segment-anything-model

Generate object masks from prompts using the Segment Anything Model.

Updated Mar 16, 2026

Search Tags:#zero-shot #annotation #sam #computer-vision #image-segmentation #object-masks

Ready to run

npx skills add https://github.com/arsity/scholar-tools --skill segment-anything-model-arsity

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: segment-anything-model
Source: https://github.com/arsity/scholar-tools/tree/main/vendor/ai-research-skills/18-multimodal/segment-anything
Command: npx skills add https://github.com/arsity/scholar-tools --skill segment-anything-model-arsity

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Segment Anything Model (SAM) provides a foundation for zero-shot image segmentation, enabling you to obtain accurate object masks with point, box, or mask prompts or through automatic generation without task-specific training.

Core Features & Use Cases

Zero-shot segmentation capable of handling arbitrary objects across diverse domains.
Flexible prompts (points, boxes, or precomputed masks) and automatic mask generation for annotation workflows.
Use cases include interactive annotation tools, data labeling pipelines, medical and satellite imagery analysis, and cross-domain segmentation tasks.

Quick Start

Install the Segment Anything model and run a sample script to generate masks for a test image.

Frequently Asked Questions about segment-anything-model

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

FAQPage Schema

What is zero-shot image segmentation and how does it handle diverse objects?▼

Zero-shot image segmentation generates high-quality object masks for arbitrary objects across diverse domains without requiring task-specific training. You obtain accurate masks using point, box, or mask prompts, or through automatic generation.

How do I generate object masks from prompts using computer vision workflows?▼

You generate object masks by applying the Segment Anything Model to preprocess images and handle point, box, or precomputed mask prompts. This initiates end-to-end segmentation workflows for interactive annotation and data labeling.

Do I need PyTorch and specific transformers to run zero-shot segmentation?▼

Yes, you need PyTorch (torch>=1.7.0) and transformers (transformers>=4.30.0) installed. These dependencies provide the necessary environment for running the Segment Anything Model and handling image preprocessing.

Can I use SAM for medical and satellite imagery analysis without task-specific training?▼

Yes, you can apply SAM to medical and satellite imagery analysis without task-specific training. The model performs cross-domain segmentation across diverse image types using flexible prompts or automatic mask generation.

What's the best way to automate data labeling pipelines for cross-domain segmentation?▼

The best way to automate data labeling pipelines is using automatic mask generation through the Segment Anything Model. This approach bypasses manual prompts, enabling scalable, cross-domain segmentation across diverse image types.