segment-anything-model

Image segmentation masks are generated by providing point, box, or prior mask prompts to guide prediction, returning precise binary masks for the targeted objects. You can iteratively refine these results by adding foreground and background points.

Zero-shot segmentation isolates objects in new image domains without task-specific training, making it ideal for automating image annotation and dataset creation. It is needed when extracting objects from photos or preparing training data for vision models.

ONNX export is supported for deploying image segmentation models, allowing integration into production APIs or user interfaces. This enables batch processing and scalable mask generation workflows directly within your existing application infrastructure.

Zero-shot segmentation applies to medical and satellite imaging datasets by isolating structures without domain-specific training. It processes single images to discover all objects automatically, supporting preprocessing workflows for specialized downstream vision models.

Automatic mask generation discovers all objects in an image simultaneously, producing binary masks for annotation and dataset creation. The resulting masks can be post-processed for quality refinement and exported for downstream training pipelines.

Refine inaccurate mask predictions during interactive segmentation by supplying additional foreground and background points or bounding boxes. This iterative prompt control updates the mask boundaries, while post-processing steps handle final quality and refinement.