Onnx And Benchmarking

Organization

VectorSpaceLab

onnx-and-benchmarking

Use 3DDFA_V2 ONNX Runtime acceleration and CPU latency or speed benchmark workflows.

Overview

Publisher	VectorSpaceLab
Repository	`AREX-Skill`
Skill name	`onnx-and-benchmarking`
Stars	341
Forks	26
Bundled files	4
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.
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 Onnx And Benchmarking 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/3ddfa-v2/sub-skills/onnx-and-benchmarking
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/3ddfa-v2/sub-skills/onnx-and-benchmarking

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/3ddfa-v2/sub-skills/onnx-and-benchmarking .claude/skills/onnx-and-benchmarking

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

Use it in TypingMind

Enable Onnx And Benchmarking 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 Onnx And Benchmarking 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 Onnx And Benchmarking 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.

ONNX and benchmarking

Use this sub-skill when the task is about --onnx, ONNX conversion, CPU latency, OMP_NUM_THREADS, latency.py, or speed_cpu.py.

When to read

Read this sub-skill when the task asks to:

Use ONNX Runtime instead of the PyTorch model path.
Explain or prepare TDDFA_ONNX and FaceBoxes_ONNX.
Run end-to-end latency timing on an image.
Run the model-only CPU microbenchmark.
Diagnose missing .onnx files, OpenMP/libomp issues, or unstable timings.

Before running

Use ../setup-and-assets/ to build and verify the baseline checkout.
Check ../../references/model-assets.md for which .pth, .pkl, and generated .onnx assets are involved.
Keep benchmark inputs and thread counts fixed when comparing numbers.

Latency wrapper

The latency wrapper preserves the original latency.py CLI. Put original arguments after --:

bash
python <skill-root>/sub-skills/onnx-and-benchmarking/scripts/run-latency.py \
  --repo-root <checkout> -- \
  -f <image-path> --onnx --repeated 1 --warmup true

This reports face detection, 3DMM regression, and reconstruction timing.

Speed wrapper

The speed wrapper preserves speed_cpu.py:

bash
python <skill-root>/sub-skills/onnx-and-benchmarking/scripts/run-speed-cpu.py \
  --repo-root <checkout>

It runs a random input through weights/mb1_120x120.onnx and prints the mean and standard deviation per inference.

ONNX conversion behavior

FaceBoxes_ONNX converts FaceBoxesProd.pth to FaceBoxesProd.onnx if the ONNX file is missing.
TDDFA_ONNX converts the selected .pth checkpoint and the BFM decoder if the ONNX artifacts are missing.
Conversion is CPU-safe but mutates the checkout by writing generated .onnx files.

Thread and provider notes

The repo sets OMP_NUM_THREADS=4 in the ONNX demo paths.
KMP_DUPLICATE_LIB_OK=True is set by the demo scripts before ONNX imports.
CPU ONNX Runtime is the baseline. CUDA/other providers are outside the required verification scope.
On macOS, the original README notes that libomp may be required.

Troubleshooting

Read references/troubleshooting.md for ONNX-specific issues and ../../references/troubleshooting.md for shared setup failures.

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 Onnx And Benchmarking AI skill do?

Use 3DDFA_V2 ONNX Runtime acceleration and CPU latency or speed benchmark workflows.

Why use Onnx And Benchmarking on TypingMind?

Because you install it once and use it with any model. Onnx And Benchmarking 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 Onnx And Benchmarking in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/3ddfa-v2/sub-skills/onnx-and-benchmarking. 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 Onnx And Benchmarking?

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 Onnx And Benchmarking?

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

Is the Onnx And Benchmarking AI skill free?

Yes. It is published on GitHub by VectorSpaceLab 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

ONNX and benchmarking

When to read

Before running

Latency wrapper

Speed wrapper

ONNX conversion behavior

Thread and provider notes

Troubleshooting

Bundled files

Frequently asked questions

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