Demo Visualize

Organization

VectorSpaceLab

demo-visualize

Guides AdelaiDet image/video/webcam demos, VisualizationDemo usage, confidence thresholds, output handling, and dataset visualization.

Overview

Publisher	VectorSpaceLab
Repository	`AREX-Skill`
Skill name	`demo-visualize`
Stars	341
Forks	26
Bundled files	4
License	NOASSERTION
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.
4 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by VectorSpaceLab on GitHub. Read the source before you install it.

Installation

Install the Demo Visualize 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/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/adelai-det/sub-skills/demo-visualize
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/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/adelai-det/sub-skills/demo-visualize

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/VectorSpaceLab/AREX-Skill.git /tmp/AREX-Skill
mkdir -p .claude/skills
cp -r /tmp/AREX-Skill/skills/repositories/repo-skills/adelai-det/sub-skills/demo-visualize .claude/skills/demo-visualize

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

Use it in TypingMind

Enable Demo Visualize 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 Demo Visualize 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 Demo Visualize 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.

demo-visualize

Use this sub-skill when a task asks to run AdelaiDet inference on images, videos, webcam streams, visualize predictions, adjust confidence thresholds, or inspect dataset annotations visually.

Use this route for

Building AdelaiDet demo commands for image, video, or webcam inference.
Understanding VisualizationDemo, Detectron2 predictors, and output behavior.
Running dataset visualization before training.
Debugging missing MODEL.WEIGHTS, input/output path, OpenCV, or headless-display issues.
Visualizing text/non-text predictions after setup succeeds.

Do not use this route for

Environment/build failures. Use ../setup-build/SKILL.md.
Training/evaluation launches. Use ../train-eval/SKILL.md.
Text lexicons/dictionaries or text evaluator behavior. Use ../text-spotting/SKILL.md.
Dataset conversion/preparation. Use ../data-prep/SKILL.md.
ONNX export. Use ../export-convert/SKILL.md.

Read first

references/demo-workflows.md for image/video/webcam launch patterns.
references/visualization.md for dataset visualization and output interpretation.
../../references/troubleshooting.md for OpenCV/headless and checkpoint issues.

Skill-owned scripts

scripts/run_demo.py — validates repo root, config, weights, input/output mode, then builds/runs the demo command.
scripts/visualize_dataset.py — validates and launches the dataset visualization workflow.

Typical workflow

Confirm setup with ../../scripts/check_install.py --cuda-ops.
Choose a config and weights. Use train-eval if you are not sure which config family fits.

Dry-run the demo:

bash
python scripts/run_demo.py --repo-root /path/to/AdelaiDet \
  --config configs/FCOS-Detection/R_50_1x.yaml \
  --weights /path/to/model.pth --input image.jpg --output outputs/ --dry-run

Remove --dry-run to execute.
For dataset visual inspection, use scripts/visualize_dataset.py --dry-run first.

Decision points

Headless server with no display: use image/video input and --output; avoid webcam or GUI window modes.
Text model predictions: use this route for drawing outputs, but switch to text-spotting for lexicon/evaluation semantics.
Missing/incorrect boxes or masks with a custom dataset: switch to data-prep to inspect annotations and dataset mappers.

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 Demo Visualize AI skill do?

Guides AdelaiDet image/video/webcam demos, VisualizationDemo usage, confidence thresholds, output handling, and dataset visualization.

Why use Demo Visualize on TypingMind?

Because you install it once and use it with any model. Demo Visualize 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 Demo Visualize in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/adelai-det/sub-skills/demo-visualize. 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 Demo Visualize?

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 Demo Visualize?

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

Is the Demo Visualize AI skill free?

Yes. It is published on GitHub by VectorSpaceLab under the NOASSERTION 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

demo-visualize

Use this route for

Do not use this route for

Read first

Skill-owned scripts

Typical workflow

Decision points

Bundled files

Frequently asked questions

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