Training a Convolutional Neural Network for Object Detection: Number Plate Detection

Mastering Object Detection with Python is a comprehensive course designed to teach you how to build AI models that can recognize and locate objects in images and videos. Whether you're a beginner or have some experience in machine learning, this course will guide you through the essential concepts of object detection and its applications in various fields like security, healthcare, and retail. You'll learn how to set up your environment, explore different object detection models, and use tools like YOLO and Roboflow to label datasets. By the end, you'll be able to fine-tune models, deploy them to production, and optimize their performance for real-world use cases. This course provides a hands-on learning experience, helping you build the skills needed to create powerful computer vision solutions.

Mastering Object Detection with Python

What is Object Detection?

Real World Applications of Object Detection

How Does Object Detection Work?

What You Will Learn in This Book

Introduction to Object Detection

What is Feature Extraction?

Understanding Convolutional Neural Networks (CNNs)

How Convolution Works

Activation Function: Making CNNs Smarter

Pooling: Reducing Complexity

Fully Connected Layers: Making Predictions

Why CNNs Are Perfect for Object Detection

Feature Extraction and Convolutional Neural Networks

Setting Up Your Environment

Preparing the Data

Building the Convolutional Neural Network

Conclusion

Training a Convolutional Neural Network for Binary Classification: Cats vs. Dogs

Introduction to Bounding Box Detection

Classification vs. Object Detection

Understanding Bounding Boxes in Object Detection

Setting Up Your Python Environment

Dataset Overview

Data Preprocessing and Augmentation

Model Architecture

Model Training

Evaluating the Model

Analysis & Potential Improvements

Conclusion and Next Steps

What is YOLO?

Setting Up the Environment

Fine-tune the YOLO Model

Evaluate the Model

Make Predictions

Conclusion and Future Work

Fine-tuning a YOLO11 Object Detection Model for Kidney Stones Detection