Note: Speaker information is preliminary and subject to change

This talk is jointly presented by Aneeq and Tom

In this talk, we provide an overview of deep learning-based object detection techniques. Our review begins with the revolution of deep learning algorithms and how they have added value to traditional computer vision based object detection techniques. We explore different deep learning based object detection architectures and frameworks. In addition to this, we also review the computational requirements for deep learning. Moreover, we also look at computer vision datasets for deep learning based object detection. Finally we demonstrate how to build your own custom object detector in real time using python libraries with a few lines of code.

Session takeaways

• understanding how Deep Learning has revolutionised traditional computer vision
• knowledge of understanding requirements and key processes in a Deep Learning based custom object detection framework
• understand how to quickly build and deploy your own custom object detector with few lines of codes and some resources for additional help

Bio

Tom Rooks has a wealth of data management and data engineering experience throughout the public and private sectors. He currently works for Software Solved in a technical consultancy capacity.