segment-anything-model

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

2|1|Updated Jul 14, 2026

Search Tags:#multimodal #image processing #computer vision #segmentation #zero-shot #sam

Ready to run

npx skills add https://github.com/heysuhas/hermes_cli --skill segment-anything-model-heysuhas

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires segment-anything, transformers, torch, and includes references (resource) components.

What problem does it solve?

This Skill solves the challenge of segmenting objects in images without the need for task-specific training or fine-tuning, enabling rapid object isolation and annotation.

Core Features & Use Cases

Zero-Shot Segmentation: Segment objects in any image domain using point, box, or mask prompts.
Automatic Mask Generation: Automatically detect and segment all objects within an image.
Use Case: Quickly generate high-quality training data for other vision models or build interactive annotation tools for medical and satellite imagery.

Quick Start

Use the segment-anything-model skill to generate masks for the image at path image.jpg using a bounding box prompt defined by coordinates 425, 600, 700, 875.

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 isolates objects in visual data without fine-tuning by processing point, box, or mask prompts to generate masks. It leverages the segment-anything library to automatically detect and segment all objects within an image.

Can I use bounding box prompts to segment objects in medical or satellite imagery?▼

Bounding box prompts can segment objects in medical and satellite imagery by passing coordinates to the model. The Skill processes these box prompts to isolate specific visual structures across diverse domains.

What's the best way to automatically generate masks for all objects in an image?▼

Automatic mask generation detects and segments all objects within an image by running inference without explicit prompts. This approach produces high-quality masks suitable for building interactive annotation tools or generating training data.

Do I need PyTorch and transformers to run SAM inference on CPU or GPU?▼

PyTorch, transformers, and the segment-anything library are required dependencies to execute zero-shot segmentation inference. The Skill supports running inference on either CPU or GPU hardware configurations.

Why use zero-shot segmentation instead of training a custom model for object isolation?▼

Zero-shot segmentation enables rapid object isolation and annotation without task-specific training or fine-tuning. It allows you to quickly generate high-quality training data for other vision models instead of building custom training pipelines.

Does the segment-anything-model support point and mask prompts in addition to bounding boxes?▼

The segment-anything-model supports point, box, and mask prompts to isolate objects within visual data. This multimodal prompt flexibility allows precise interactive segmentation across general computer vision tasks.