The field of image segmentation is rapidly evolving, driven by advances in computing power and the availability of diverse benchmark datasets, which allow for comprehensive evaluation across various industrial domains. This progress is particularly evident in the use of Vision Transformers (ViT) and few-shot learning methods that enhance segmentation accuracy using state-of-the-art algorithms requiring minimal labeled data. GitHub serves as a vital platform where over 100 million developers contribute to exploring modern segmentation models, showcasing different techniques for complex image segmentation. The primary applications of image segmentation are in autonomous driving and medical imaging, where it plays a crucial role in classifying objects and detecting anomalies. A recent survey of GitHub repositories highlights the importance of factors such as activity level, documentation quality, and community support in assessing a repository's health. Notable repositories include those that focus on referring image segmentation, transformer-based visual segmentation, and models like the Segment Anything Model (SAM), each contributing unique advancements and applications in the field. These repositories illustrate the integration of cutting-edge technologies such as transformer architectures and few-shot learning, heralding a new era for artificial intelligence in computer vision applications.