Bookmark and Share

"Bad Data, Bad Network, or: How to Create the Right Dataset for Your Application," a Presentation from AMD

Register or sign in to access the Embedded Vision Academy's free technical training content.

The training materials provided by the Embedded Vision Academy are offered free of charge to everyone. All we ask in return is that you register, and tell us a little about yourself so that we can understand a bit about our audience. As detailed in our Privacy Policy, we will not share your registration information, nor contact you, except with your consent.

Registration is free and takes less than one minute. Click here to register, and get full access to the Embedded Vision Academy's unique technical training content.

If you've already registered, click here to sign in.

See a sample of this page's content below:

Mike Schmit, Director of Software Engineering for computer vision and machine learning at AMD, presents the "Bad Data, Bad Network, or: How to Create the Right Dataset for Your Application" tutorial at the May 2018 Embedded Vision Summit.

When training deep neural networks, having the right training data is key. In this talk, Schmit explores what makes a successful training dataset, common pitfalls and how to set realistic expectations. He illustrates 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. Schmit's results are based on his personal dataset of over 10,000 hand-labeled images from around the world.

Last seen: 21 weeks 2 days ago
Level 1: Prestidigitator
Joined: 2017-07-21
Points: 1

Very interesting speech