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Computer Vision for Embedded Systems

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

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Computer Vision for Embedded Systems

Hay una sesión disponible:

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Comienza el 2 nov
Termina el 12 dic

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 horas por semana
Al ritmo del instructor
Con un cronograma específico
Gratis
Verificación opcional disponible

Hay una sesión disponible:

Una vez finalizada la sesión del curso, será archivadoAbre en una pestaña nueva.
Comienza el 2 nov
Termina el 12 dic

Sobre este curso

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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.

De un vistazo

  • Institución: PurdueX
  • Tema: Ingeniería
  • Nivel: Advanced
  • Prerrequisitos:

    Knowledge of Python and Data Science or similar.

  • Idioma: English
  • Transcripción de video: English

Lo que aprenderás

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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.

Plan de estudios

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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

Testimonios de los estudiantes

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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.

Acerca de los instructores

Preguntas frecuentes

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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.

¿Quién puede hacer este curso?

Lamentablemente, las personas residentes en uno o más de los siguientes países o regiones no podrán registrarse para este curso: Irán, Cuba y la región de Crimea en Ucrania. Si bien edX consiguió licencias de la Oficina de Control de Activos Extranjeros de los EE. UU. (U.S. Office of Foreign Assets Control, OFAC) para ofrecer nuestros cursos a personas en estos países y regiones, las licencias que hemos recibido no son lo suficientemente amplias como para permitirnos dictar este curso en todas las ubicaciones. edX lamenta profundamente que las sanciones estadounidenses impidan que ofrezcamos todos nuestros cursos a cualquier persona, sin importar dónde viva.

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