segment-anything-model

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

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

1|Updated May 18, 2026

Search Tags:#computer vision #sam #image segmentation #mask generation #onnx deployment #interactive annotation #segment anything

Ready to run

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

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you isolate objects in images without training a custom model, making it easier to annotate images, extract foregrounds, and adapt segmentation workflows to new domains quickly.

Core Features & Use Cases

Interactive segmentation: Use points, boxes, or previous masks to refine object boundaries.
Automatic mask generation: Produce masks for all visible objects in an image for labeling and dataset creation.
Production deployment: Apply SAM in applications such as medical imaging, satellite analysis, annotation tools, and API services.
Model guidance: Compare SAM variants, choose the right prompt strategy, and troubleshoot common inference or setup issues.

Quick Start

Ask the assistant to segment the main object in your attached image and return the best mask, bounding box, and confidence score.

Frequently Asked Questions about segment-anything-model

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I segment objects in an image without task-specific training?▼

Zero-shot image segmentation isolates objects without task-specific training by using point, box, or mask prompts to identify boundaries. This approach adapts quickly to new domains, allowing you to extract foregrounds and refine boundaries interactively.

Can I generate masks for all visible objects in an image automatically?▼

Automatic mask generation produces masks for all visible objects in an image to streamline labeling and dataset creation. It evaluates all potential objects and outputs quality-filtered masks suitable for building large-scale annotation datasets.

How do I use interactive annotation with point and box prompts?▼

Interactive annotation uses point, box, and previous mask prompts to refine object boundaries. By providing spatial coordinates as prompts, you guide the segmentation model to isolate specific regions and iteratively correct the extracted mask.

Does the Segment Anything model support ONNX deployment for production APIs?▼

ONNX deployment supports implementing the Segment Anything model in production APIs and web interfaces. This allows you to integrate mask generation into applications for medical imaging, satellite analysis, and automated annotation tools.

What is the best way to troubleshoot CUDA and checkpoint issues during image segmentation?▼

Troubleshooting CUDA, checkpoint, and coordinate issues during image segmentation involves validating environment configurations and verifying input preprocessing. Correcting these setup and inference errors ensures stable batched inference and accurate mask generation.

Can I use zero-shot segmentation for medical and satellite imaging applications?▼

Zero-shot segmentation applies directly to medical and satellite imaging applications without custom training. By processing specialized image inputs with quality filtering and prompt engineering, it extracts critical features and objects across diverse analytical domains.