Bad Data, Bad Network, or: How to Create the Right Dataset for Your Application

Tuesday, May 22, 4:50 PM - 5:20 PM
Summit Track: 
Mission City Ballroom M1-M3

When training deep neural networks, having the right training data is key. In this talk, I will explore what makes a successful training dataset, common pitfalls and how to set realistic expectations. I’ll illustrate these ideas using several object classification models that have won the annual ImageNet challenge. By analyzing accurate and inaccurate classification examples (some humorous and some amazingly accurate) you will gain intuition on the workings of neural networks. My results are based on my personal dataset of over 10,000 hand-labeled images from around the world.


Mike Schmit

Director of Software Engineering, AMD

Mike is the Director of Software Engineering for computer vision and machine learning at AMD. Mike has been immersed in code optimization for many years. He was the chief software architect for the first 8086 to control experiments in the Space Shuttle, authored the formative book on optimizing code for the Pentium and developed and managed the team that built the first software DVD player. Shortly after that he joined ATI and managed the software video codec team, for many years, which eventually began working on computer vision optimizations and then the OpenVX computer vision standard. Mike has given many industry talks on his team’s optimizations for massively parallel GPUs including recent talks on 360 Video stitching at VRLA, SVVR and Oculus OC3.

See you at the Summit! May 20-23 in Santa Clara, California!
Register today and reserve your hotel room!