segment-anything-model

Zero-shot image segmentation is achieved by generating high-quality masks using flexible prompts like points, boxes, or existing masks, eliminating the need for task-specific training. You can use the SamPredictor to load an image and generate segmentation masks directly.

Automatic mask generation is supported for large sets of images without requiring manual prompts. This enables efficient dataset creation and annotation tooling by automatically generating and exporting segmentation masks across entire image collections.

Zero-shot segmentation is applicable to cross-domain segmentation tasks including medical and satellite imagery. The model generates masks for any object in an image regardless of domain, making it suitable for specialized imaging applications without domain-specific training.

Running the Segment Anything Model requires the segment-anything package, transformers version 4.30.0 or higher, and torch version 1.7.0 or higher. These dependencies enable the zero-shot segmentation functionality and support multiple model sizes with optional ONNX export.

Prompt-based object masking accepts points, boxes, or existing masks as input prompts. This flexibility allows you to interactively guide the segmentation process by specifying regions of interest through different prompt types to generate accurate object masks.