Your browser is not optimized for viewing this website.

More information »

Maine Robotics/USM

AI and Computer Vision

in By Program

Using AI and Computer Vision - Portland0


with Ahmad Tafti

Calendar Jun 20, 2022 at 9 am, runs for 1 week

Object Detection; Computer Vision; Self Driving Cars; Artificial Intelligence... if these sound like a cool thing to be learning about and getting real world experience in, then check out this year's offering out of the USM Dubyak Center for Digital Science and Innovation.

In this summer class/camp, you will learn -from scratch- about YOLO (“You Only Look Once”) which is a precise and effective real-time object recognition algorithm. We will discuss an object detection mechanism in practice with several hands-onpractices starting from manual image annotation to programming and implementation to make YOLO up and running. We, together, will explore what YOLO computational vision algorithm is, what is does, and how.

The USM 2022 Summer Class on AI-Powered Object Detection program is structured such that besides attending lectures, the students will be also working in teams on a project assignment. The team projects will be evaluated by a committee of the summer program, and there will be three awards in the end of the summer school.

Half of each day class will be devoted to lectures, with the remaining time used for practical work in teams on solving object detection in digital images.

For a full description, you can check out the PDF document for more info.

Offered free due to donations from the USM Department of Computer Science. 

Open to students currently enrolled in high school (grades 10, 11, and 12).


IF REQUIRED DUE TO STATE OF THE PANDEMIC, THE CAMPS WILL BE ONLINE OR HYBRID THIS SUMMER. If conditions require online or hybrid operations, all camper families will have the opportunity to cancel for a full refund. If camps are offered in person, then normal cancellation policy remains in effect.

Click here to see the FAQ Page

Click here to see the Policies and Forms Page

Full Course

Forgot password?
Staff Log In