---

name: "training-and-inference" description: "Routes training, fine-tuning, validation, checkpoint, and inference workflows for 3D ResNets PyTorch." disable-model-invocation: true metadata: disco-role: operating license: MIT

training-and-inference

Use this sub-skill when you need to train, fine-tune, validate, resume, or run inference for this repository's main.py-style workflows without reopening the source checkout.

Use this route for

• Fresh training runs and stage-wise fine-tuning.
• Checkpoint save/resume, optimizer state recovery, and milestone handling.
• Loading pretrained weights with a different final class count.
• Validation, multi-clip inference, and result scoring.
• DataParallel checkpoint cleanup before reuse or inspection.

Do not use this route for

• Raw video extraction or annotation JSON creation. Use data-preparation.
• Repo-wide routing or root skill selection. Use the root router.
• Model conversion or export outside the main.py training loop.

Read first

• references/workflows.md
• references/model-catalog.md
• references/troubleshooting.md
• the root router
• the root run helper
• data-preparation when dataset layout is not ready

Skill-owned scripts

• scripts/evaluate_results.py — score top-k recognition results against a ground-truth JSON.
• scripts/strip_dataparallel.py — remove module. prefixes from checkpoint state dicts.

Typical flow

1. Confirm the dataset tree and annotation JSON already exist.
2. Pick a model family and depth from references/model-catalog.md.
3. Choose scratch train, resume, or fine-tune flags in references/workflows.md.
4. Run the root-level main.py wrapper from the repo skill tree.
5. Evaluate results with scripts/evaluate_results.py or clean checkpoints with scripts/strip_dataparallel.py when needed.

Cross-links

• Dataset preparation and layout prerequisites: data-preparation
• Repo-level routing: root router
• Root main.py wrapper: run_main helper