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name: configuration-model-zoo description: "Select, inspect, and adapt MMDetection3D configs and model-zoo entries." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Configuration and Model Zoo

Use this sub-skill when the task is to choose, inspect, compare, or lightly adapt an MMDetection3D v1.x config or model-zoo entry before running another workflow.

Load this when

• A user names a model family, config filename, checkpoint alias, metric table row, dataset, or modality and asks which MMDetection3D config to use.
• A user needs to inspect a config's inherited model, dataset, dataloader, evaluator, runtime, or schedule fields.
• A user wants a safe child config or --cfg-options override for dataset roots, classes, batch size, work directory, test-time options, AMP, or other config-only changes.
• A user needs to understand config inheritance, _delete_=True, naming conventions, model-index entries, or model-zoo checkpoint/config matching.

Route elsewhere

• Raw dataset download, conversion, info-file generation, or layout validation: use the data-preparation sub-skill.
• Launching train/test/evaluation/distributed jobs after the config is chosen: use the training-evaluation sub-skill.
• Inference API calls, demo-style prediction, or visualization outputs from a selected config/checkpoint: use the inference sub-skill.
• Custom Python modules, registry implementations, optional project packages, or new components: use the customization-extensions sub-skill.
• Box/coordinate-system or visualizer API reasoning: use the structures-visualization sub-skill.

Operating workflow

1. Identify the task axis: modality, dataset, model family, metric target, available hardware/backend, and whether the request is selection, inspection, or adaptation.
2. If a config path is available, inspect it with scripts/check_config.py before recommending overrides.
3. Use references/model-overview.md to map model aliases, checkpoint names, and dataset/task families to likely config folders.
4. Use references/configuration.md to reason about _base_, top-level keys, child-config edits, --cfg-options, and class/dataset changes.
5. Use references/troubleshooting.md when parsing fails, overrides do not apply, legacy fields appear, model/checkpoint pairs mismatch, or optional sparse/project backends are involved.
6. Hand off the selected config, checkpoint expectation, and concrete overrides to training-evaluation or inference when execution is requested.

Default deliverables

For config/model-zoo tasks, produce one or more of:

• A short recommendation naming the config family, expected dataset/task, checkpoint compatibility, and any backend caveats.
• A compact child-config patch or override list, not a full copied config unless the user asks for a standalone file.
• A check_config.py summary with suspicious fields highlighted.
• A routing handoff to the next sub-skill for data preparation, training/evaluation, inference, or customization.