segment-anything-model

Zero-shot image segmentation isolates unfamiliar objects by using point, bounding box, or mask prompts to generate precise masks without requiring task-specific training. This removes manual effort and accelerates annotation in new image domains.

Automatic mask generation produces masks for all visible objects in an image to accelerate annotation and dataset creation. This allows you to extract multiple objects quickly without manually prompting each target individually.

ONNX deployment supports interactive annotation and edge production workflows by enabling efficient production use of mask generation. This allows prompt-driven vision workflows to run effectively in deployed environments.

Segmenting objects in medical imaging without training data is best achieved through zero-shot mask generation using point, box, or prior mask prompts. This approach applies prompt-driven vision workflows to extract precise object masks instantly.

Refining masks generated from a single prompt involves multi-mask scoring and prompt refinement to isolate the selected object accurately. This ensures you return the best mask based on your interactive point or box inputs.