HomeCoursesDeep Dive into Object Detection with YOLO

Intermediate

10h

Updated 4 months ago

Deep Dive into Object Detection with YOLO

Gain insights into YOLO architecture, core concepts like IoU and NMS, and optimization strategies. Delve into real-time object detection, data augmentation, and hyperparameters for various use cases.

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Overview

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Object detection has revolutionized the field of computer vision, security, autonomous driving, and more. Among the various algorithms available, YOLO stands out due to its efficiency and accuracy. This course dives deep into the YOLO architecture, highlighting its significance in object detection. The course starts with the YOLO architecture, followed by tackling overfitting and mastering data augmentation. It delves into core YOLO concepts IoU and NMS, real-time object detection, data augmentation, and optimization strategies for different use cases. It concludes with a discussion on the role of hyperparameters in object detection. After completing this course, you will have a comprehensive understanding of YOLO and its applications in object detection. Whether you aim to advance in a career in autonomous systems, security, or any domain requiring real-time object detection, this course will be a significant stepping stone in your professional journey.

Object detection has revolutionized the field of computer vision, security, autonomous driving, and more. Among the various algo...Show More

WHAT YOU'LL LEARN

An understanding of different types of object detection models

An understanding of YOLO architecture and its significance in object detection

Familiarity with core concepts of YOLO, including Intersection over Union (IoU) and non-maximum suppression (NMS)

Hands-on experience implementing data augmentation techniques for improved model performance

Working knowledge of overfitting, its implications, and methods to prevent it

An understanding of topics like batch normalization, optimizers in YOLO, learning rate scheduling, mAP score evaluation, and loss calculations for object detection

An understanding of different types of object detection models

TAKEAWAY SKILLS

Machine Learning

Image Augmentation

Content

41 Lessons1 Project4 Quizzes3 Challenges

Introduction to Object Detection

5 Lessons

Get familiar with foundational and practical knowledge of YOLO object detection models.

Getting Started

Introduction

Why Is Object Detection Important?

Types of Object Detection Algorithms

Why Is YOLO So Popular?

Fundamentals for Understanding YOLO

16 Lessons

Unpack the core of CNNs, IoU, NMS, anchor boxes, bounding boxes, overfitting, and optimization.

Basics of the Convolutional Neural Network (CNN): Part I

Basics of Convolutional Neural Network (CNN: Part II

Understanding IoU (Intersection over Union)/Jaccard Index

Understanding NMS (Non-Maximum Suppression)

Understanding Anchor Boxes: Part I

Understanding Anchor Boxes: Part II

Bounding Box Predictions

A Summary of How YOLO Makes Predictions

What Is Overfitting in Object Detection?

Batch Normalization (BN)

Optimizers in YOLO

Understanding Learning Rate (LR) Schedulers

mAP Scores as Performance Metrics

Understanding Loss Calculation

Exercise: Build Your Own CNN

Quiz

Building a System for Safety Helmet Detection Based on YOLOv5

Project

Premium

YOLOv7 Architecture

11 Lessons

Explore YOLOv7's advanced architecture, feature handling, evolution, and neural network optimizations.

The Structure of YOLO (Backbone, Neck, and Head)

Evolution of YOLO Models

How does YOLO Handle Multi-Scale Predictions

Understanding the YOLOv7 Model Structure

Extended Efficient Layer Aggregation Networks (E-ELANs)

Model Scaling for Concatenation-Based Models

Trainable Bag-of-Freebies in YOLOv7

Decoding YOLOv8: A High-Level Overview

Exercise (Designing a Darknet-19 Architecture)

Exercise Solution

Quiz

Improving Model Performance: Handling Overfitting/Underfitting

4 Lessons

Grasp methods to enhance model robustness, reduce overfitting, and effectively handle varied data inputs.

Why Is Data Augmentation Important?

Exercise: Using the Albumentations Library for Augmentations

Adding Background Images to Reduce False Positives

Adding Synthetic Data to Our Dataset

Dealing With Small Datasets In ML

Project

Premium

Pre-Trained Models, Fine-Tuning, and Hyperparameters in OD

4 Lessons

Take a closer look at fine-tuning, pretrained models, and hyperparameters in YOLO.

Pretrained Models and Transfer Learning in YOLO

Introduction to Fine-Tuning and Hyperparameters

The Role of Hyperparameters in YOLO

Quiz: Practical Scenarios

Sun Detection Using YOLOv8

Project

Conclusion

1 Lessons

Focus on enhancing your object detection skills and applying them to real-world tasks.

Wrap Up

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Course Author:

Akanksha Nagar

Developed by MAANG Engineers

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