Deep Learning for Object Detection
Master object detection using YOLO models and MATLAB, from pre-trained networks to custom model training. Perfect for computer vision applications.
Master object detection using YOLO models and MATLAB, from pre-trained networks to custom model training. Perfect for computer vision applications.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Deep Learning for Computer Vision Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
Instructors:
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What you'll learn
Retrain YOLO deep learning models for custom applications
Visualize and analyze model performance results
Evaluate detection models using class and location accuracy
Analyze labeled images to improve data quality
Implement object detection in real-world scenarios
Skills you'll gain
This course includes:
0.9 Hours PreRecorded video
7 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
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There are 4 modules in this course
Detecting and locating objects is one of the most common uses of deep learning for computer vision. This comprehensive course covers object detection from fundamentals to advanced applications. Students learn to train YOLO models, evaluate detection accuracy, and apply these skills to real-world scenarios like autonomous systems, medical imaging, and agriculture. The course includes hands-on projects using MATLAB, focusing on practical implementation and model optimization.
Detecting Objects with Pre-trained Models
Module 1 · 1 Hours to complete
Training Object Detection Models
Module 2 · 2 Hours to complete
Evaluating Object Detection Models
Module 3 · 1 Hours to complete
Final Project: Train and Evaluate a Detection Model
Module 4 · 2 Hours to complete
Fee Structure
Instructors
Manager Online Courses
Brandon Armstrong is a Principal Online Content Developer at MathWorks. He earned a Ph.D. in physics from the University of California at Santa Barbara in 2010.
Online Course Developer at MathWorks
Amanda Wang is an Online Course Developer at MathWorks, specializing in creating educational content related to MATLAB and its applications in computer vision and deep learning. She holds dual Bachelor's degrees in Mathematics with Computer Science and Business Analytics from the Massachusetts Institute of Technology (MIT), which she completed in 2020. Currently, Amanda is pursuing a Master’s degree in Computer Science from the University of Illinois Urbana-Champaign.
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