YOLOv8 Object Detection for Number Plate Recognition

Published 2/2024
Created by Yacine Rouizi
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 26 Lectures ( 2h 50m ) | Size: 1.5 GB

Collect and Label Data, Train YOLOv8 Model, Implement OCR to Recognize Text, Integrate with a Streamlit Web App

What you’ll learn:
Set up your environment for object detection
Learn how to recognize number plates in images and videos using OCR
Collect and label a custom dataset for training the YOLOv8 model
Integrating the number plate recognition system with a Streamlit web application
Train the YOLOv8 model and learn how to use it to detect number plates in images and videos

Requirements:
Basic knowledge of Python programming, OpenCV, and computer vision.

Description:
In this comprehensive course, you’ll learn everything you need to know to master YOLOv8. With detailed explanations, practical examples, and step-by-step tutorials, this course will help you build your understanding of YOLOv8 from the ground up.Discover how to train the YOLOv8 model to accurately detect and recognize license plates in images and real-time videos.From data collection to deployment, master every step of building an end-to-end ANPR system with YOLOv8.What you’ll get:Here’s what you’ll get with this course:3 hour of HD video tutorialsSource code used in the courseHands-on coding experience and real-world implementation.Step-by-step guide with clear explanations and code examples.Gain practical skills that can be applied to real-world projects.Lifetime access to the coursePriority supportWhat is covered in this course:Just so that you have some idea of what you will learn in this course, these are the topics that we will cover:Set Up Your Environment for Object DetectionCollect the Data for Training the ModelTrain the YOLO Model and Learn How to Use it to Detect Number Plates in Images and Video StreamsLearn How to Recognize Number Plates in Images and Videos Using OCRIntegrating the Number Plate Recognition System with a Streamlit Web Application