segment-anything-model

Zero-shot image segmentation uses prompts like points, boxes, or masks to segment any object without task-specific training. You provide a prompt to the predictor on an image to obtain segmentation masks instantly.

The Segment Anything Model (SAM) performs zero-shot segmentation using prompts to automatically mask objects. It handles annotation by accepting point or box inputs to generate precise segmentation masks across photography, medical, and satellite domains.

Zero-shot image segmentation requires PyTorch (torch>=1.7.0) and Transformers (transformers>=4.30.0). It supports multiple model sizes (ViT-B/L/H) and ONNX export for deployment flexibility.

Yes, point and box prompts can segment objects in medical imaging. The predictor accepts these prompt types to generate precise masks, applicable across annotation tools, data labeling pipelines, and research prototyping.

Zero-shot segmentation limitations include dependency on prompt precision for accurate masks and computational requirements for larger model sizes. It is best suited for prototyping and annotation rather than real-time inference without ONNX export.

The best way to export the Segment Anything Model for computer vision pipelines is using its ONNX export support. This allows deploying zero-shot segmentation masks generated from point or box prompts into production environments.