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Image segmentation with SAM (Segment Anything) v1.0.0

Use this resource to segment objects in images without task-specific training

New

  • 469 downloads
  • 228 likes
  • 160 saves

Free

Install with the CLI

In a terminal, from the folder you want:

npx --yes palatekit@latest install sam-image-segmentation

With no option, the skill goes to ~/.agents/skills/; add --agent claude-code (or codex, cursor…) to target an agent.

First time: connect the CLI with

npx --yes palatekit@latest login

The agent installs SAM, loads the checkpoint suited to your hardware (ViT-H, ViT-L or ViT-B), then segments an object from points or a box, or generates every mask in an image automatically. You get masks you can use for annotation or detection.

Mostly picked by

Updated November 2, 2026

Card for the “Image segmentation with SAM (Segment Anything)” skill

Details

Works in
Install guides
Generates with
Your agent's model
Licence
MIT

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What you get

  • SKILL.md: installation, checkpoint choice and SamPredictor usage
  • references/advanced-usage.md: advanced usage
  • references/troubleshooting.md: common issues and fixes

Good fit for

  • You annotate images and want to speed up cut-outs
  • You generate training data for another vision model

Not for

    Tags

    • segmentation
    • vision
    • sam

    About

    You say

    “Segment the foreground product in this photo from a single click point.”

    Card for the “Image segmentation with SAM (Segment Anything)” skill

    Skill published by davila7 under the MIT license, delivered in its original version.

    Show more

    How to use it

    1. Install the skill in your agent's skills folder
    2. Ask your agent to segment objects in images without task-specific training
    3. Review and adjust the result

    Content preview

    Files

    • LICENSE1.1 KB
    • NOTICE-PALATEKIT.md0.5 KB
    • references/advanced-usage.md16 KB
    • references/troubleshooting.md12.1 KB
    • SKILL.md13.6 KB

    Origin and licence

    Listed unmodified

    Author
    davila7
    Source
    github.com/davila7/claude-code-templates/tree/f57964ebda91cdfb31755661ecea9395f30020f1/cli-tool/components/skills/ai-research/multimodal-segment-anything
    Licence
    MIT

    Notice

    « multimodal-segment-anything » (davila7, MIT) livré dans sa version d'origine : fiche en français et en anglais par PalateKit.

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