---

name: training description: "Routes UltralyticsPro training workflows for YOLO and RT-DETR presets, including detection, classification, segmentation, pose, and oriented bounding-box examples." disable-model-invocation: true metadata: disco-role: operating license: NO_LICENSE

Training

Use this sub-skill when the user wants to train, finetune, or resume a model using one of the repository's training wrappers.

Typical triggers

• "train YOLOv11 on coco128"
• "run the classification example"
• "use the RT-DETR training script"
• "switch the model preset or device"
• "why does train_yolo12.py fail on this machine?"

What belongs here

• The training wrappers that mirror train_v8.py, train_v8_linux.py, train_yolo11.py, train_yolov10.py, train_yolo12.py, train_cls.py, train_obb.py, train_pose.py, train_seg01.py, and train_rtdetr.py.
• Preset selection, model-config path selection, dataset YAML selection, and device overrides for those training examples.
• Safe dry-run planning before a real training job starts.
• Troubleshooting for missing packaged configs, missing dataset YAMLs, device selection, and first-run downloads.

What stays out

• Single-image prediction workflows. Use sub-skills/prediction instead.
• Upstream library source changes. This repo skill only wraps the public ultralytics package.
• Long benchmark or large-scale training sweeps.

First reads

1. references/workflows.md for the preset-to-script map and the canonical command forms.
2. references/presets.md when you need to translate a source script name into the bundled training preset name.
3. references/troubleshooting.md for data, config, device, and download failures.
4. ../../references/interface-reference.md when you need verified Ultralytics API or CLI details.
5. ../../references/model-family-map.md when you need the broader source script inventory.

Bundled helper

• scripts/run_train.py — preferred wrapper for all training presets. It is safe by default and performs a dry run unless --execute is supplied.

How to use the helper

• Start with --list-presets when you are mapping a source script to the bundled preset.
• Use --preset train-yolo11 or another preset when you want the same model and dataset pair as a source example.
• Override --model, --data, --imgsz, --batch, --workers, --device, --project, or --name when a user asks for a variation.
• Pass --execute only after confirming that the model config and dataset are available and that the user is willing to start a real training job.

Common decisions

• If the path is cfg_yolov12/yolo12.yaml, the preset is a custom local config case. The verified public Ultralytics install used for authoring does not ship that file, so the user must supply it or choose a different preset.
• If the user only needs to inspect the parameters, stay in dry-run mode and read the printed plan instead of launching training.
• If the requested run is meant for CPU, choose the preset or override that sets device=cpu explicitly.

When to escalate to the root skill

Go back to SKILL.md if the task turns into general package inspection, installation, or route selection across training and prediction.