---

name: training-evaluation description: "Guides VAD training, checkpoint loading, safe configuration overrides, single-GPU evaluation, result formatting, and diagnosis of legacy-stack runtime failures." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

VAD training and evaluation

Use this route for launch commands, work directories, resume/override behavior, checkpoint evaluation, metrics, or result formatting.

Route

1. Prepare and validate data with data-preparation.
2. Select and parse a VAD config with architecture-configuration and its safe checker.
3. Read workflow-reference.md and cli-reference.md.
4. Run python scripts/check_training_contract.py CONFIG before an expensive command.
5. Train with the selected single/distributed launch recipe; evaluate with one non-distributed GPU unless you have independently verified a compatible alternative. The repository warns that distributed evaluation can produce inaccurate results.

Full training/evaluation was not run during construction: it needs external nuScenes data, checkpoints, and CUDA/native operators.

Scope boundaries

• Raw data and temporal PKLs: data-preparation.
• Model graph and config semantics: architecture-configuration.
• Rendering result artifacts: visualization.

Use troubleshooting.md for import, launcher, normalization, and output failures.