segment-anything-model

Name: segment-anything-model
Availability: InStock
Author: Automate

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

1|Updated Apr 24, 2026

Search Tags:#computer vision #SAM #zero-shot #image segmentation #mask generation #object annotation

Ready to run

npx skills add https://github.com/automatedigital/spark --skill segment-anything-model-automatedigital

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: segment-anything-model
Source: https://github.com/automatedigital/spark/tree/main/skills/mlops/models/segment-anything
Command: npx skills add https://github.com/automatedigital/spark --skill segment-anything-model-automatedigital

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill eliminates the need for time-consuming, expensive task-specific model training to segment objects in images, which fails for new or niche domains like medical scans, satellite imagery, or custom object categories.

Core Features & Use Cases

Zero-shot segmentation: Segment any object in any image without fine-tuning the foundation model.
Flexible prompt support: Guide segmentation with point clicks, bounding boxes, or previous masks for precise control.
Use case: A computer vision researcher can use this Skill to automatically generate high-quality segmentation masks for 1000 unlabeled cell microscopy images to train a downstream disease detection model, cutting annotation time from weeks to hours.

Quick Start

Use the segment-anything-model skill to generate segmentation masks for all objects in the attached image 'field_survey.jpg' and return the mask data with quality and stability scores.

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?▼

You can guide mask generation prompts using interactive point clicks, bounding boxes, or previous masks for precise control over the segmentation output. This flexible prompt support allows you to refine segmentation results for automated dataset generation and object extraction pipelines.

What is the best way to automate dataset generation for unlabeled images?▼

Automated dataset generation is best handled by applying zero-shot image segmentation to automatically generate high-quality masks for unlabeled images. This cuts annotation time from weeks to hours, providing training data for downstream models without task-specific fine-tuning.

Can I use zero-shot segmentation for medical image analysis and satellite imagery?▼

Zero-shot segmentation supports medical image analysis and satellite imagery processing by segmenting arbitrary objects without task-specific model training. It applies directly to interactive annotation tools and object extraction pipelines for these niche domains.

Does this image segmentation model support ONNX export for edge deployment?▼

ONNX export is supported for edge deployment, allowing you to transition zero-shot mask generation to resource-constrained environments. Multiple model size options are also available to manage the speed-accuracy tradeoff required for your specific deployment context.

When should I not use zero-shot image segmentation?▼

Zero-shot image segmentation may not be suitable when your deployment environment cannot support foundation model sizes, even with ONNX export, or when task-specific accuracy requirements exceed what zero-shot mask generation can achieve without fine-tuning.