---

name: training-and-validation description: "Plan and troubleshoot Ultralytics YOLO training, validation, resume, tuning, device selection, trainer/validator ownership, and metrics workflows." disable-model-invocation: true metadata: disco-role: operating license: AGPL 3.0

Training and Validation

Use this sub-skill when the user needs to train or validate Ultralytics models through model.train(), model.val(), model.tune(), yolo train, or yolo val, including resume behavior, CPU/GPU/MPS device choices, and metric interpretation.

Route Here For

• Creating safe train or validation commands with yolo TASK MODE arg=value or Python YOLO(...).train()/val()/tune().
• Resuming interrupted training from last.pt, diagnosing missing or non-resumable checkpoint state, or choosing between resume and a fresh fine-tune.
• Selecting device, batch, workers, amp, compile, cache, fraction, patience, optimizer, and tuning options for constrained hardware.
• Reading validation outputs such as metrics.box.map, metrics.box.map50, metrics.confusion_matrix, to_df(), to_csv(), and to_json().
• Explaining trainer/validator ownership, callbacks, result directories, checkpoints, and where side effects are written.

Do Not Handle Here

• Dataset YAML layout, class names, custom data paths, and config-file authoring; route to ../data-and-configuration/SKILL.md.
• Choosing model families, task names, or checkpoint variants; route to ../model-families-and-tasks/SKILL.md.
• Prediction result objects, streaming inference, saving inference media, or post-processing; route to ../inference-and-results/SKILL.md.
• Export, deployment, benchmark formats, TensorRT/ONNX conversion, or serving; route to ../export-and-deployment/SKILL.md.
• Tracking and solutions workflows; route to ../tracking-and-solutions/SKILL.md.
• Repository development, source tests, or maintainer workflows; route to ../repo-development/SKILL.md.

Start With These References

• references/workflows.md for train, validation, resume, tuning, device, and metrics patterns.
• references/api-reference.md for Python API/CLI argument shape and trainer/validator ownership.
• references/troubleshooting.md for install/import, CLI syntax, data/config, backend/device, network/download, checkpoint, and side-effect failures.
• scripts/plan_training_run.py to generate safe CPU/tiny-fixture train/val/tune command plans without executing training.

Safe Planning Defaults

Prefer a dry plan before running any training. For smoke tests or examples, default to device=cpu, imgsz=32, epochs=1, batch=1, workers=0, plots=False, and a tiny dataset such as coco8.yaml only when a download is acceptable. If the user wants to avoid downloads, require a local dataset YAML or prepared fixture and route data-layout checks to data-and-configuration.

Common Command Shapes

yolo detect train model=yolo26n.pt data=coco8.yaml epochs=1 imgsz=32 device=cpu batch=1 workers=0 plots=False
yolo detect val model=yolo26n.pt data=coco8.yaml imgsz=32 device=cpu batch=1 plots=False
python scripts/plan_training_run.py --mode train --task detect --model yolo26n.pt --data coco8.yaml --cpu-tiny

from ultralytics import YOLO

model = YOLO("yolo26n.pt")
metrics = model.train(data="coco8.yaml", epochs=1, imgsz=32, device="cpu", batch=1, workers=0, plots=False)
metrics = model.val(data="coco8.yaml", imgsz=32, device="cpu", batch=1, plots=False)

Handoff Notes

Training and validation create run directories and may download model weights or datasets when names such as yolo26n.pt or coco8.yaml are not already available. Always surface those side effects before execution, and use the bundled planner script when the user asks for a safe plan rather than an actual run.