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.