---

name: luminoth description: "Routes Luminoth object-detection dataset, training, prediction, and checkpoint workflows." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

Luminoth

Luminoth is an alpha TensorFlow 1.x object-detection toolkit. Use this skill for workflow guidance around dataset conversion, training, evaluation, prediction, checkpoint packaging, and the demo web app.

This skill is self-contained for future agents. Do not depend on the original checkout after reading the bundled references and scripts.

Read first

• references/repo-provenance.md when checking whether this skill still matches the repository snapshot you are using.
• references/installation.md for supported Python/TensorFlow versions, optional extras, and a minimal install smoke.
• references/troubleshooting.md for cross-cutting install, import, and runtime failures.
• scripts/check_luminoth_install.py for a safe import and CLI smoke check.
• references/repo-routing-metadata.json if you need to understand how this skill is meant to be routed by a managed repo-skills router.

Route by task

User goal	Read or run	Notes
Convert or merge object-detection datasets	sub-skills/dataset-preparation/SKILL.md	Covers lumi dataset transform, lumi dataset merge, reader layouts, classes.json, TFRecord output, and dataset validation.
Train or evaluate a model locally or in Google Cloud	sub-skills/training/SKILL.md	Covers lumi train, lumi eval, config files, overrides, TensorBoard, and the optional lumi cloud gc workflow.
Predict images or videos, serve the demo web app, or use the Python API	sub-skills/prediction/SKILL.md	Covers lumi predict, lumi server web, Detector, read_image, and vis_objects.
Inspect, download, import, export, or package checkpoints	sub-skills/checkpoints/SKILL.md	Covers the local checkpoint index, remote index refresh, alias/id rules, and checkpoint tarballs.

What to do before deeper work

• Run the bundled install smoke if you only need to confirm the package is importable:

  python scripts/check_luminoth_install.py
  

• Use the sub-skill that owns the workflow instead of mixing several routes in one place. In particular:

  • dataset conversion belongs before training,
  • checkpoints belong before prediction or web serving,
  • training/eval configuration belongs before lumi eval, and
  • Google Cloud usage is an optional branch of the training sub-skill, not a separate root route.

Quick capability map

• Public CLI entry point: lumi.
• Main model families: Faster R-CNN and SSD.
• Dataset registry: object_detection / tfrecord.
• Supported dataset readers: coco, csv, flat, imagenet, openimages, pascal, and taggerine.
• Common prediction fallback: accurate checkpoint when neither config nor checkpoint is specified.

When to read the root references

Use the root references when you need cross-cutting information that is shared by several sub-skills:

• installation details and supported dependency groups,
• TensorFlow / FFmpeg / Google Cloud prerequisites,
• deprecation or maintenance caveats,
• staleness checks for the generated skill, and
• router metadata for managed import or refresh decisions.

Cross-skill routing reminders

• If the request starts with raw data and no TFRecords, route to dataset preparation first.
• If the request is about a run directory, checkpoint alias, remote checkpoint, or tarball, route to checkpoints.
• If the request is about lumi predict, the Flask demo, image/video inputs, or Python inference APIs, route to prediction.
• If the request is about lumi train, lumi eval, config merging, TensorBoard, or Google Cloud training, route to training.

Maintenance note

Read references/repo-provenance.md before deciding whether this skill is fresh enough for the current checkout. If the repo commit, dirty state, package version, or supported workflows changed, refresh this skill instead of guessing.