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segment-anything-model

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

Updated Mar 2, 2026
Search Tags:#zero-shot#annotation#sam#prompt-based#image-segmentation#segment-anything
One-click install
npx skills add https://github.com/gigagiova/hermes-agent --skill segment-anything-model-gigagiova
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: segment-anything-model
Source: https://github.com/gigagiova/hermes-agent/tree/main/skills/mlops/segment-anything
Command: npx skills add https://github.com/gigagiova/hermes-agent --skill segment-anything-model-gigagiova

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires segment-anything, transformers>=4.30.0, torch>=1.7.0, and includes references (resource) components.

What problem does it solve?

Segment any object in images using prompt-based or automatic segmentation with zero-shot capabilities.

Core Features & Use Cases

  • Zero-shot segmentation: Works on any image domain without fine-tuning
  • Flexible prompts: Points, bounding boxes, or previous masks
  • Automatic segmentation: Generate all object masks automatically
  • Use Case: Annotation pipelines, data labeling, and image editing workflows across domains.

Quick Start

Install the required dependencies and run a sample inference to segment objects in an image using 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 do zero-shot image segmentation on new datasets without fine-tuning?▼

Zero-shot image segmentation uses the Segment Anything Model to isolate objects in any image domain without fine-tuning. You provide point, bounding box, or previous mask prompts to guide the segmentation across diverse visual data.

What is the best way to automatically generate all object masks for an image?▼

Automatic segmentation generates all object masks for a given image without requiring manual prompts. This enables rapid annotation pipelines and data labeling workflows across diverse image domains using zero-shot transfer.

Can I use point and box prompts together for prompt-based image segmentation?▼

Yes, prompt-based image segmentation supports point, bounding box, and previous mask prompts simultaneously. These flexible prompt types allow you to accurately annotate and edit specific objects within images across various domains.

Do I need PyTorch and transformers installed to run SAM for image annotation?▼

Yes, running SAM for image annotation requires installing segment-anything, transformers>=4.30.0, and torch>=1.7.0. You must configure these dependencies before executing prompt-based or automatic segmentation workflows.

Does zero-shot image segmentation work for custom image domains outside standard datasets?▼

Zero-shot image segmentation works on any custom image domain without requiring fine-tuning. It leverages the Segment Anything Model to transfer segmentation capabilities across diverse visual inputs for flexible annotation pipelines.

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