SAM is a promptable segmentation system that can achieve zero-shot generalization for unfamiliar objects and images without additional training. It utilizes various input prompts to perform a wide range of segmentation tasks without further training. Its promptable design allows for flexible integration with other systems. Trained on 11 million images, it has 1 billion segmentation masks. Its efficient modular design enables inference within milliseconds. The Segment Anything Model (SAM) enables image segmentation based on text commands and others, recognizes all objects and provides one-click image masking. Simply upload an image and click on an object for recognition.