segment-anything-model

Generate zero-shot segmentation masks for objects in images using prompts.

Updated Mar 18, 2026

Search Tags:#zero-shot #prompt-based #computer-vision #image-segmentation #segment-anything #object-masking

Ready to run

npx skills add https://github.com/tadod12/fraud-detection-research --skill segment-anything-model-tadod12

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: segment-anything-model
Source: https://github.com/tadod12/fraud-detection-research/tree/main/.agent/skills/18-multimodal/segment-anything
Command: npx skills add https://github.com/tadod12/fraud-detection-research --skill segment-anything-model-tadod12

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Segment any object in images without task-specific training by generating high-quality masks using prompts (points, boxes, or masks) or automatically across large sets of images.

Core Features & Use Cases

Zero-shot segmentation for arbitrary objects with flexible prompts (points, boxes, or masks).
Automatic mask generation and export options for dataset creation and annotation tooling.
Multiple model sizes and easy integration with the SAM ecosystem for research and production workflows.

Quick Start

Install the package, load an image, and use SamPredictor to generate segmentation masks.

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 on arbitrary objects without task-specific training?▼

Zero-shot image segmentation is achieved by generating high-quality masks using flexible prompts like points, boxes, or existing masks, eliminating the need for task-specific training. You can use the SamPredictor to load an image and generate segmentation masks directly.

Can I generate image segmentation masks automatically across large sets of images?▼

Automatic mask generation is supported for large sets of images without requiring manual prompts. This enables efficient dataset creation and annotation tooling by automatically generating and exporting segmentation masks across entire image collections.

Does zero-shot segmentation work with medical or satellite imagery?▼

Zero-shot segmentation is applicable to cross-domain segmentation tasks including medical and satellite imagery. The model generates masks for any object in an image regardless of domain, making it suitable for specialized imaging applications without domain-specific training.

What dependencies do I need to run the Segment Anything Model for image segmentation?▼

Running the Segment Anything Model requires the segment-anything package, transformers version 4.30.0 or higher, and torch version 1.7.0 or higher. These dependencies enable the zero-shot segmentation functionality and support multiple model sizes with optional ONNX export.

What types of prompts can I use for prompt-based object masking?▼

Prompt-based object masking accepts points, boxes, or existing masks as input prompts. This flexibility allows you to interactively guide the segmentation process by specifying regions of interest through different prompt types to generate accurate object masks.