Crop Recognition with YOLOv5

This project implements object recognition for crops using the YOLOv5 model. The dataset consists of 5,000 images categorized into three classes. This README provides an overview of the project, setup instructions, and how to use the trained model for inference.

Table of Contents

• Introduction
• Dataset
• Model
• Setup

Introduction

This project aims to accurately recognize and classify crops in images using the YOLOv5 model. YOLOv5 is known for its balance between speed and accuracy, making it ideal for real-time object detection tasks.

Dataset

The dataset consists of around 5,000 images divided into three classes. Each image is annotated with bounding boxes specifying the location and class of the crops.

Model

We used the YOLOv5 model for this project version due to its efficiency and accuracy for the given dataset size.

Setup

To set up the environment and dependencies, follow these steps: I will soon put the setup guide also .....