Image and Video Segmentation Using Yolo-Nas and Segment Anything Model (Sam): Machine Learning

Abstract

In this article, we evaluate the effectiveness of computer vision and machine learning, which are revolutionizing photography and video studies, ushering in an unprecedented era of creativity and informed consent for study. Despite many advances in the field, object recognition and classification have become technologies with many applications, from advanced analytics to enhancing driving safety for augmented reality. This research uses two advanced models, YOLO-NAS (You Just Look at Neural Architecture Search) and Segment One (SAM), to analyze the evolution of image and video segmentation. In real-time search of products and components, YOLO-NAS and SAM models have established a good relationship and are expected to become a unified system for image and video segmentation in digital transformation. Key Words: YOLO-NAS, Convolutional Neural Networks (Cnns), Object Recognition, Computer Vision, Spatial Attention Module (SAM), Instance Video Segmentation, And Image Segmentation.