Deep Learning for Object Detection (Coursera)

In the course projects, you will apply detection models to real-world scenarios and train a model to detect various parking signs. Completing this course will give you the skills to train detection models for your application.
By the end of this course, you will be able to:
• Explain how deep learning networks locate and classify objects in images
• Retrain popular YOLO deep learning models for your application
• Use a variety of metrics to evaluate prediction results
• Visualize results to gain insights into model performance
• Improve model performance by adjusting important model parameters
• Analyze labeled images to identify and fix potential shortcomings in your data
For the duration of the course, you will have free access to MATLAB, software used by top employers worldwide. The courses draw on the applications using MATLAB, so you spend less time coding and more time applying deep learning concepts.
This course is part of the Deep Learning for Computer Vision Specialization.

What you'll learn

• Retrain popular YOLO deep learning models for your applications
• Visualize results to gain insights into model performance
• Evaluate detection models by examining both class and location accuracy.
• Analyze labeled images to identify and fix potential data shortcomings

Syllabus

Detecting Objects with Pre-trained Models
Get started with object detection by using pre-trained models

Training Object Detection Models
Use transfer learning to retrain YOLO models for new applications

Evaluating Object Detection Models
Use metrics like recall, precision, and mean average precision to evaluate your models

Final Project: Train and Evaluate a Detection Model
Apply the full object detection workflow on a final project