---

name: training description: "Plan and debug YOLOv3 training, custom dataset YAMLs, checkpoints, hyperparameters, resume, DDP, and CPU smoke commands." disable-model-invocation: true metadata: disco-role: operating license: AGPL 3.0

Training Sub-skill

Read this for train.py, custom object-detection datasets, data/*.yaml, data/hyps/*.yaml, pretrained versus scratch training, resume/evolve/freeze/DDP flags, and training smoke plans.

Use

• Read references/workflows.md for training command patterns and dataset layout.
• Use scripts/yolov3_command_builder.py to build reproducible train commands without running them.
• Use scripts/yolov3_dataset_yaml_check.py before training when dataset paths or class names are uncertain.
• Read references/troubleshooting.md for dataset, checkpoint, logging, and DDP issues.

Important facts

• Main entry point: python train.py.
• Defaults: --weights yolov3-tiny.pt, --data data/coco128.yaml, --hyp data/hyps/hyp.scratch-low.yaml, --epochs 100, --batch-size 16, --imgsz 640.
• CPU smoke used by repo guidance: tiny model, image size 64, one epoch, --device cpu, --name smoke, --exist-ok.
• Dataset YAML needs train, val, and names; if nc is present it must match the number of names. Optional download commands can have network side effects.