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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.
4.9
41 Lessons
3 Projects
10h
Join 2.9 million developers at
Join 2.9 million developers at
LEARNING OBJECTIVES
- 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
Learning Roadmap
1.
Introduction to Object Detection
Introduction to Object Detection
Get familiar with foundational and practical knowledge of YOLO object detection models.
2.
Fundamentals for Understanding YOLO
Fundamentals for Understanding YOLO
Unpack the core of CNNs, IoU, NMS, anchor boxes, bounding boxes, overfitting, and optimization.
Basics of the Convolutional Neural Network (CNN): Part IBasics of Convolutional Neural Network (CNN: Part IIUnderstanding IoU (Intersection over Union)/Jaccard IndexUnderstanding NMS (Non-Maximum Suppression)Understanding Anchor Boxes: Part IUnderstanding Anchor Boxes: Part IIBounding Box PredictionsA Summary of How YOLO Makes PredictionsWhat Is Overfitting in Object Detection?Batch Normalization (BN)Optimizers in YOLOUnderstanding Learning Rate (LR) SchedulersmAP Scores as Performance MetricsUnderstanding Loss CalculationExercise: Build Your Own CNNQuiz
3.
YOLOv7 Architecture
YOLOv7 Architecture
11 Lessons
11 Lessons
Explore YOLOv7's advanced architecture, feature handling, evolution, and neural network optimizations.
4.
Improving Model Performance: Handling Overfitting/Underfitting
Improving Model Performance: Handling Overfitting/Underfitting
4 Lessons
4 Lessons
Grasp methods to enhance model robustness, reduce overfitting, and effectively handle varied data inputs.
5.
Pre-Trained Models, Fine-Tuning, and Hyperparameters in OD
Pre-Trained Models, Fine-Tuning, and Hyperparameters in OD
4 Lessons
4 Lessons
Take a closer look at fine-tuning, pretrained models, and hyperparameters in YOLO.
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Developed by MAANG Engineers
ABOUT THIS COURSE
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.
ABOUT THE AUTHOR
Akanksha Nagar
Senior Machine Learning Engineer with over 8 years of hands-on experience
Trusted by 2.9 million developers working at companies
A
Anthony Walker
@_webarchitect_
E
Evan Dunbar
ML Engineer
S
Software Developer
Carlos Matias La Borde
S
Souvik Kundu
Front-end Developer
V
Vinay Krishnaiah
Software Developer
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