---

name: "training" description: "Routes NanoDet training, validation, checkpoint, logging, optimizer, and evaluator workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

training

Use this sub-skill when you need to train, validate, test, resume, or debug a NanoDet Lightning run.

Use this route for

• Launching training or evaluation from a config.
• Resuming from model_last.ckpt or loading a specific checkpoint.
• Inspecting the Lightning task, evaluator, logger, EMA, and optimizer behavior.
• Understanding where checkpoints, logs, and evaluation files are written.
• Converting a legacy .pth checkpoint into the Lightning format.

Do not use this route for

• Config syntax and dataset layout questions. Use dataset-config first.
• Demo inference or export. Use inference-export instead.

Read first

• references/workflows.md for the train / test / checkpoint flow.
• references/troubleshooting.md for runtime and config failures.
• ../../references/api-reference.md for the verified builders and helper APIs.

Skill-owned scripts

• scripts/train.py — run a NanoDet training job from a config.
• scripts/test.py — run validation or test-time evaluation from a config and checkpoint.
• scripts/convert_old_checkpoint.py — convert a legacy checkpoint to the Lightning format.

Typical workflow

1. Validate the config and dataset with dataset-config.
2. Run the skill-owned training launcher.
3. Inspect save_dir, model_last.ckpt, model_best/, eval_results.txt, and logs.
4. Use the test launcher or checkpoint converter when you need to reproduce or debug a saved model.

Cross-links

• If the problem is really a config or dataset issue, switch back to dataset-config.
• If you only need inference or export after training, switch to inference-export.