segment-anything-model

Zero-shot image segmentation segments any object within an image without task-specific training by using point, box, or mask prompts to guide the model and automatic mask generation to identify all objects.

Yes, bounding boxes can be used as flexible prompts to guide image segmentation, allowing you to target specific objects within an image alongside point prompts or automatic mask generation.

Automatic mask generation identifies and masks all objects in an image, making it suitable for traffic analysis applications to automatically segment cars and pedestrians in street-view images.

Yes, performing zero-shot image segmentation requires the segment-anything library and compatible model checkpoints to successfully execute object detection and segmentation pipelines.

Yes, zero-shot image segmentation workflows support ONNX and onnxruntime dependencies, enabling you to export and run model inference in optimized deployment environments.

Automatic mask generation identifies all objects in an image but may require manual point or box prompts for precise segmentation, limiting its accuracy in complex object detection pipelines without guided intervention.