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Free Lessons (4)
Introduction to Object Detection
Getting Started
Introduction
Why Is Object Detection Important?
Types of Object Detection Algorithms
Why Is YOLO So Popular?
Fundamentals for Understanding YOLO
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
Project
Building a System for Safety Helmet Detection Based on YOLOv5
YOLOv7 Architecture
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
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
Project
Dealing With Small Datasets In ML
Pre-Trained Models, Fine-Tuning, and Hyperparameters in OD
Pretrained Models and Transfer Learning in YOLO
Introduction to Fine-Tuning and Hyperparameters
The Role of Hyperparameters in YOLO
Quiz: Practical Scenarios
Mini Project
Sun Detection Using YOLOv8
Conclusion
Wrap Up
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Deep Dive into Object Detection with YOLO
Wrap Up
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