segment-anything-model

You can generate zero-shot image segmentation masks using prompt-based control with points, boxes, or prior masks. This approach produces object masks across new image domains without task-specific fine-tuning. It leverages SamPredictor or SamAutomaticMaskGenerator to output masks, scores, and quality metrics.

Prompt-driven segmentation works by using lightweight inputs like point prompts, bounding boxes, and iterative refinement with previous masks to segment foreground and background regions. It allows interactive annotation and object extraction pipelines without custom training.

Yes, you need to install segment-anything with compatible PyTorch (version 1.7.0 or higher) and optionally transformers (version 4.30.0 or higher). OpenCV and pycocotools are also required to process inputs and handle mask output formats.

Yes, you can export and run SAM via ONNX for production-friendly deployment. The Skill supports ONNX runtime environments, allowing you to integrate zero-shot segmentation into automated mask generation pipelines and broader workflows.

The best way is using zero-shot segmentation to generate accurate masks for domain imagery like medical or satellite images without annotations. By providing prompt-based controls such as foreground clicks or bounding boxes, you can extract target objects instantly.

Yes, it provides optional extensions for text-prompted segmentation using Grounded SAM and video segmentation using SAM 2. These advanced guides expand the core zero-shot mask generation capabilities to handle multi-modal and sequential frame workflows.