In this module, we will guide you through the complete process of training YOLO-NAS + v8, from understanding the training workflow to working with custom datasets. You will learn key techniques like data pre-processing and augmentation, explore real-world applications like waste sorting detection, and master the use of Roboflow for streamlining model training, testing, and deployment. By the end of this module, you’ll be equipped to handle custom projects with greater ease and efficiency.

In this module, we will introduce you to Multi-Object Tracking (MOT) and how it can be enhanced by integrating YOLO-NAS + v8 with tracking algorithms like DeepSORT, Bytetrack, and FairMOT. You will also learn how to apply YOLO-NAS + v8 to track objects in challenging environments and create a real-time tracking and analytics dashboard using Streamlit. By the end of this module, you’ll have the skills to implement robust object tracking solutions in various real-world applications.

In this module, we will guide you through the essential steps of converting PyTorch models to various formats, such as CoreML, OpenVino, TensorFlow, and TensorRT. You will learn how to prepare the environment for model conversion and understand the specific requirements for deploying models in different ecosystems. By the end of this module, you’ll be proficient in converting models for optimized performance across various hardware platforms.

In this module, we will take you through the process of setting up a Flask application and integrating the YOLO-NAS + v8 model to create web-based AI solutions. You will also learn how to design intuitive front-end interfaces that allow users to interact with your AI models seamlessly. By the end of this module, you'll be equipped to build full-stack AI applications using Flask.

In this module, we will explore various practical applications of YOLO-NAS + v8 within Flask apps. From generating retail heat maps to ensuring safety compliance in mining and detecting environmental hazards like plastic waste and smoke, you’ll learn how to leverage Flask to build real-time, AI-driven solutions. Additionally, you'll dive into the creative world of gaming by developing a CS-GO aimbot with YOLO-NAS + v8. By the end of this module, you’ll have a strong foundation in deploying YOLO-NAS + v8 in real-world applications across different industries.

In this module, we will guide you through the complete process of mobile app development using Kivy, from the initial setup to deploying a fully functional app. You will learn how to integrate the YOLO-NAS + v8 model for mobile object detection, convert it to TensorFlow Lite for optimized performance, and create engaging user interfaces with widgets and buttons. Additionally, we’ll cover important deployment, testing, and debugging steps, ensuring your Kivy app runs smoothly across devices. By the end of this module, you’ll be equipped to build and deploy sophisticated mobile applications with advanced AI capabilities.

In this module, we will guide you through the development of mobile applications with a focus on practical AI-based solutions. You’ll learn how to use real-time video streams for people counting, build a document scanner with Easy-OCR, and explore sign language recognition for enhancing communication. Additionally, we’ll teach you how to integrate bar graph widgets for dynamic data visualization in your apps. By the end of this module, you’ll be equipped to develop powerful mobile applications that leverage computer vision and data visualization for various use cases.

In this module, we will dive into the essentials of object segmentation, teaching you how to set up and optimize models for high performance. You'll explore effective data collection, annotation, and augmentation techniques, along with specialized applications like crack detection. Additionally, you’ll learn how to train segmentation models on GPUs, deploy them for real-time use, and enhance them by integrating YOLO-NAS with SAM segmentation for superior accuracy. By the end of this module, you’ll be equipped to tackle complex segmentation tasks in a variety of industries.

In this module, we will guide you through deploying YOLO-NAS in Docker, ensuring that your AI models run efficiently in isolated and scalable environments. Additionally, you’ll learn how to deploy YOLO-NAS on the Jetson NANO platform, focusing on optimizing performance for edge computing scenarios. By the end of this module, you'll be proficient in setting up and deploying YOLO-NAS across different platforms for real-world AI applications.

In this module, we will showcase exciting bonus content that expands your AI knowledge. You’ll dive into the VegGPT project, exploring the use of GPT models in agriculture, and stay updated on the latest enhancements in YOLOv8, particularly regarding improvements in object bounding box accuracy. This content will equip you with cutting-edge insights to enhance your computer vision applications.