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name: data-and-configs description: "Select, validate, and safely modify D-FINE dataset and YAML configs." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

D-FINE data and configs router

Use this sub-skill when the task is about D-FINE dataset layout, annotation sanity, choosing a COCO/Objects365/CrowdHuman/VOC/custom config, changing YAML fields, adjusting class counts, remap policy, batch size, input size, or doing a safe dataset/config preflight.

Start with references/data-and-configs.md. For failures, use references/troubleshooting.md. For COCO-format annotation checks, run the bundled validator in scripts/validate_detection_dataset.py.

Route here for

• Selecting a model-size config for COCO, Objects365, Objects365-to-COCO, Objects365-to-custom, CrowdHuman, VOC, or custom COCO-format data.
• Explaining how __include__, task, num_classes, remap_mscoco_category, dataloader fields, transforms, total_batch_size, and input size interact.
• Preparing a custom dataset config safely before training or evaluation.
• Preflighting Object365 remap/resize assumptions without running long or mutating dataset scripts.
• Diagnosing malformed COCO JSON, missing images/annotations, wrong category IDs, class-count mistakes, or batch-size/world-size divisibility errors.

Route away

• Training, evaluation, resume, tuning, checkpoints, DDP launch, AMP, or output directories: use ../training-evaluation/SKILL.md.
• PyTorch/ONNX/OpenVINO/TensorRT inference, export, or benchmark commands that reuse a config: use ../inference-export/SKILL.md.
• Model internals, registry construction, class-head mapping code, component changes, or deploy-mode behavior: use ../architecture-api/SKILL.md.

Quick operating loop

1. Identify the dataset family and desired model size (n, s, m, l, or x).
2. Pick the nearest config family from the catalog in references/data-and-configs.md.
3. For custom COCO-format data, set num_classes, remap_mscoco_category, train/val img_folder, and train/val ann_file before constructing a training command.
4. Validate annotations with python scripts/validate_detection_dataset.py --annotation <instances.json> --image-root <images-dir> --dataset-config <dataset-or-final-config.yml>.
5. If a workflow proceeds to launch training/evaluation or inference/export, hand off to the sibling sub-skill instead of continuing here.