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segment-anything-model

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

1|Updated Mar 22, 2026
Search Tags:#prompts#zero-shot#sam#mask-generation#image-segmentation#segment-anything#points-and-boxes
One-click install
npx skills add https://github.com/nelohenriq/hermes-agent-plus --skill segment-anything-model-nelohenriq
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: segment-anything-model
Source: https://github.com/nelohenriq/hermes-agent-plus/tree/main/skills/mlops/models/segment-anything
Command: npx skills add https://github.com/nelohenriq/hermes-agent-plus --skill segment-anything-model-nelohenriq

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires segment-anything, transformers>=4.30.0, torch>=1.7.0, and includes references (resource) components.

What problem does it solve?

Segment objects in images quickly and without task-specific retraining.

Core Features & Use Cases

  • Interactive point/box prompting for precise segmentation on diverse image domains.
  • Automatic mask generation to extract all object masks in an image for dataset creation.
  • Flexible integration into vision pipelines and annotation tools across research, healthcare, satellite imagery, and general photography.

Quick Start

Load a SAM-compatible model and run segmentation on an image to obtain object masks using prompts or automatic generation.

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 retraining a model?▼

Zero-shot image segmentation can be performed using the Segment Anything Model (SAM) to extract object masks from images via interactive prompts or automatic generation, entirely avoiding task-specific retraining.

Can I use points and bounding boxes to guide mask generation in SAM?▼

Yes, SAM supports interactive point and bounding box prompting to guide precise mask generation, allowing you to define target objects spatially within diverse image domains like medical or satellite imagery.

How do I automatically generate masks for all objects in an image?▼

Automatic mask generation in SAM extracts all object masks from an image simultaneously, providing a fast method for dataset creation and data labeling pipelines without requiring manual prompt inputs.

Do I need PyTorch and Transformers to run segment anything model pipelines?▼

Yes, running SAM requires PyTorch version 1.7.0 or higher and Transformers version 4.30.0 or higher, along with the segment-anything library to execute the segmentation logic.

What is the best way to integrate image segmentation into data labeling pipelines?▼

Integrating SAM into data labeling pipelines provides flexible zero-shot segmentation capabilities, enabling rapid annotation tool workflows for research, healthcare, and general computer vision tasks.

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