco.names dataset , weights and configuration files of real time object detection algorithm YOLOv4

2. Objective

A Web App of Object Detection using YOLOv4 and Streamlit

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

4. Technology Stack

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/rahularepaka/ObjectDetectionYolov4Web.git
cd ObjectDetectionYolov4Web

# Paramaters which can be tuned to your requirements
confThreshold = 0.5
nmsThreshold = 0.2

# for reading all the datasets from the coco.names file into the array
with open("coco.names", 'rt') as f:
    class_names = f.read().rstrip('\n').split('\n')

# configration and weights file location
model_config_file = "yolo-config\\yolov3-tiny.cfg"
model_weight = "yolo-weights\\yolov3-tiny.weights"

streamlit run main.py

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 rahularepaka 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.