Image segmentation, traditionally a labor-intensive and error-prone task, is being revolutionized by Labelbox's Auto-Segment 2.0, which leverages Meta AI's Segment Anything Foundation Model (SAM) to enhance speed and accuracy in generating training data for complex computer vision applications. This tool uses a raster-based rendering system and a combination of pen and brush tools to facilitate precise mask drawing, improving model performance across diverse use cases such as insurance compliance through geospatial data, smart agriculture with crop classification, and medical diagnostics via automatic segmentation. By integrating zero-shot and transfer learning capabilities, Auto-Segment 2.0 not only streamlines AI development but also empowers machine learning teams to tackle complex real-world challenges more effectively. The tool's ability to generate pre-labels in bulk and support model-assisted labeling further accelerates the annotation process, while chaining models like Yolo V8 with SAM enhances segmentation outcomes, demonstrating its potential to transform various industries by improving efficiency and accuracy in computer vision tasks.