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name: data-datasets description: "Register and validate Detectron2 datasets, metadata, COCO helpers, mappers, and data loaders." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Detectron2 Data & Datasets

Use this sub-skill when a task is about getting data into Detectron2: registering custom datasets, checking Detectron2-standard dataset dicts, setting metadata, using COCO helpers, designing mappers, or building train/test data loaders.

Read First

• references/dataset-format.md for DatasetCatalog, MetadataCatalog, standard dataset dict fields, COCO registration, metadata keys, and dataset-root conventions.
• references/data-loading.md for DatasetMapper, custom mapper patterns, augmentations, and build_detection_train_loader / build_detection_test_loader usage.
• references/troubleshooting.md for common registration, annotation, metadata, class-count, COCO path, and mapper failures.

Common Workflows

1. Register a dataset by name with DatasetCatalog.register(name, callable); the callable must take no arguments and return the same ordered records on every call.
2. Attach shared dataset facts through MetadataCatalog.get(name).set(...), especially class names and evaluator/COCO metadata.
3. Validate records with scripts/validate_dataset_dicts.py before wiring them into a loader or config.
4. Use register_coco_instances or load_coco_json when annotations are COCO instance/keypoint style.
5. Pass the registered names into cfg.DATASETS.TRAIN / cfg.DATASETS.TEST, and route class-count or solver changes to the config/training sub-skills.
6. Customize loading through DatasetMapper(..., augmentations=...) or a custom mapper passed to build_detection_train_loader(..., mapper=...).

Bundled Helpers

• scripts/validate_dataset_dicts.py: validates a JSON list of Detectron2-standard dataset dicts and reports actionable schema/data-shape errors.
• scripts/validate_dataset_registration.py: smoke-checks catalog registration and metadata behavior with a synthetic dataset, and optionally validates a user JSON fixture.

Boundaries

• This sub-skill owns dataset registration, metadata, dataset dict structure, COCO helpers, mapper design, loader construction, dataset roots, and annotation/loader troubleshooting.
• Use the training/evaluation sub-skill for DefaultTrainer, launch commands, evaluators, class-count config changes, and metrics.
• Use the inference/visualization sub-skill for rendering predictions or dataset visualizations.
• Use extension-projects guidance for project-specific dataset converters or custom task heads.