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Home > IoT & Hardware > Robotics > YOLO-NAS + v8 Full-Stack Computer Vision Course [Video]
YOLO-NAS + v8 Full-Stack Computer Vision Course [Video]
YOLO-NAS + v8 Full-Stack Computer Vision Course [Video]

YOLO-NAS + v8 Full-Stack Computer Vision Course: Empowering AI Innovations with NVIDIA Jetson [Video]

By Augmented AI , Ritesh Kanjee
€224.99 €8.99
Video May 2024 8 hours 18 minutes 1st Edition
Video
€224.99 €8.99
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Video
€224.99 €8.99
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Key benefits

  • 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

Product Details

Country selected
Publication date : May 17, 2024
Length 8 hours 18 minutes
Edition : 1st Edition
Language : English
ISBN-13 : 9781836209874
Category :
IoT & Hardware
Languages :
Concepts :
Robotics

What do you get with a video?

Product feature icon Download this video in MP4 format
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
Buy Now

Product Details

Publication date : May 17, 2024
Length 8 hours 18 minutes
Edition : 1st Edition
Language : English
ISBN-13 : 9781836209874
Category :
IoT & Hardware
Languages :
Concepts :
Robotics

Table of Contents

11 Chapters
1. YOLO-NAS + v8 Introduction Chevron down icon Chevron up icon
How to Upgrade from YOLOv8 to YOLO-NAS
Lecture 1: Introduction to YOLO-NAS + v8
Lecture 2: Architecture of YOLO-NAS + v8 and comparison to other models
Lecture 3: Running YOLO-NAS + v8 on Ubuntu
Lecture 4: Running YOLO-NAS + v8 on Windows
Lecture 5: Running YOLO-NAS + v8 in Google Colab
Lecture 6: YOLO-NAS + v8 Object Detection on Images, Video & WebCam in Google Colab
Lecture 7: Common use cases for YOLO-NAS + v8
Lecture 8: Fine-tuning pre-trained YOLO-NAS + v8 models
2. Training Custom YOLO-NAS + v8 Chevron down icon Chevron up icon
Lecture 1: Training Process of YOLOv8
Lecture 2: Training YOLO NAS on custom Dataset
Lecture 3: Custom Dataset Waste Sorting Detection
Lecture 4: Data Pre-processing
Lecture 5: Data Augmentation
Lecture 6: Training, Testing, and Deploying Model on Roboflow
Lecture 7: Using Roboflow models for AI data annotation
3. YOLO-NAS + v8 Object Tracking Chevron down icon Chevron up icon
Lecture 1: Introduction to Multi-Object Tracking (MOT)
Lecture 2: YOLO-NAS + v8 with DeepSORT
Lecture 3: YOLO-NAS + v8 with Bytetrack
Lecture 4: YOLO-NAS + v8 with FairMOT
Lecture 5: Integrating YOLO-NAS + v8+ tracking on Streamlit dashboard
4. Model Conversion Chevron down icon Chevron up icon
Lecture 1: Overview of model conversion
Lecture 2: Setting up the environment for model conversion
Lecture 3: Converting PyTorch models to CoreML
Lecture 4: Converting PyTorch models to OpenVino
Lecture 5: Converting PyTorch models to TensorFlow
Lecture 6: Converting PyTorch models to TensorRT
5. Flask Integration Chevron down icon Chevron up icon
Lecture 1: Setting up Flask Application
Lecture 2: Integrate YOLO-NAS + v8 with Flask
Lecture 3: Designing Front-End
6. YOLO-NAS + v8 Flask Apps Chevron down icon Chevron up icon
Lecture 1: Retail Heat Maps
Lecture 2: Mining Safety Check- Helmet/Glasses or vest.
Lecture 3: Plastic Waste Detection using Security Cameras
Lecture 4: Smoke Detection
Lecture 5: CS-GO Gaming Aimbot with YOLOv8 | NAS
7. Mobile Development Using Kivy Chevron down icon Chevron up icon
Lecture 1: Introduction to Mobile Development
Lecture 2: Setting up Kivy Application (part 1)
Lecture 2: Setting up Kivy Application (part 2)
Lecture 3: Setting up a Dashboard
Lecture 4: Integrating YOLO-NAS + v8 with Kivy
Lecture 5: Conversion to TFlite
Lecture 6.1 Widgets and Buttons in Kivy
Lecture 6.2: Widgets and Buttons in Kivy
Lecture 6.3: Widgets and Buttons in Kivy
Lecture 6.4: Widgets and Buttons in Kivy
Lecture 7.1- Deployment and Testing
Lecture 7.2- Modify the User Interface
Lecture 8.8.1: Configuring Buildozer
Lecture 8.8.2: Debugging Application
Lecture 8.8.3 Run Application
Lecture 9.1: Updating the Search Bar
Lecture 9.2: Updating the Slider
Lecture 9.3: Adding a Navigation Drawer
Lecture 9.4: Configuring & Running the App
8. Mobile Apps Chevron down icon Chevron up icon
Lecture 1: Introduction to Mobile Apps
Lecture 2: People Counting over Time
Lecture 3: Document Scanner (using Easy-OCR)
Lecture 4: Sign Language Recognition
Lecture 5: Bar Graph Widget Visualization
9. Object Segmentation Chevron down icon Chevron up icon
Lecture 1: Introduction to Object Segmentation
Lecture 2: Setting up Object Segmentation model
Lecture 3: Data Collection, Annotation and Health checker
Lecture 4: Data Pre-processing and Augmentation
Lecture 5: Training on GPU and deployment
Lecture 6: Crack detection and measuring the pixel size
Lecture 7: YOLO-NAS + SAM Segmentation
10. YOLO-NAS Deployment for Docker Chevron down icon Chevron up icon
YOLO-NAS Deployment for Docker
YOLO-NAS Deployment for Jetson NANO
11. Bonus Content Chevron down icon Chevron up icon
VegGPT
Ultralytics Update: YOLOv8 Object Bounding Boxes

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About the author

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Profile icon Augmented Startups
LinkedIn Github
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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