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name: data-tools description: "Prepare and validate datasets for MMYOLO: COCO/YOLO/VOC/DOTA/LabelMe layouts, converters, analysis tools, and data APIs." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

MMYOLO data-tools

Use this sub-skill when the task is about making dataset annotations usable by MMYOLO before training, evaluation, browsing, anchor analysis, or config wiring.

Fast route

1. For annotation schema and config wiring, read data formats.
2. For converter, browser, statistics, anchor, subset, split, and DOTA recipes, read data tools.
3. For data-prep failures and MMYOLO-specific gotchas, read troubleshooting.
4. Run the bundled validators when possible before sending a user to training/evaluation:

python scripts/inspect_coco_annotations.py annotations/trainval.json --image-root images --require-annotations
python scripts/convert_yolo_txt_to_coco_skeleton.py ./my_yolo_dataset --out annotations/result.json --image-width 640 --image-height 480

Own these tasks

• Validate COCO-style images, annotations, and categories consistency, including image/category references, duplicate ids, bbox shape, and image file presence.
• Convert a small YOLO normalized-txt dataset into a COCO detection JSON skeleton and catch invalid normalized coordinates early.
• Explain COCO, YOLO txt, VOC, DOTA, LabelMe, cat, and balloon data layouts.
• Wire dataset fields into MMYOLO configs: data_root, ann_file, data_prefix, metainfo, and evaluator ann_file.
• Summarize MMYOLO data utilities for browsing annotations/datasets, dataset statistics, anchor optimization, COCO subset extraction, COCO splitting, and DOTA splitting without requiring the original source checkout.
• State dataset and transform API facts from MMYOLO's package surface.

Route away

• Actual training, testing, resume, AMP, distributed launch, or metric execution: route to training-evaluation.
• Config inheritance, model-family selection, and complete model/head edits: route to config-customization.
• Model internals, registries, custom modules, and low-level model API behavior: route to model-api.
• Deployment export or checkpoint conversion: route to deployment-conversion.

Safety notes

The bundled scripts are deterministic, local-file-only helpers. They do not download datasets, run training, invoke MMYOLO, or require OpenMMLab packages. Treat MMYOLO's heavier visual/statistical utilities as reference recipes unless the user explicitly has a prepared checkout/environment and accepts their plotting, copying, or compute side effects.