Cv 3d Patch Sliding Window Inference

Community

wenmin-wu

cv-3d-patch-sliding-window-inference

Tiles 3D volumes into overlapping patches for inference and averages overlapping regions to produce seamless predictions.

Overview

Publisher	wenmin-wu
Repository	`ds-skills`
Skill name	`cv-3d-patch-sliding-window-inference`
Stars	63
Forks	4
Bundled files	Instructions only
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by wenmin-wu on GitHub. Read the source before you install it.

Installation

Install the Cv 3d Patch Sliding Window Inference AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/wenmin-wu/ds-skills/tree/main/skills/cv/3d-patch-sliding-window-inference
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/wenmin-wu/ds-skills/tree/main/skills/cv/3d-patch-sliding-window-inference

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/wenmin-wu/ds-skills.git /tmp/ds-skills
mkdir -p .claude/skills
cp -r /tmp/ds-skills/skills/cv/3d-patch-sliding-window-inference .claude/skills/cv-3d-patch-sliding-window-inference

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cv 3d Patch Sliding Window Inference in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Cv 3d Patch Sliding Window Inference on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Cv 3d Patch Sliding Window Inference is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

3D Patch Sliding-Window Inference

Overview

Large 3D volumes (CT, cryo-ET, MRI) rarely fit in GPU memory whole. Sliding-window inference tiles the volume into overlapping patches, runs the model on each, then blends overlapping regions via averaging or Gaussian weighting. Eliminates boundary artifacts while keeping memory constant.

Quick Start

python
from monai.inferers import SlidingWindowInferer

inferer = SlidingWindowInferer(
    roi_size=(96, 96, 96),
    sw_batch_size=4,
    overlap=0.5,
    mode="gaussian",  # Gaussian weighting reduces edge artifacts
)
output = inferer(inputs=volume_tensor, network=model)

Workflow

Choose roi_size matching the model's training patch size
Set overlap (0.25–0.5) — higher overlap = smoother but slower
Select blending mode: "gaussian" weights center pixels more; "constant" averages uniformly
Run inferer — it handles tiling, batching, and stitching automatically
Post-process the full-resolution prediction volume

Key Decisions

Overlap ratio: 0.5 is standard; 0.25 acceptable if speed-constrained
Blending mode: Gaussian preferred — reduces checkerboard artifacts at patch boundaries
sw_batch_size: Max patches per forward pass; tune to available VRAM
Without MONAI: Implement manually with np.lib.stride_tricks + weighted accumulation buffer

Frequently asked questions

What does the Cv 3d Patch Sliding Window Inference AI skill do?

Tiles 3D volumes into overlapping patches for inference and averages overlapping regions to produce seamless predictions.

Why use Cv 3d Patch Sliding Window Inference on TypingMind?

Because you install it once and use it with any model. Cv 3d Patch Sliding Window Inference is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Cv 3d Patch Sliding Window Inference in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wenmin-wu/ds-skills/tree/main/skills/cv/3d-patch-sliding-window-inference. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Cv 3d Patch Sliding Window Inference?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Cv 3d Patch Sliding Window Inference?

As many as you like. As long as a model supports skills, you can use Cv 3d Patch Sliding Window Inference with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Cv 3d Patch Sliding Window Inference AI skill free?

It is published on GitHub by wenmin-wu. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

3D Patch Sliding-Window Inference

Overview

Quick Start

Workflow

Key Decisions

References

Frequently asked questions

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