segment-anything-model

Zero-shot image segmentation works on any image domain by accepting point, box, or mask prompts to segment objects without task-specific training. It automatically generates object masks without requiring fine-tuning.

Automatic segmentation can generate all object masks in an image without manual prompts. This capability is useful for generating training data or handling domain-specific image processing tasks comprehensively.

Zero-shot segmentation requires torch>=1.7.0 and transformers>=4.30.0 to function. These dependencies provide the necessary PyTorch and Hugging Face transformers environments for running the segmentation models.

The best way to segment specific objects is by providing point or box coordinates as prompts via the command line interface. This allows precise targeting of objects within images for accurate boundary detection.

ONNX export is supported for deploying the segmentation model in browsers and edge devices. This enables efficient inference and integration into lightweight or resource-constrained computer vision pipelines.

Automatic mask generation should be used when you need to identify all objects in an image without specific target coordinates, such as for panoptic segmentation. Point prompts are better for isolating single, specific objects.