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name: config-customization description: "Choose, customize, and validate MMYOLO model-family configs without running training, testing, deployment, or dataset conversion." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

MMYOLO Config Customization

Use this sub-skill when the task is to choose an MMYOLO configuration, understand MMEngine inheritance, edit model/dataset/runtime fields, apply safe --cfg-options, or preflight a config before handing it to training, testing, inference, or deployment workflows.

Read this way

1. Start with model zoo and config selection to choose a family and baseline config pattern.
2. Use configuration editing for _base_ inheritance, one-class COCO-style fine-tuning edits, num_classes, dataloaders, evaluators, hooks, schedulers, --cfg-options, TTA, and batch_shapes_cfg caveats.
3. Use troubleshooting when config loading, class metadata, TTA, or override behavior is wrong.
4. Run scripts/print_mmyolo_config_summary.py against a user-provided config to print a model/dataloader/runtime/TTA summary without training or building a runner.

Scope and routing

This sub-skill owns:

• Config families for YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLOX, RTMDet, and PPYOLOE.
• model-index.yml and family metafile.yml selection signals distilled into model-family guidance.
• MMEngine _base_ inheritance, _delete_=True, inherited variable reuse, intermediate-variable rebinding, and config filename interpretation.
• Key config fields: model, train_dataloader, val_dataloader, test_dataloader, val_evaluator, test_evaluator, train_cfg, default_hooks, custom_hooks, optim_wrapper, param_scheduler, load_from, resume, visualizer, log_processor, and env_cfg.
• Custom dataset metadata: lowercase metainfo keys, classes, palette, num_classes, evaluator annotation files, batch size/workers, validation/checkpoint/logger intervals, and scheduler adjustments.
• Config-only TTA checks: tta_model, tta_pipeline, and the batch_shapes_cfg incompatibility handled by MMYOLO test logic.

Route elsewhere:

• Training, testing, evaluation command execution, checkpoint handling, metrics output, and distributed launch: training-evaluation.
• Dataset conversion, COCO/YOLO layout validation, annotation inspection, anchor optimization, and data browsing: data-tools.
• Low-level registry/class/API extension, custom modules, and model component implementation: model-api.
• ONNX/TensorRT/RKNN/DeepStream/export/deploy configs and backend artifacts: deployment-conversion.

Fast decision checklist

• Need a baseline model? Pick a family by speed/accuracy/resource signals, then choose the nearest size/config pattern in model zoo and config selection.
• Need to fine-tune on one or a few COCO-style classes? Create a small child config inheriting the closest baseline. Do not edit the baseline in-place unless the user explicitly wants to maintain that project config.
• Need a quick override? Use --cfg-options only for scalar, simple dict, or simple list changes; prefer a child config for dataset, pipeline, model-head, scheduler, hook, or TTA edits.
• Need to validate before a run? Use the bundled summary helper and inspect num_classes, metainfo, annotation paths, dataloader batch size/workers, evaluator files, epoch/interval settings, and TTA readiness.