Meta's foundation model for zero-shot image segmentation. Point at anything in an image and get a mask, no training required. Works across domains (medical, satellite, photos) because it was trained on 1.1 billion masks. You get three model sizes (ViT-B is fast, ViT-H is accurate), flexible prompts (points, boxes, or previous masks), and automatic mask generation that finds every object. The image encoder runs once, then you can segment interactively in real time. Honest take: it's overkill if you already have labeled data for your specific task, but if you're building annotation tools or need segmentation to work on weird domains out of the box, nothing else comes close.

skills.sh installs
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GitHub stars
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last updated
Aug 21, 2026

Install

$ npx -y skills add orchestra-research/ai-research-skills --skill segment-anything-model --agent claude-code

Installs into .claude/skills of the current project.

Files

Segment Anything Model (SAM)

Comprehensive guide to using Meta AI's Segment Anything Model for zero-shot image segmentation.

When to use SAM

Use SAM when:

  • Need to segment any object in images without task-specific training
  • Building interactive annotation tools with point/box prompts
  • Generating training data for other vision models
  • Need zero-shot transfer to new image domains
  • Building object detection/segmentation pipelines
  • Processing medical, satellite, or domain-specific images

Key features:

  • 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
  • High quality: Trained on 1.1 billion masks from 11 million images
  • Multiple model sizes: ViT-B (fastest), ViT-L, ViT-H (most accurate)
  • ONNX export: Deploy in browsers and edge devices

Sponsors

Details

Repository
github.com/orchestra-research/ai-research-skills
Categories
AI & Agent Building, Data Science & ML
First seen
Jun 3, 2026

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