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Datos e investigación · SKILL.md

datasets-and-preprocessing

datasets-and-preprocessing

Route Det3D dataset layout, metadata conversion, annotation, loading, voxelization, and pipeline-preprocessing tasks.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentación de la Skill

GitHub

name: datasets-and-preprocessing description: "Route Det3D dataset layout, metadata conversion, annotation, loading, voxelization, and pipeline-preprocessing tasks." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Datasets and Preprocessing

Use this route for KITTI, nuScenes, or Lyft directory layouts; info/database creation; annotation schemas; point-cloud loading; voxel/pillar preprocessing; transforms; and dataset validation. Read data-formats.md before changing a config and data-preparation.md before running a conversion.

Workflow

  1. Identify dataset/version/split and make the root explicit.
  2. Run scripts/validate_dataset_layout.py as a non-mutating preflight.
  3. Confirm generated info paths, DB-info paths, sweep count, class names, and coordinate conventions match the config.
  4. Read pipeline-api.md for transform ordering, fields, and tensor/voxel contracts.
  5. Only then plan create_data or training/evaluation; conversion can write many files and may require dataset SDKs and a GPU environment.

Dataset conversion, ground-truth database creation, and full loaders are not validated by an empty directory or a config parse. Keep data and output paths user-supplied; never embed checkout-specific paths in reusable instructions.

For model/config coupling use configuration-and-models; for CLI execution use training-and-evaluation; for missing SDKs or compiled ops use runtime-ops.

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