Upgrade and configure YOLO-NAS + v8 models for advanced uses
Implement real-time object detection and segmentation in various environments
Cross-platform training from PC to mobile to web
Description
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.
What you will learn
Run AI models efficiently on various operating systems
Train and fine-tune YOLO-NAS + v8 on custom datasets
Integrate object tracking with modern algorithms and tools
Convert AI models between multiple machine learning frameworks
Develop and deploy Flask web applications integrating YOLO-NAS + v8
Build and configure mobile applications using Kivy
Augmented Startups have over 8 years experience in Printed Circuit Board (PCB) design as well in image processing and embedded control. Author Ritesh Kanjee has completed his Masters Degree in Electronic engineering and published two papers on the IEEE Database with one called "Vision-based adaptive Cruise Control using Pattern Matching" and the other called "A Three-Step Vehicle Detection Framework for Range Estimation Using a Single Camera" (on Google Scholar). His work was implemented in LabVIEW. He works as an embedded electronic engineer in defence research and has experience in FPGA design with programming in both VHDL and Verilog. He also has expertise in augmented reality and machine learning in which he shall be introducing new technologies through the medium of video
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