YOLOv8 Object Detection for Number Plate Recognition

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 tutorials

• Source code used in the course

• Hands-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 course

• Priority support

Table of Contents

1. What is Object Detection

2. Advancements in Object Detection

3. YOLO: The Object Detection Framework

•     3.1. What is YOLO    
•     3.2. How YOLO works    
•     3.3. YOLO Architecture    
•     3.4. YOLO Versions

4. Environment Setup

•     4.1. Install Miniconda    
•     4.2. Install the Required Packages    
•     4.3. Install CUDA and cuDNN for GPU support    
•     4.4. Project Structure

5. Data Preparation

•     5.1. Gathering the Data    
•     5.2. Labeling the Data    
•     5.3. Splitting the Data    
•     5.4. Creating the YAML File 

6. Training the YOLO Model

•     6.1. Choose a Model
•     6.2. Start Training

7. Detecting Number Plates with the Trained Model

•     7.1. Number Plate Detection in Images    
•     7.2. Number Plate Detection in Videos

8. Recognizing Number Plates Using OCR

•     8.1. Number Plate Recognition in Images    
•     8.2. Number Plate Recognition in Videos

9. Create a Web Application with Streamlit

•     9.1. Introduction    
•     9.2. Installing Streamlit    
•     9.3. Creating a New Streamlit App    
•     9.4. Adding Upload Feature    
•     9.5. Integrating our Number Plate Recognition System with Streamlit

10. Conclusion