segment-anything-model

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

Segment images using SAM with point, box, and mask prompts.

Updated May 4, 2026

Search Tags:#prompting #zero-shot #deep-learning #computer-vision #image-segmentation #segment-anything

Ready to run

npx skills add https://github.com/Plaidmustache/hermes-nulab --skill segment-anything-model-plaidmustache

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Segment images using Meta's Segment Anything Model (SAM) to obtain accurate object masks with minimal task-specific training.

Core Features & Use Cases

Zero-shot segmentation across diverse image domains using prompts (points, boxes, or masks).
Interactive annotation and data labeling workflows for AI training data.
Application across products, research, medical imaging, and satellite imagery where rapid segmentation is required.

Quick Start

Install the required dependencies and run a sample 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 without task-specific training?▼

Zero-shot image segmentation generates accurate object masks using points, boxes, or masks as prompts. Meta's Segment Anything Model enables this across diverse domains without requiring task-specific training.

Can I use point and box prompts for interactive image annotation?▼

Yes, interactive image annotation supports point, box, and mask prompts to generate segmentation boundaries. This enables rapid object extraction and labeling workflows for AI training data across various domains.

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

Yes, running SAM for image segmentation requires the segment-anything package with transformers>=4.30.0 and torch>=1.7.0 installed. These dependencies provide the deep-learning environment needed for mask generation.

Does zero-shot segmentation work for medical imaging and satellite imagery?▼

Zero-shot segmentation applies across medical imaging and satellite imagery domains. SAM processes these multimodal tasks using point, box, or mask prompts to obtain accurate object masks without domain-specific retraining.

What is the best way to extract objects from images for computer vision datasets?▼

Using zero-shot prompts with SAM is an efficient way to extract objects from images for computer vision datasets. It generates accurate segmentation masks for interactive annotation, reducing manual data labeling effort.