segment-anything-model

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

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

1|Updated Jul 31, 2026

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

Ready to run

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

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires segment-anything, transformers, torch, opencv-python, pycocotools, matplotlib, onnxruntime, onnx, 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 any object in any image domain immediately.
Flexible Prompting: Use points, bounding boxes, or existing masks to define the target object.
Use Case: Quickly generate high-quality training data for other vision models or build interactive annotation tools for medical or satellite imagery.

Quick Start

Use the segment-anything-model skill to generate a mask for the object located at coordinates 500, 375 in the image file image.jpg.

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 using pre-trained ViT-based architectures without fine-tuning. You provide point, bounding box, or mask prompts to generate high-quality masks for any object in an image domain immediately.

Can I use point and box prompts to generate object masks in OpenCV?▼

Yes, you can process point and box prompts to generate object masks. The skill integrates OpenCV and pre-trained ViT models to isolate specific objects within visual data based on your provided coordinates or boundaries.

What is the best way to generate training data for computer vision models?▼

Generating training data for computer vision models is best achieved by using zero-shot segmentation to automatically isolate objects. This approach rapidly produces high-quality masks for medical or satellite imagery without manual annotation.

Does the Segment Anything Model support ONNX deployment pipelines?▼

The Segment Anything Model supports ONNX-compatible deployment pipelines using onnxruntime. This allows you to export and run the pre-trained ViT-based segmentation architecture in optimized production environments.

How do I segment objects in medical or satellite imagery using PyTorch?▼

To segment objects in medical or satellite imagery using PyTorch, apply zero-shot segmentation with flexible prompting. You can immediately isolate domain-specific objects by passing point or box prompts to the model.

Do I need pycocotools to export generated masks from image segmentation?▼

You need pycocotools to handle and export generated masks in standard annotation formats. It integrates with the segmentation pipeline to manage the mask outputs for downstream training data generation.