Design and Train a YOLOv2 Network in MATLAB

In this video, Neha Goel joins Connell D’Souza how you can design and train a deep neural network in MATLAB®. The example discussed is a You-Only-Look-Once (YOLOv2) neural network. YOLOv2 is a popular real-time object detection algorithm for autonomous systems.

Neha first discusses the architecture of a YOLOv2 network and the different layers and then demonstrates how to assemble the layers in the network and visualize them in MATLAB. She also explains the importance of anchor boxes in a YOLOv2 network.

Neha also covers training options and how they can be manipulated to achieve the best results. The trained model is tested on a test dataset to visually inspect performance before evaluating the network with numerical performance metrics like precision-recall curves.


Resources:

- Deep Learning with MATLAB: https://bit.ly/37APNSf - Anchor Boxes for Object Detection: https://bit.ly/2uHqqiU - Getting Started with YOLO V2: https://bit.ly/2O6pJqx - Download the files used in this video: https://bit.ly/2U4pb8e - Contact the MathWorks student competitions team: roboticsarena@mathworks.com - Visit the MATLAB and Simulink Robotics Arena for additional videos: https://bit.ly/2U4vubN - Request software for your student competition: https://bit.ly/2RxWrmU