Image segmentation is a fundamental task in computer vision that involves partitioning an image into multiple segments or objects, allowing machines to understand and analyze the content of images at a pixel level. The process simplifies the representation of an image into something more meaningful and easier to analyze. Image segmentation can be semantic or instance-based, producing labeled images or binary masks for each identified object, respectively. Mask generation is particularly useful for tasks that require precise object boundaries, such as image editing or medical image analysis. It has numerous applications across various industries, including medical imaging, autonomous vehicles, satellite imagery, and more. The top image segmentation model is Meta's Segment Anything Model 2 (SAM2), which excels in tasks requiring flexible, user-guided segmentation and can be used with language prompts for segment description.