segment-anything-model

Zero-shot image segmentation creates accurate masks from point, box, or previous-mask prompts without fine-tuning. You can isolate target objects instantly by providing an image and a prompt to generate the best mask.

Automatic mask generation produces masks for all visible objects to support broad image annotation or dataset creation. It enables batched inference to extract multiple objects from a single image efficiently.

SAM integrates with Hugging Face Transformers, FastAPI, and Gradio to build production deployment services. You can create interactive annotation tools and object extraction pipelines within these frameworks.

ONNX export is supported for deploying SAM models in production workflows. This allows you to troubleshoot integration and build scalable image segmentation services outside of native Python environments.

Interactive annotation with point, box, and mask prompts is fully supported for medical imaging workflows. You can extract target objects and build specialized annotation pipelines without requiring task-specific training data.

Building an object extraction pipeline with SAM involves using point or box prompts to generate masks, combining it with Grounding DINO for text prompts, and deploying via FastAPI or Gradio for interactive services.