segment-anything-model

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

Updated Mar 13, 2026

Search Tags:#computer vision #object detection #zero-shot #sam #image segmentation #mask generation

Ready to run

npx skills add https://github.com/hochoa13/Asistente-Harlest --skill segment-anything-model-hochoa13

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: segment-anything-model
Source: https://github.com/hochoa13/Asistente-Harlest/tree/main/skills/mlops/models/segment-anything
Command: npx skills add https://github.com/hochoa13/Asistente-Harlest --skill segment-anything-model-hochoa13

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires segment-anything, transformers, torch, opencv-python, pycocotools, matplotlib, onnxruntime, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill enables precise image segmentation for any object without requiring task-specific training data, simplifying complex computer vision tasks.

Core Features & Use Cases

Zero-Shot Segmentation: Segment objects based on points, boxes, or masks as prompts.
Automatic Mask Generation: Automatically identify and mask all objects within an image.
Use Case: Quickly create masks for objects in medical scans, satellite imagery, or product photos for further analysis or annotation.

Quick Start

Use the segment-anything-model skill to segment the object at coordinates (500, 375) in the image 'image.jpg'.

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 without task-specific training data?▼

Zero-shot image segmentation identifies and masks objects without requiring training data. You can segment objects using flexible prompts like points, boxes, or existing masks to guide the model.

How do I automatically generate masks for all objects in an image?▼

Automatic mask generation identifies and masks all objects within an image simultaneously. This process simplifies creating masks for complex visual data like medical scans or satellite imagery.

Can I use PyTorch and the segment-anything library for interactive image annotation?▼

Yes, interactive annotation is supported using PyTorch and the segment-anything library. You can provide prompts like coordinates or bounding boxes to segment specific objects within an image.

Does zero-shot mask generation work for domain-specific image processing like product photos?▼

Zero-shot mask generation works for domain-specific image processing across various contexts. It can quickly create masks for product photos, medical scans, or satellite imagery for further analysis.

What are the limitations of using point prompts for object detection in images?▼

Point prompts guide object detection but require precise coordinate inputs to segment accurately. Prompting with bounding boxes or masks offers flexibility for complex shapes and multiple objects.