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 Online-Ressource
Titel:YOLO-NAS + v8 full-stack computer vision course
Mitwirkende:Kanjee, Ritesh [MitwirkendeR]   i
Institutionen:Augmented AI (Firm), [MitwirkendeR]   i
 Packt Publishing, [Verlag]   i
Ausgabe:[First edition].
Verlagsort:[Birmingham, United Kingdom]
Verlag:Packt Publishing
E-Jahr:2024
Jahr:[2024]
Umfang:1 online resource (1 video file (8 hr., 19 min.))
Illustrationen:sound, color.
Fussnoten:Online resource; title from title details screen (O’Reilly, viewed May 29, 2024)
ISBN:978-1-83620-987-4
 1-83620-987-8
Abstract:This course offers an in-depth journey into the world of YOLO-NAS + v8, starting with upgrading from YOLOv8 to YOLO-NAS and an introduction to their architectures. You will explore the detailed nuances of running these models on different operating systems like Ubuntu and Windows, and even on Google Colab for accessible cloud-based learning. The course meticulously guides you through training custom YOLO-NAS + v8 on specialized datasets, including waste sorting, and extends to mastering object tracking with integrations like DeepSORT and Bytetrack. A significant portion is dedicated to practical implementations, such as setting up Flask applications for AI, mobile development with Kivy, and leveraging mobile apps for real-time uses like sign language recognition. From configuring environments for model conversion across various frameworks to deploying advanced object segmentation techniques, this course prepares you for real-world AI applications. It culminates in deploying YOLO-NAS in Docker and Jetson NANO environments, ensuring you are equipped to handle professional AI tasks seamlessly.
URL:Aggregator: https://learning.oreilly.com/library/view/-/9781836209874/?ar
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Vision par ordinateur
 Intelligence artificielle
 artificial intelligence
 Instructional films
 Nonfiction films
 Internet videos
 Films de formation
 Films autres que de fiction
 Vidéos sur Internet
K10plus-PPN:1892759594
 
 
Lokale URL UB: Zum Volltext
 
 Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
 Klinikum MA Bestellen/Vormerken für Benutzer des Klinikums Mannheim
Eigene Kennung erforderlich
Bibliothek/Idn:UW / m4544437075
Lokale URL Inst.: Zum Volltext

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