---

name: training-data description: "Routes COCO/VOC annotation preparation, class and anchor config, dataset validation, and training workflows for tensorflow-yolov4-tflite." disable-model-invocation: true metadata: disco-role: operating license: MIT

Training Data and Training

Use this sub-skill when the user needs to prepare annotations, validate class files, adapt COCO/VOC data, change train/test config, or run the repository's YOLOv3/YOLOv4 training loop.

Before changing data or training

• Read references/data-formats.md to confirm the annotation line format and class-id expectations.
• Use scripts/validate_annotation_line.py on a tiny sample before launching training or evaluation.
• Confirm the target checkout root is the command working directory. Default paths in core.config.cfg are relative to that root.
• Confirm class-file order, anchors, model family, and tiny/full choice before loading or converting weights.

Main routes

1. Validate converted annotations: run the bundled validator and fix malformed lines before touching TensorFlow.
2. COCO preparation: read references/workflows.md for the repo's JSON-to-pickle and pickle-to-annotation sequence.
3. VOC preparation: read references/workflows.md for the XML-to-annotation flow.
4. Training configuration: read references/workflows.md for cfg.TRAIN values, freeze stages, checkpoints, and scratch/transfer caveats.
5. Training troubleshooting: use references/troubleshooting.md for path, class-count, NumPy, dataset, and weight-loading failures.

Validator examples

Validate one converted annotation line:

python sub-skills/training-data/scripts/validate_annotation_line.py \
  --classes data/classes/coco.names \
  --line "image.jpg 10,20,100,200,0"

Validate a file and require image paths to exist:

python sub-skills/training-data/scripts/validate_annotation_line.py \
  --classes data/classes/coco.names \
  --annotation-file data/dataset/val2017.txt \
  --check-images

Do not use the repository's source data/dataset/*.txt files as portable training data; they contain source-author absolute image paths and are best used only as format examples.

Handoff to other sub-skills

• After custom training or weight selection, use ../model-conversion/SKILL.md to export a SavedModel/TFLite/TF-TRT artifact.
• After an artifact exists, use ../inference-evaluation/SKILL.md for image checks, mAP, and benchmarks.
• For Android deployment, use ../android-deployment/SKILL.md only after a validated TFLite artifact and class labels are ready.

Stop conditions

Stop before downloading COCO, running full training, modifying config for custom class counts, or overwriting checkpoints unless the user explicitly approves the runtime, data, and output policy.