https://github.com/davityak03/object-detection-in-images-using-yolo

This project demonstrates object detection using the YOLO (You Only Look Once) model, providing a solution for detecting and labeling multiple objects within images or video streams. This implementation covers image preprocessing, model inference, and visualization of detected objects.
https://github.com/davityak03/object-detection-in-images-using-yolo

computer-vision matplotlib-pyplot numpy object-detection opencv-python pandas python yolo

Last synced: 8 days ago
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This project demonstrates object detection using the YOLO (You Only Look Once) model, providing a solution for detecting and labeling multiple objects within images or video streams. This implementation covers image preprocessing, model inference, and visualization of detected objects.

• Host: GitHub
• URL: https://github.com/davityak03/object-detection-in-images-using-yolo
• Owner: Davityak03
• Created: 2024-09-08T09:11:25.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-09-08T09:18:06.000Z (2 months ago)
• Last Synced: 2024-10-31T13:04:34.640Z (8 days ago)
• Topics: computer-vision, matplotlib-pyplot, numpy, object-detection, opencv-python, pandas, python, yolo
• Language: Jupyter Notebook
• Homepage:
• Size: 1.86 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Object-Detection-in-Images-using-YOLO

## Overview

This Jupyter Notebook demonstrates the implementation of object detection using the YOLO (You Only Look Once) deep learning model. YOLO is a real-time object detection system capable of detecting multiple objects within an image or video stream. This notebook provides step-by-step instructions to load a pre-trained YOLO model, process images, and identify objects within those images.

## Features

- **Loading YOLO Model:** This notebook explains how to load the YOLOv3 model along with its configuration and weights files.

- **Image Preprocessing:** The notebook includes a section on image preprocessing, where the input image is resized, normalized, and converted into a blob for YOLO.

- **Object Detection:** It covers the process of detecting objects within an image, including extracting bounding boxes, confidence scores, and class labels.

- **Post-Processing:** The notebook includes post-processing steps such as applying non-max suppression (NMS) to filter overlapping boxes and enhance detection accuracy.

- **Visualization:** The final step involves visualizing the detected objects by drawing bounding boxes and labels on the image.

## Requirements

- Python 3.x

- OpenCV

- NumPy

- Matplotlib

- YOLOv3 weights and configuration files (`yolov3.weights`, `yolov3.cfg`)

## Usage

1. **Clone the Repository:** Clone this repository to your local machine.

2. **Download YOLO Weights:** Ensure you have the YOLOv3 weights file (`yolov3.weights`) and configuration file (`yolov3.cfg`).

3. **Run the Notebook:** Open the notebook in Jupyter and run each cell sequentially. Ensure that the necessary libraries are installed.

## Important Sections

- **Loading the YOLO Model:** Detailed steps on how to load the YOLO model and set up the required files.

- **Processing Images:** Steps to preprocess images before feeding them to the YOLO model.

- **Detection Logic:** Explanation of the detection logic, including how to interpret the model's output.

- **Visualization:** Final step to draw bounding boxes and labels on the detected objects.

## Acknowledgments

- The YOLO model was developed by Joseph Redmon and Ali Farhadi.

- This notebook utilizes the pre-trained YOLOv3 model for demonstration purposes.

## Final Output

![Image not Available at Moment](https://github.com/Davityak03/Object-Detection-in-Images-using-YOLO/blob/main/final%20image.png)