YOLOv5 Object Detection

This repository contains a Python script for performing object detection using the YOLOv5 model on images containing cars, bicycles, buses, trucks, and motorbikes. The detected objects are highlighted with bounding boxes and labeled accordingly.

Overview

The yolo.py script loads the pre-trained YOLOv5 model and performs object detection on images. It then displays the detection results using OpenCV.

Usage

• Clone this repository: --git clone https://github.com/kunalkushwahatg/yolov5.git
• --cd yolov5-object-detection
• Download models from yolov5_models and place it in main folder.
• Place your images containing objects you want to detect in the images directory.
• Run the yolo.py script
• This script also contains a function for detection that can be used for flask / fastapi / django based webapps.

Pre-trained Models

This repository includes pre-trained YOLOv5 models of different sizes:

• YOLOv5s (Small)
• YOLOv5m (Medium)
• YOLOv5l (Large)
• YOLOv5x (Extra Large)

You can switch between these models by modifying the model initialization line in the yolo.py script according to your requirements: model = Model('yolov5s.pt', device='cpu')

Requirements

• Python 3.x
• PyTorch
• OpenCV
• NumPy

Install the required dependencies using: pip install -r requirements.txt

Acknowledgments

• YOLOv5: Ultralytics YOLOv5
• COCO dataset: COCO