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segment-anything-model

Segment image content into object-level masks using the Segment Anything Model.

Updated Mar 30, 2026
Search Tags:#prompts#zero-shot#annotation#sam#cv#image-segmentation#segment-anything
One-click install
npx skills add https://github.com/attentiondotnet/hermes-agent --skill segment-anything-model-attentiondotnet
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: segment-anything-model
Source: https://github.com/attentiondotnet/hermes-agent/tree/main/skills/mlops/models/segment-anything
Command: npx skills add https://github.com/attentiondotnet/hermes-agent --skill segment-anything-model-attentiondotnet

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Segment Anything Model (SAM) enables zero-shot image segmentation, allowing you to isolate any object in an image without task-specific training.

Core Features & Use Cases

  • Prompt-driven segmentation: extract objects from images using points, boxes, or masks with minimal setup.
  • Interactive annotation and data prep: accelerate labeling, dataset curation, and cross-domain segmentation across diverse datasets.
  • Deployment-friendly: supports multiple model sizes and ONNX export for deployment in various environments.

Quick Start

Install the required libraries and run a minimal example to segment an image using a single point 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 perform zero-shot image segmentation without task-specific labels?▼

Prompt-driven segmentation extracts objects from images using points, boxes, or masks with minimal setup. You provide spatial prompts to the Segment Anything Model to generate corresponding object-level masks without task-specific labels.

Can I use SAM for interactive image annotation and data preparation?▼

Yes, SAM accelerates interactive annotation and data prep by generating object-level masks from minimal prompts. It enables fast labeling and dataset curation across diverse datasets and cross-domain image modalities.

Does the Segment Anything Model support ONNX export for deployment?▼

Yes, the Segment Anything Model supports ONNX export for deployment in various environments. It accommodates multiple model sizes and backends to facilitate flexible production deployment.

Do I need PyTorch and transformers to run SAM for image segmentation?▼

Yes, running SAM for image segmentation requires PyTorch and the transformers library. These dependencies handle the underlying model loading and prompt-based interaction to produce segmentation masks.

What are the limitations of using zero-shot segmentation for complex images?▼

Zero-shot segmentation relies entirely on provided prompts, meaning ambiguous points or boxes can yield inaccurate masks. It requires careful prompt construction and may struggle with highly overlapping objects without precise inputs.

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