Vlm Segmentation

Community

AnastasiyaW

vlm-segmentation

Choose and evaluate VLM or segmentation pipelines, including text-conditioned detection, masks, part labels, model-license constraints, and measured GPU deployment choices. Use when a task has a VLM or segmentation component; route pure diffusion prompting, training, or serving to its specialized skill.

Overview

Publisher	AnastasiyaW
Repository	`codex-claude-code-config`
Skill name	`vlm-segmentation`
Stars	212
Forks	32
Bundled files	3
License	MIT
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.
3 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by AnastasiyaW on GitHub. Read the source before you install it.

Installation

Install the Vlm Segmentation 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/AnastasiyaW/codex-claude-code-config/tree/main/skills/ai-ml/vlm-segmentation
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/AnastasiyaW/codex-claude-code-config/tree/main/skills/ai-ml/vlm-segmentation

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/AnastasiyaW/codex-claude-code-config.git /tmp/codex-claude-code-config
mkdir -p .claude/skills
cp -r /tmp/codex-claude-code-config/skills/ai-ml/vlm-segmentation .claude/skills/vlm-segmentation

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

Use it in TypingMind

Enable Vlm Segmentation 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 Vlm Segmentation 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 Vlm Segmentation 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.

VLM + Segmentation + Diffusion Engineering

Скилл охватывает три тесно связанных домена. Выбери нужный раздел и загрузи соответствующий reference-файл.

Навигация по доменам

Задача	Reference файл
Выбор модели сегментации, pipeline "текст → маски", VLM-стек, part-labeling	`references/vlm-segmentation.md`
Диффузионные архитектуры, schedulers, обучение, LoRA, text encoder fusion	`references/diffusion-engineering.md`
Два инстанса SAM3 на H100, MIG/MPS, memory, профилирование	`references/gpu-deployment.md`

Правило выбора: если вопрос смешивает темы (например, "как деплоить диффузионную модель на H100") — прочитай оба релевантных файла.

Быстрые ответы без чтения reference-файлов

Candidate pipeline "фраза → маски"


1. SAM3 PCS (текстовый концепт) → instance masks + boxes + scores
   ИЛИ
   Grounding DINO / OWLv2 / YOLO-World → boxes → SAM2.1 → masks

2. Part-labeling: отдельный классификатор по ROI + фиксированный словарь

Candidate diffusion pipeline


1. Backbone: UNet (просто) или DiT/Flow (масштабирование)
2. Latent diffusion (VAE → латенты → денойзер → VAE decode)
3. Text encoder: CLIP (SD), два CLIP (SDXL), Qwen3 (Flux.2 klein 9B)
4. Fine-tune: начинать с LoRA, full fine-tune только если нужно
5. Memory: AMP (BF16) → checkpointing → ZeRO/FSDP при масштабе

Two SAM3 instances on H100 (only after host inspection)


MIG can provide hardware partitioning where the inspected GPU, driver, current
MIG layout and workload support it. It changes host GPU configuration: preserve
the current layout and obtain explicit operational approval before any change.

MPS (fallback) → кооперативный шеринг, без строгой изоляции

Ключевые характеристики моделей (быстрая справка)

Модель	Параметры	Лицензия	Главная сильная сторона
SAM3	848M	SAM License (gated)	Open-vocab сегментация по тексту, все инстансы
SAM2.1-large	model-card specific	Apache-2.0	Видео-трекинг, интерактивная сегментация; reproduce any FPS on the target stack
SAM2.1-tiny	model-card specific	Apache-2.0	Lightweight variant; reproduce any FPS on the target stack
Florence-2-large	770M	MIT	Унифицированные задачи через task prompt
EdgeTAM	~SAM2-tiny	Apache-2.0	16 FPS на iPhone 15 Pro Max, CoreML
Grounding DINO	—	Apache-2.0	Text-conditioned detection, boxes
YOLO-World	—	GPL-3.0	Real-time open-vocab OD, 52 FPS V100

Критические предупреждения

SAM3: gated access на HF, кастомная SAM License — проверь перед продакшном
YOLO-World: upstream states GPL-3.0 and supports commercial usage. GPL obligations apply by default; obtain a separate commercial licence only when the intended distribution or policy requires terms outside GPL, with legal review for the specific product.
Замена text encoder: не plug-and-play, нужен projection + переобучение cross-attention
MIG vs MPS: только MIG даёт аппаратную изоляцию VRAM/SM; MPS — кооперативный шеринг
For non-English prompts, compare the target model’s supported languages on a representative evaluation set; do not silently translate or claim a universal English advantage.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Vlm Segmentation AI skill do?

Why use Vlm Segmentation on TypingMind?

Because you install it once and use it with any model. Vlm Segmentation 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 Vlm Segmentation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AnastasiyaW/codex-claude-code-config/tree/main/skills/ai-ml/vlm-segmentation. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Vlm Segmentation?

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 Vlm Segmentation?

As many as you like. As long as a model supports skills, you can use Vlm Segmentation with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Vlm Segmentation AI skill free?

Yes. It is published on GitHub by AnastasiyaW under the MIT license. 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

VLM + Segmentation + Diffusion Engineering

Навигация по доменам

Быстрые ответы без чтения reference-файлов

Candidate pipeline "фраза → маски"

Candidate diffusion pipeline

Two SAM3 instances on H100 (only after host inspection)

Ключевые характеристики моделей (быстрая справка)

Критические предупреждения

Bundled files

Frequently asked questions

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