---

name: data-preparation description: "Prepares YOLO annotation lists, class-name files, anchors, and Pascal VOC conversion inputs for dataset setup, label validation, and training-input hygiene." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Data Preparation

Use this sub-skill when the request is about preparing or checking dataset inputs for this repo:

• YOLO annotation rows and split files
• *.names class files
• anchor files and anchor-shape sanity checks
• Pascal VOC trees and annotation conversion
• label-quality troubleshooting before training or evaluation

What it covers

• Annotation schema: image_path xmin,ymin,xmax,ymax,class_id ...
• Class ids are zero-based line numbers in the chosen class file.
• Anchor files must provide 18 numeric values that reshape to (3, 3, 2).
• VOC conversion assumes the repo's train/VOCdevkit/... and test/VOCdevkit/... tree layout.
• core/dataset.py expects cfg.YOLO.CLASSES, cfg.YOLO.ANCHORS, cfg.TRAIN.ANNOT_PATH, and cfg.TEST.ANNOT_PATH to stay aligned.
• The bundled validator at scripts/validate_yolo_annotations.py checks rows, optional class and anchor files, and optional image existence.

Use the bundled references

• Data formats
• VOC workflow
• Troubleshooting

Route away when needed

This sub-skill does not run training, frozen-graph export, image/video inference, or mAP evaluation. Hand off those requests to the sibling sub-skills that own them.

Run the validator before training whenever label files, class files, anchor files, or image roots change.