segment-anything-model

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

Updated Apr 23, 2026

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

Ready to run

npx skills add https://github.com/Chris-Chai-Minjae/hermes-agent-r1-bridge --skill segment-anything-model-chris-chai-minjae

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: segment-anything-model
Source: https://github.com/Chris-Chai-Minjae/hermes-agent-r1-bridge/tree/main/skills/mlops/models/segment-anything
Command: npx skills add https://github.com/Chris-Chai-Minjae/hermes-agent-r1-bridge --skill segment-anything-model-chris-chai-minjae

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Segment Anything Model (SAM) enables zero-shot segmentation of any object in images, reducing manual annotation effort and enabling rapid dataset creation.

Core Features & Use Cases

Zero-shot segmentation across diverse domains with no task-specific training.
Interactive prompts via points, boxes, or previous masks; or automatic mask generation for all objects.
Supports multiple model variants and export in ONNX for deployment.

Use cases include annotation tools, dataset labeling, medical and satellite imagery workflows, and rapid prototyping across domains.

Quick Start

Install the Segment Anything Model, load a pretrained checkpoint, and segment objects with point or box prompts or enable automatic mask generation.

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 on any object?▼

Zero-shot image segmentation isolates objects without task-specific training by accepting point, box, or mask prompts. You load a pretrained checkpoint and pass prompts to generate accurate masks instantly across diverse domains.

Can I automatically generate masks for all objects in an image without prompts?▼

Automatic mask generation segments every object in an image without manual prompts. The SAM architecture processes the entire image through its encoder and decoder to identify and extract all distinct object masks automatically.

Does zero-shot segmentation with SAM require specific PyTorch or transformers versions?▼

Zero-shot segmentation with SAM requires torch>=1.7.0 and transformers>=4.30.0, plus the segment-anything package. Ensure these dependencies are installed in your Python environment before loading model checkpoints.

What is the best way to build an image annotation tool using computer vision models?▼

The best way to build an annotation tool is using zero-shot segmentation to reduce manual labeling effort. SAM supports interactive point and box prompts, enabling rapid dataset creation across medical, satellite, and general imagery domains.

Can I export the SAM image segmentation model to ONNX for deployment?▼

You can export the SAM image segmentation model to ONNX format for deployment. This allows the zero-shot segmentation workflow to run efficiently in production environments outside the original PyTorch framework.