Ultralytics Docs

Ultralytics Docs

Official Ultralytics YOLO docs synced from ultralytics/ultralytics/docs.

Use this skill for Ultralytics YOLO models, tasks, modes, datasets, Python API, integrations, HUB/platform docs, solutions, deployment/export formats, and YOLOv5 legacy docs.

Directory Optimization

The upstream docs tree contains English content in docs/en/, reusable MkDocs/Jinja snippets in docs/macros/, and theme/static overrides under docs/overrides/. This skill intentionally syncs:

• docs/en/** to references/** without the en/ prefix.
• docs/macros/** to references/macros/** because it contains shared argument tables used by train/predict/export/track/solution pages.
• No docs/overrides/**, static assets, or local-build documentation noise.

Hard Rules

• MUST search references/ before giving version-sensitive Ultralytics YOLO model, CLI, Python API, dataset format, training, validation, prediction, export, tracking, or HUB/platform guidance.
• MUST distinguish Ultralytics package behavior from generic PyTorch, OpenCV, ONNX Runtime, TensorRT, or Hugging Face guidance.
• MUST call out model family and task scope when relevant: YOLO26, YOLO11, YOLOv8, YOLOv5, SAM/FastSAM, RT-DETR, YOLO-World, detect, segment, classify, pose, OBB, or track.
• NEVER invent mode arguments, dataset YAML keys, export formats, model filenames, result object attributes, or API signatures without checking references.

Fast Lookup

rg -n "YOLO26|YOLO11|YOLOv8|YOLOv5|RT-DETR|SAM|FastSAM|YOLO-World" skills/ultralytics-docs/references
rg -n "train|predict|val|export|track|benchmark|Results|Boxes|Masks|Keypoints|OBB" skills/ultralytics-docs/references
rg -n "dataset.yaml|data.yaml|names:|nc:|detect|segment|classify|pose|obb" skills/ultralytics-docs/references
rg -n "ONNX|TensorRT|OpenVINO|CoreML|TFLite|Edge TPU|HUB|Platform" skills/ultralytics-docs/references

Reference Map

• references/quickstart.md — installation and first-use entry point.
• references/models/ — model families including YOLO26, YOLO11, YOLOv8, YOLOv5, SAM/FastSAM, RT-DETR, YOLO-World.
• references/tasks/ — task guides for detection, segmentation, classification, pose, OBB, and tracking.
• references/modes/ — train, predict, validate, export, track, benchmark, and tuning modes.
• references/datasets/ — dataset format and dataset-specific YAML guidance.
• references/reference/ — Python API reference stubs for ultralytics package modules.
• references/macros/ — shared argument tables and performance tables included by upstream docs.

Workflow

1. Identify whether the question is model selection, task/data format, mode arguments, Python API, export/deployment, HUB/platform, integration, or solution workflow.
2. Search the relevant subtree and references/macros/ for argument tables.
3. Prefer exact documented argument names, model filenames, dataset YAML fields, result attributes, and export backend limitations.
4. Route generic deep learning, PyTorch runtime, CUDA, ONNX Runtime, TensorRT, or OpenCV questions to more specific docs when Ultralytics docs are not the source of truth.