The article provides an overview of the top 10 open-source Computer Vision (CV) repositories on GitHub, aimed at researchers, practitioners, and enthusiasts seeking to stay updated with the latest advancements in the field. It discusses the format, content, key learnings, and proficiency levels required for each repository, which includes resources like Awesome Computer Vision, Segment Anything Model (SAM), and Visual Instruction Tuning (LLaVA), among others. Each repository offers a unique set of tools, from comprehensive codebases and datasets to tutorials and research papers, designed to enhance understanding and implementation of computer vision techniques. The article also highlights the importance of GitHub as a collaborative platform for developers and its role in providing accessible resources for CV projects. Additionally, it emphasizes various factors for assessing a repository's health, such as activity level, community contribution, documentation quality, release frequency, responsiveness, and popularity, which are crucial for determining the reliability and sustainability of these open-source projects.