Segment Anything-Object Segmentation-Prompt-based Segmentation System-Zero-shot Generalization-SAM - Object Segmentation System with Prompting

Segment Anything is an AI model designed by Meta AI to provide a solution for object segmentation in computer vision research. With just a single click, users can segment objects in any image. This model utilizes a promptable segmentation system and zero-shot generalization, allowing for accurate and efficient object segmentation without the need for additional training. The system accepts various input prompts, including interactive points and bounding boxes, and can generate multiple effective masks for ambiguous prompts or complex scenes. The output masks can be used as input for other AI systems, can be used for tracking in videos, image editing applications, elevated to 3D, or for creative tasks. The model is designed to be efficient enough to power data engines, featuring a lightweight, one-shot image encoder and mask decoder that can run at speeds of a few milliseconds per prompt in a web browser. The image encoder requires GPU for efficient inference, while the prompt encoder and mask decoder can run directly in PyTorch or be converted to ONNX for efficient execution on various platforms supporting ONNX runtime on CPU or GPU. The model was trained on the SA-1B dataset, which consists of over 11 million licensed and privacy-protected images, collecting over 1.1 billion segmentation masks.

Segment Anything is an AI model designed to provide a solution for object segmentation in computer vision research. The model utilizes a promptable segmentation system and zero-shot generalization, allowing for accurate and efficient object segmentation without the need for additional training. The system can accept various input prompts, including interactive points and bounding boxes, and can generate multiple effective masks for ambiguous prompts or complex scenes. The output masks can be used as inputs for other AI systems, can be tracked in videos, used for image editing applications, elevated to 3D, or utilized for creative tasks. The model is designed to be sufficiently efficient, powering data engines with a lightweight, one-shot image encoder and mask decoder that can run at speeds of a few milliseconds per prompt in a web browser.