segment-anything-model

Segment objects in images using prompts or automatic mask generation.

2|Updated Feb 4, 2026

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

Ready to run

npx skills add https://github.com/zhuangbiaowei/smart_bot --skill segment-anything-model-zhuangbiaowei

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill enables precise object segmentation in images without requiring task-specific training data, making advanced computer vision accessible for diverse applications.

Core Features & Use Cases

Zero-Shot Segmentation: Segment any object in any image using flexible prompts like points, boxes, or masks.
Automatic Mask Generation: Automatically identify and mask all objects within an image.
Use Case: Quickly create masks for objects in a photograph for image editing, generate training data for other AI models, or build interactive annotation tools.

Quick Start

Use the segment-anything-model skill to segment all objects in the attached image 'photo.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 on an object without training data?▼

You can perform zero-shot image segmentation by providing flexible prompts like points, bounding boxes, or masks to identify any object. It leverages Meta AI's Segment Anything Model to generate high-quality masks without requiring task-specific training data.

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

Yes, automatic mask generation identifies and segments all objects within an image simultaneously. This feature outputs multiple high-quality masks for distinct items, enabling rapid scene parsing without manually drawing individual prompts for each object.

What input prompts does the Segment Anything Model accept for object detection?▼

The Segment Anything Model accepts spatial points, bounding boxes, and existing masks as input prompts for object detection and segmentation. These flexible prompt types allow precise interactive annotation and versatile visual understanding across diverse image contexts.

Do I need PyTorch and OpenCV to run zero-shot image segmentation?▼

Yes, running zero-shot image segmentation requires PyTorch and OpenCV along with the segment-anything, transformers, and onnxruntime libraries. These dependencies provide the necessary deep learning inference and computer vision processing capabilities for mask generation.

What is the best way to generate training data for computer vision models?▼

The best way to generate training data for computer vision models is using zero-shot image segmentation to automatically produce precise object masks. This approach quickly creates annotated datasets for image editing or training other AI systems without manual labeling.

Are there limitations when using zero-shot segmentation for image editing?▼

Zero-shot segmentation handles arbitrary objects without task-specific training, but relies heavily on prompt accuracy and input image resolution. Complex occlusions or highly ambiguous visual boundaries may produce less precise masks compared to fine-tuned object detection models.