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3d Resnets Pytorch

Organization
VectorSpaceLab
3d-resnets-pytorch

Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.

Overview

PublisherVectorSpaceLab
RepositoryAREX-Skill
Skill name3d-resnets-pytorch
Stars
341
Forks
26
Bundled files
19
LicenseMIT
Links
Skill sourceRepository
  • 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.

  • 19 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 3d Resnets Pytorch AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/3d-resnets-pytorch
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

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/3d-resnets-pytorch .claude/skills/3d-resnets-pytorch
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable 3d Resnets Pytorch 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 3d Resnets Pytorch 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 3d Resnets Pytorch 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 ResNets PyTorch

Use this root skill when the user asks about the 3D ResNets PyTorch repository, its CLI flags, its dataset layouts, or its video action-recognition workflows.

Read first

  • references/cli-reference.md
  • references/troubleshooting.md
  • references/repo-provenance.md
  • scripts/check_imports.py
  • scripts/check_main_help.py

What this skill covers

  • Training, validation, inference, and fine-tuning for video action recognition.
  • Dataset preparation from raw videos into JPEG frame trees, RGB HDF5 files, and annotation JSONs.
  • Checkpoint handling, model-family selection, and result evaluation.
  • Common data-layout and runtime compatibility pitfalls.

Route to a sub-skill

training-and-inference

Use this route for:

  • Fresh training runs, resume flows, and pretrained fine-tuning.
  • Validation-only runs and sliding-window inference.
  • Result scoring and DataParallel checkpoint cleanup.
  • Questions about model families, class counts, ft_begin_module, or resume_path / pretrain_path behavior.

Read:

  • sub-skills/training-and-inference/SKILL.md
  • sub-skills/training-and-inference/references/workflows.md
  • sub-skills/training-and-inference/references/model-catalog.md
  • sub-skills/training-and-inference/references/troubleshooting.md
  • sub-skills/training-and-inference/scripts/evaluate_results.py
  • sub-skills/training-and-inference/scripts/strip_dataparallel.py

data-preparation

Use this route for:

  • Extracting JPEG frames or RGB HDF5 files from raw videos.
  • Building Kinetics, UCF101, HMDB51, MIT, or ActivityNet JSON metadata.
  • Adding ActivityNet fps fields.
  • Questions about class directories, split files, HDF5 manifests, or jpg versus hdf5 layout.

Read:

  • sub-skills/data-preparation/SKILL.md
  • sub-skills/data-preparation/references/workflows.md
  • sub-skills/data-preparation/references/data-formats.md
  • sub-skills/data-preparation/references/troubleshooting.md
  • sub-skills/data-preparation/scripts/extract_video_frames.py
  • sub-skills/data-preparation/scripts/extract_video_hdf5.py
  • sub-skills/data-preparation/scripts/build_annotation_json.py

Quick runtime helpers

  • scripts/check_imports.py verifies that the core source modules import from a checkout and applies the temporary legacy Scale alias when needed.
  • scripts/check_main_help.py prints the full main.py CLI help through the same compatibility shim.
  • scripts/run_main.py forwards into the repository CLI after preparing the checkout and compatibility shim.

Shared environment facts

This repo expects a Python environment with PyTorch, torchvision, pandas, h5py, scikit-learn, joblib, and FFmpeg/FFprobe available on PATH.

A modern torchvision wheel may not expose torchvision.transforms.Scale. If that happens, use the compatibility shim in scripts/_torchvision_compat.py or a legacy torchvision release that still ships Scale.

Route selection guidance

  • If the user already has prepared videos and annotation JSONs, start with training-and-inference.
  • If the user still needs frames, HDF5 files, or split JSONs, start with data-preparation.
  • If the request mentions both, do data preparation first unless the videos and labels are already ready.
  • If the user only wants command discovery or environment checks, use the root helpers and the sub-skill references rather than reopening the source repo.

Common handoff sequence

  1. Prepare or verify the dataset layout with data-preparation.
  2. Verify the environment with scripts/check_imports.py.
  3. Inspect the CLI with scripts/check_main_help.py.
  4. Run the desired training or inference command with scripts/run_main.py.
  5. Score or clean outputs with the training-and-inference helpers.

Don’t do this

  • Do not point future agents back to the original checkout paths.
  • Do not use flow with JPEG inputs.
  • Do not assume resume checkpoints can change architecture.
  • Do not score --inference_no_average JSON with the result evaluator before aggregating segment outputs.

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 3d Resnets Pytorch AI skill do?

Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.

Why use 3d Resnets Pytorch on TypingMind?

Because you install it once and use it with any model. 3d Resnets Pytorch 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 3d Resnets Pytorch in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/3d-resnets-pytorch. 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 3d Resnets Pytorch?

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 3d Resnets Pytorch?

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

Is the 3d Resnets Pytorch 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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