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Computer Vision for Embedded Systems

Learn about constraints and reducing resource requirements for computer vision on embedded systems.

...
Computer Vision for Embedded Systems

There is one session available:

13 already enrolled!
After a course session ends, it will be archivedOpens in a new tab.
Starts Nov 2
Ends Dec 12

Computer Vision for Embedded Systems

Learn about constraints and reducing resource requirements for computer vision on embedded systems.

Computer Vision for Embedded Systems
Estimated 5 weeks
7–8 hours per week
Instructor-paced
Instructor-led on a course schedule
Free
Optional upgrade available

There is one session available:

After a course session ends, it will be archivedOpens in a new tab.
Starts Nov 2
Ends Dec 12

About this course

Skip About this course

This course provides an overview of running computer vision (OpenCV and PyTorch) on embedded systems (such as Raspberry Pi and Jetson). The course emphasizes the resource constraints imposed by embedded systems and examines methods (such as quantization and pruning) to reduce resource requirements. This course will have programming assignments and projects proposed by the students.

Required texts or technologies:

This course does not have a required text. The course will read recently published papers. Students will use Google Colab for programming assignments.

At a glance

  • Institution: PurdueX
  • Subject: Engineering
  • Level: Advanced
  • Prerequisites:

    Knowledge of Python and Data Science or similar.

  • Language: English
  • Video Transcript: English

What you'll learn

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i. Use computer vision to analyze images.

ii. List the constraints of embedded systems.

iii. Explore design space of computer vision.

iv. Evaluate different methods for accuracy/time tradeoffs.

Lecture topics:

  • Overview, image data formats, OpenCV
  • Edge detection and segmentation
  • Applications of computer vision in embedded systems
  • Datasets, bias, privacy, competitions
  • Machine learning and PyTorch
  • Performance and resources (time, memory, accuracy)
  • Object detection and motion tracking
  • Data annotation and generation
  • Quantization
  • Pruning and network architecture search
  • Tree modular networks
  • Vision in context, MobileNet
  • Real-time scheduling

Learner testimonials

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Fall 2021 course feedback:

  • The organization of the content is superb.
  • It was a very innovative class. It was refreshing that this class was focused on learning, rather than only testing the students.
  • I think the concepts are delivered very well.
  • I enjoyed having the exposure to quantization.
  • The project structure was great in my opinion.

Spring 2022 course feedback:

(Dr. Lu gave the short course at the Seoul National University in South Korea)

  • Instruction for the assignments is clear and explicit, and these assignments help me to fully understand the contents learned in the lecture.
  • Lectures were very well done.
  • I can feel the professor prepared the lecture well.
  • Good quiz, good lecture, good lecturer
  • Everything was perfect.

About the instructors

Frequently Asked Questions

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Q: Does this course focus on theory or practice?

A: The emphasis is using machine learning, not about deriving equations for the theory of machine learning. For example, we will use the tools in PyTorch.

Who can take this course?

Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.

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