segment-anything-model

Generate binary object masks from images via zero-shot segmentation.

Updated Apr 10, 2026

Search Tags:#pytorch #zero-shot #sam #onnx #mask-generation #image-segmentation #segment-anything

Ready to run

npx skills add https://github.com/VYRE-Studios/Windows-Agentic-Framework --skill segment-anything-model-vyre-studios

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: segment-anything-model
Source: https://github.com/VYRE-Studios/Windows-Agentic-Framework/tree/main/skills/mlops/models/segment-anything
Command: npx skills add https://github.com/VYRE-Studios/Windows-Agentic-Framework --skill segment-anything-model-vyre-studios

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Segment objects in images without task-specific training by producing high-quality binary masks from point, box, or mask prompts and by automatically generating all object masks in an image to accelerate annotation, extraction, and downstream vision pipelines.

Core Features & Use Cases

Zero-shot segmentation across domains (natural, medical, satellite) with no fine-tuning required.
Flexible prompting using points, bounding boxes, or previous masks for interactive annotation and iterative refinement.
Automatic mask generation for dataset creation, large-scale annotation, and object extraction workflows.
Deployment options including PyTorch/HuggingFace usage, ONNX export, TensorRT acceleration, and scalable batch processing for production services.

Quick Start

Load a SAM checkpoint, set the image in the predictor, and request a mask by providing a point, box, or using the automatic mask generator to produce masks for the image.

Frequently Asked Questions about segment-anything-model

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

What is zero-shot image segmentation and how does it generate object masks?▼

Zero-shot image segmentation produces high-quality binary object masks from images without requiring task-specific training. It accepts point, box, or mask prompts to isolate objects, enabling interactive annotation and automatic mask generation across domains like medical and satellite imaging.

How do I use segment anything checkpoints to segment objects in PyTorch?▼

To segment objects in PyTorch, load a segment-anything checkpoint into the predictor, set your target image, and request a mask by providing point or bounding box prompts. You can also use the automatic mask generator to produce all object masks for the image.

Can I export the model to ONNX or TensorRT for production deployment?▼

Yes, the model supports ONNX export and TensorRT acceleration for scalable batch processing. These deployment options allow you to integrate zero-shot segmentation into production vision pipelines and large-scale object extraction workflows.

Does zero-shot segmentation work for medical and satellite imaging without fine-tuning?▼

Yes, zero-shot segmentation applies to medical and satellite imaging without fine-tuning. It generates binary masks across natural, medical, and satellite domains by leveraging prompt-based segmentation, accelerating annotation and extraction in specialized fields.

Do I need sufficient GPU memory to run automatic mask generation?▼

Yes, you need sufficient GPU memory for automatic mask generation based on your chosen model size. The process loads segment-anything checkpoints into a PyTorch or ONNX runtime, and larger models require more memory to produce all object masks.

What is the best way to automate dataset creation with image segmentation?▼

The best way to automate dataset creation is using automatic mask generation to produce all object masks in an image. This zero-shot approach accelerates large-scale annotation and object extraction workflows without requiring manual fine-tuning.