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name: datasets-and-customization description: "Route dataset layouts, annotation validation, pipeline design, and registry-based customization for MMPretrain." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Datasets and Customization

Use this sub-skill for dataset layout decisions, annotation validation, pipeline construction, and registry-based extension of datasets, transforms, models, heads, losses, metrics, hooks, and optimizers.

Route elsewhere when needed

• Training, testing, resume, or config execution details -> ../training-and-evaluation/SKILL.md
• Inference, checkpoint choice, or model browsing -> ../model-zoo-inference/SKILL.md
• Log/result analysis, visualization, FLOPs, CAM, t-SNE, publishing, or deployment utilities -> ../tools-analysis-and-deployment/SKILL.md

What to do first

1. Read references/data-formats.md to choose the right dataset layout and annotation shape.
2. Read references/registry-customization.md to register or expose a custom class.
3. Read references/troubleshooting.md when labels, paths, registries, or transforms do not line up.
4. Run scripts/inspect_dataset_config.py to inspect a config tree without building datasets or reading image files.

What this sub-skill covers

• CustomDataset, BaseDataset, ImageNet, KFoldDataset, and dataset wrappers.
• OpenMMLab 2.0 annotation files with metainfo and data_list.
• Common image pipelines with LoadImageFromFile, RandomResizedCrop, ResizeEdge, CenterCrop, RandAugment, and PackInputs.
• Registry registration for DATASETS, TRANSFORMS, MODELS, METRICS, HOOKS, and OPTIMIZERS.
• Custom dataset/model/metric patterns and project-module import rules.

Quick rule

If your samples only store img_path, the pipeline must load the image before augmentation. For classification-style pipelines, end with PackInputs.