segment-anything-model

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

Segment objects in images using zero-shot prompts with the Segment Anything Model.

2|2|Updated Apr 16, 2026

Search Tags:#multimodal #prompts #zero-shot #computer-vision #image-segmentation #segment-anything

Ready to run

npx skills add https://github.com/huidge/hermes-skills --skill segment-anything-model-huidge

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Segmenting objects in images quickly and without task-specific training is essential for labeling, annotation, and downstream computer-vision tasks.

Core Features & Use Cases

Zero-shot segmentation using prompts (points, boxes, or masks)
Support for multiple model sizes (ViT-B/L/H) and ONNX export
Use cases include annotation tools, data labeling pipelines, and research prototyping

Quick Start

Provide a point or box prompt to the SamPredictor on an image to obtain 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 do zero-shot image segmentation without task-specific training?▼

Zero-shot image segmentation uses prompts like points, boxes, or masks to segment any object without task-specific training. You provide a prompt to the predictor on an image to obtain segmentation masks instantly.

What is the Segment Anything Model and how does it handle object annotation?▼

The Segment Anything Model (SAM) performs zero-shot segmentation using prompts to automatically mask objects. It handles annotation by accepting point or box inputs to generate precise segmentation masks across photography, medical, and satellite domains.

Does zero-shot image segmentation work with PyTorch and Transformers?▼

Zero-shot image segmentation requires PyTorch (torch>=1.7.0) and Transformers (transformers>=4.30.0). It supports multiple model sizes (ViT-B/L/H) and ONNX export for deployment flexibility.

Can I use point and box prompts to segment objects in medical imaging?▼

Yes, point and box prompts can segment objects in medical imaging. The predictor accepts these prompt types to generate precise masks, applicable across annotation tools, data labeling pipelines, and research prototyping.

What are the limitations of zero-shot segmentation for data labeling pipelines?▼

Zero-shot segmentation limitations include dependency on prompt precision for accurate masks and computational requirements for larger model sizes. It is best suited for prototyping and annotation rather than real-time inference without ONNX export.

What's the best way to export a Segment Anything Model for computer vision pipelines?▼

The best way to export the Segment Anything Model for computer vision pipelines is using its ONNX export support. This allows deploying zero-shot segmentation masks generated from point or box prompts into production environments.