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segment-anything-model

Segment objects in images into masks using point, box, or mask prompts.

Updated May 4, 2026
Search Tags:#computer vision#segmentation#zero-shot#annotation#onnx#image masks
One-click install
npx skills add https://github.com/Supporter09/Face_Anti_Spoofing_Biometric --skill segment-anything-model-supporter09
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: segment-anything-model
Source: https://github.com/Supporter09/Face_Anti_Spoofing_Biometric/tree/main/.claude/skills/segment-anything
Command: npx skills add https://github.com/Supporter09/Face_Anti_Spoofing_Biometric --skill segment-anything-model-supporter09

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Segment Anything Model helps you isolate objects in images without task-specific training, removing the need for manual cutouts and repetitive annotation work.

Core Features & Use Cases

  • Prompt-driven segmentation: Use points, bounding boxes, or prior masks to generate precise object masks.
  • Automatic mask generation: Produce masks for all visible objects in an image for dataset creation or exploratory analysis.
  • Practical workflows: Build annotation tools, object extraction pipelines, medical imaging helpers, or image-processing services that need reliable zero-shot segmentation.

Quick Start

Use this Skill to segment the main object in the image by providing a point, box, or mask prompt.

Frequently Asked Questions about segment-anything-model

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I do zero-shot image segmentation without task-specific training?▼

Automatic mask generation produces masks for all visible objects in an image. You can use this output directly for dataset creation, exploratory analysis, or building larger computer vision annotation workflows.

Does the Segment Anything Model support ONNX or FastAPI for deployment?▼

Image mask generation requires loading the SAM model, encoding your prompts, and computing image embeddings. You can reuse these image embeddings across multiple prompts to maintain efficiency during interactive annotation sessions.

Can I use automatic mask generation for dataset creation in computer vision?▼

Automatic mask generation produces masks for all visible objects in an image. You can use this output directly for dataset creation, exploratory analysis, or building larger computer vision annotation workflows.

How do I use point and box prompts for interactive image annotation?▼

Interactive annotation uses point, box, or mask prompts to guide the segmentation of specific objects in an image. This prompt-driven method allows you to build precise object extraction pipelines and medical imaging helpers.

What is needed to generate image masks using the SAM model?▼

Image mask generation requires loading the SAM model, encoding your prompts, and computing image embeddings. You can reuse these image embeddings across multiple prompts to maintain efficiency during interactive annotation sessions.

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