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segment-anything-model

Segment objects in images using point and box prompts.

2|1|Updated May 10, 2026
Search Tags:#image#deep learning#computer vision#segmentation#zero-shot#annotation
One-click install
npx skills add https://github.com/zli5460/hermes-agent-X-Phoenix-Architecture --skill segment-anything-model-zli5460
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: segment-anything-model
Source: https://github.com/zli5460/hermes-agent-X-Phoenix-Architecture/tree/main/skills/mlops/models/segment-anything
Command: npx skills add https://github.com/zli5460/hermes-agent-X-Phoenix-Architecture --skill segment-anything-model-zli5460

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires transformers, onnxruntime, opencv-python, pycocotools, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill enables automatic and flexible image segmentation without task-specific training, saving time and effort in labeling and annotation processes.

Core Features & Use Cases

  • Zero-shot segmentation: Identifies objects in images without needing custom data.
  • Interactive prompts: Supports point and box inputs for precise segmentation.
  • Builds training data: Assists in generating labeled datasets for machine learning models.
  • Use Case: A researcher wants to segment objects across large image datasets for training a vision classifier efficiently.

Quick Start

Use the segment-anything skill to quickly generate masks for an uploaded image, refining segmentation with point or box prompts.

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 training data?▼

Zero-shot segmentation identifies objects without custom training data by using pretrained vision transformers. You can generate masks automatically and refine them interactively using point or box prompts for precise results.

Can I use interactive prompts like points or boxes for precise image segmentation?▼

Yes, interactive prompts like points and boxes are supported for precise image segmentation. This allows you to refine automated masks and target specific objects within diverse image domains without task-specific training.

Does this image segmentation skill support ONNX export for deployment?▼

Yes, the image segmentation skill ensures compatibility with various deployment environments via ONNX export and model variants. This allows you to run inference using onnxruntime across different platforms.

What's the best way to automate annotation for large computer vision datasets?▼

Automating annotation for large computer vision datasets is best handled through batch processing workflows. This skill processes large image collections using pretrained vision transformers to generate labeled training data efficiently.

Do I need pyccocotools and opencv-python to build training data from segmented images?▼

Yes, pyccocotools and opencv-python are required dependencies for building training data from segmented images. They handle mask formatting and image processing operations alongside pretrained vision transformers.

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