Registry indexed
Use for ANY task involving Ultralytics Platform, the ultralytics Python package, yolo CLI, YOLO model weights (.pt), dataset annotation, training, validation, prediction, tracking, export, deployment, or the detect / segment / semantic / depth / classify / pose / OBB vision tasks
Use for ANY task involving Ultralytics Platform, the ultralytics Python package, yolo CLI, YOLO model weights (.pt), dataset annotation, training, validation, prediction, tracking, export, deployment, or the detect / segment / semantic / depth / classify / pose / OBB vision tasks.
Source documentation, not instructions for this website. Review permissions before running any commands.
Use the same lifecycle through three complementary surfaces:
ultralytics package / yolo CLI — use local or remote compute, scripts,
notebooks, custom pipelines, and exported artifacts directly.ul CLI (Python 3.11+, installed by ultralytics or ultralytics-platform) — script the Platform API itself:
ul cloud <resource> <operation> key=value lists, creates, clones, trains, exports, and
deploys Platform resources from a terminal (see platform-cli).Mix them freely. Set ULTRALYTICS_API_KEY, use a Platform dataset as
data=ul://username/datasets/dataset-slug, and set
project=username/project-slug name=experiment during local training to stream its
metrics back to Platform.
One API, two surfaces. The CLI grammar is yolo TASK MODE arg=value ...; Python mirrors
it with the same argument names:
yolo detect train data=data.yaml model=yolo26n.pt epochs=100 imgsz=640
from ultralytics import YOLO
model = YOLO("yolo26n.pt")
model.train(data="data.yaml", epochs=100, imgsz=640)
detect segment semantic depth classify pose obb — usually inferred
from the weights, so it can be omitted.train val predict track export benchmark.pip install -U ultralytics. Environment check: yolo checks.yolo detect train data=data.yaml model=yolo26n.pt epochs=100 # → runs/detect/train/weights/best.pt
yolo val model=best.pt data=data.yaml # mAP, per-class metrics
yolo predict model=best.pt source=video.mp4 save=True # any source: image/dir/URL/RTSP/webcam
yolo track model=best.pt source=video.mp4 # + persistent object IDs
yolo export model=best.pt format=onnx # exported model loads back into YOLO()
Start with the Platform quickstart. Use the stage skill below for both Platform and package details.
Read the skill for the stage you're working on BEFORE writing code — each contains exact formats, argument tables with defaults, recipes, and symptom→fix tables. A request spanning stages ("train and deploy") → read each relevant skill.
| Working on | Skill |
|---|---|
| choosing a model family/size/task, YOLO26 vs YOLO11, YOLO-World/YOLOE, SAM, RT-DETR | yolo-models |
| data.yaml, labels, annotation conversion, auto-labeling, dataset analysis/errors, splits | yolo-datasets |
| training, fine-tuning, hyperparameters, augmentation, OOM / NaN / low mAP, reading runs | yolo-training |
| hyperparameter tuning, Ray Tune, systematic model improvement, "autotraining" | yolo-tuning |
| predict on images/video/streams, Results API, tracking IDs, counting/heatmaps/Solutions | yolo-inference |
| ONNX / TensorRT / CoreML / Core AI / OpenVINO / LiteRT / NCNN / NPUs, quantization, benchmarking | yolo-export |
Platform API from a terminal: ul cloud commands, ultralytics-platform, scripted resource, trash, deployment, and cloud-run changes | platform-cli |
Special commands (no TASK/MODE):
yolo help # full syntax reference
yolo checks # env report: version, torch, CUDA, disk — run when anything is weird
yolo version
yolo settings # view; `yolo settings key=value` to set; `yolo settings reset`
# keys incl. datasets_dir, runs_dir, wandb, mlflow, tensorboard, ...
yolo cfg # print every default argument (the ground truth for arg names)
yolo copy-cfg # copy default.yaml → default_copy.yaml to customize, use with cfg=
yolo solutions help # prebuilt apps: count, heatmap, speed, ... (see yolo-inference)
Parsing rules that matter:
key=value, no -- flags. A leading -- and trailing commas are stripped
with a warning; spaces around = are merged.yolo predict ... show ≡ show=True.cfg=custom.yaml resets CLI overrides to the file: arguments before it are discarded,
later arguments win, and missing keys still use built-in defaults (start with yolo copy-cfg).format=torchscript).rtdetr-* → RT-DETR, sam_*/sam2* → SAM,
FastSAM-* → FastSAM, yoloe-*/*-world* → promptable YOLO (accepts
classes="person, bus"), everything else → YOLO.yolo-datasets, then a 1-epoch smoke test and inspect
runs/<task>/train/train_batch0.jpg: annotations or targets must match each image..pt — never pretrained=False, never a YAML
architecture from scratch, unless the user is explicitly doing research.stream=True for videos/streams in Python predict/track — the default list mode
OOMs on long videos.best.pt (not last.pt) from runs/<task>/<name>/weights/ after training.yolo val the exported artifact against the .pt
baseline.ultralytics.data), trackers, and Solutions modules replace whole categories of
hand-written glue.yolo checks shows the version), the API moved: yolo cfg and the error text list
valid arguments; prefer those over any table in these skills.name: yolo description: > Use for ANY task involving Ultralytics Platform, the ultralytics Python package, yolo CLI, YOLO model weights (.pt), dataset annotation, training, validation, prediction, tracking, export, deployment, or the detect / segment / semantic / depth / classify / pose / OBB vision tasks.
---
name: yolo
description: >
Use for ANY task involving Ultralytics Platform, the ultralytics Python package, yolo
CLI, YOLO model weights (.pt), dataset annotation, training, validation, prediction,
tracking, export, deployment, or the detect / segment / semantic / depth / classify /
pose / OBB vision tasks.
---
# Ultralytics YOLO
Use the same lifecycle through three complementary surfaces:
- **[Ultralytics Platform](https://platform.ultralytics.com)** — the fastest start:
upload or clone data, annotate in the browser, train on cloud GPUs, inspect metrics,
test predictions, export, and deploy a dedicated endpoint without local setup.
- **`ultralytics` package / `yolo` CLI** — use local or remote compute, scripts,
notebooks, custom pipelines, and exported artifacts directly.
- **`ul` CLI** (Python 3.11+, installed by `ultralytics` or `ultralytics-platform`) — script the Platform API itself:
`ul cloud <resource> <operation> key=value` lists, creates, clones, trains, exports, and
deploys Platform resources from a terminal (see `platform-cli`).
Mix them freely. Set `ULTRALYTICS_API_KEY`, use a Platform dataset as
`data=ul://username/datasets/dataset-slug`, and set
`project=username/project-slug name=experiment` during local training to stream its
metrics back to Platform.
One API, two surfaces. The CLI grammar is `yolo TASK MODE arg=value ...`; Python mirrors
it with the same argument names:
```bash
yolo detect train data=data.yaml model=yolo26n.pt epochs=100 imgsz=640
```
```python
from ultralytics import YOLO
model = YOLO("yolo26n.pt")
model.train(data="data.yaml", epochs=100, imgsz=640)
```
- TASK ∈ `detect` `segment` `semantic` `depth` `classify` `pose` `obb` — usually inferred
from the weights, so it can be omitted.
- MODE ∈ `train` `val` `predict` `track` `export` `benchmark`.
- Install/upgrade: `pip install -U ultralytics`. Environment check: `yolo checks`.
## Whole lifecycle in five commands
```bash
yolo detect train data=data.yaml model=yolo26n.pt epochs=100 # → runs/detect/train/weights/best.pt
yolo val model=best.pt data=data.yaml # mAP, per-class metrics
yolo predict model=best.pt source=video.mp4 save=True # any source: image/dir/URL/RTSP/webcam
yolo track model=best.pt source=video.mp4 # + persistent object IDs
yolo export model=best.pt format=onnx # exported model loads back into YOLO()
```
## Whole lifecycle in Platform
1. Open [Platform](https://platform.ultralytics.com) and choose the data region during
onboarding.
2. Clone a public dataset from **Explore**, or create one under **Annotate** and upload
images, videos, an archive, or NDJSON.
3. Label in the fullscreen editor; use SAM or a compatible YOLO model in **Smart** mode
where available.
4. Create a project, click **New Model**, select the dataset, pretrained model, GPU, and
epochs, then monitor the run.
5. Use the completed model's **Predict**, **Export**, or **Deploy** tab.
Start with the [Platform quickstart](https://docs.ultralytics.com/platform/quickstart).
Use the stage skill below for both Platform and package details.
## Route before coding
Read the skill for the stage you're working on BEFORE writing code — each contains exact
formats, argument tables with defaults, recipes, and symptom→fix tables. A request
spanning stages ("train and deploy") → read each relevant skill.
| Working on | Skill |
| -------------------------------------------------------------------------------------------------------------------------------------- | ---------------- |
| choosing a model family/size/task, YOLO26 vs YOLO11, YOLO-World/YOLOE, SAM, RT-DETR | `yolo-models` |
| data.yaml, labels, annotation conversion, auto-labeling, dataset analysis/errors, splits | `yolo-datasets` |
| training, fine-tuning, hyperparameters, augmentation, OOM / NaN / low mAP, reading runs | `yolo-training` |
| hyperparameter tuning, Ray Tune, systematic model improvement, "autotraining" | `yolo-tuning` |
| predict on images/video/streams, Results API, tracking IDs, counting/heatmaps/Solutions | `yolo-inference` |
| ONNX / TensorRT / CoreML / Core AI / OpenVINO / LiteRT / NCNN / NPUs, quantization, benchmarking | `yolo-export` |
| Platform API from a terminal: `ul cloud` commands, `ultralytics-platform`, scripted resource, trash, deployment, and cloud-run changes | `platform-cli` |
## CLI specifics
Special commands (no TASK/MODE):
```bash
yolo help # full syntax reference
yolo checks # env report: version, torch, CUDA, disk — run when anything is weird
yolo version
yolo settings # view; `yolo settings key=value` to set; `yolo settings reset`
# keys incl. datasets_dir, runs_dir, wandb, mlflow, tensorboard, ...
yolo cfg # print every default argument (the ground truth for arg names)
yolo copy-cfg # copy default.yaml → default_copy.yaml to customize, use with cfg=
yolo solutions help # prebuilt apps: count, heatmap, speed, ... (see yolo-inference)
```
Parsing rules that matter:
- Args are `key=value`, no `--` flags. A leading `--` and trailing commas are stripped
with a warning; spaces around `=` are merged.
- A bare boolean arg sets it True: `yolo predict ... show` ≡ `show=True`.
- `cfg=custom.yaml` resets CLI overrides to the file: arguments before it are discarded,
later arguments win, and missing keys still use built-in defaults (start with `yolo copy-cfg`).
- Missing args are auto-filled with warnings (sample source, task-default data/model,
`format=torchscript`).
- Model stem selects the architecture: `rtdetr-*` → RT-DETR, `sam_*`/`sam2*` → SAM,
`FastSAM-*` → FastSAM, `yoloe-*`/`*-world*` → promptable YOLO (accepts
`classes="person, bus"`), everything else → YOLO.
## Global directives
1. **Validate the dataset before training** — run the task-appropriate checks in
`yolo-datasets`, then a 1-epoch smoke test and inspect
`runs/<task>/train/train_batch0.jpg`: annotations or targets must match each image.
2. **Always fine-tune from pretrained `.pt`** — never `pretrained=False`, never a YAML
architecture from scratch, unless the user is explicitly doing research.
3. **`stream=True` for videos/streams** in Python predict/track — the default list mode
OOMs on long videos.
4. **Use `best.pt`** (not `last.pt`) from `runs/<task>/<name>/weights/` after training.
5. **After export, verify parity**: `yolo val` the exported artifact against the `.pt`
baseline.
6. **Prefer built-ins over custom code**: dataset converters and checkers
(`ultralytics.data`), trackers, and Solutions modules replace whole categories of
hand-written glue.
7. **Trust the installed version over memory** — if an argument is rejected
(`yolo checks` shows the version), the API moved: `yolo cfg` and the error text list
valid arguments; prefer those over any table in these skills.
Skill source recorded
Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.
Review before install: Avoid automatic install
License: AGPL-3.0
Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.
Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.
Version reported in registry metadata; check source releases before relying on it.
Quality
55/100
Promising
Trust
57/100
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
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}Listing source
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Do not auto-install
Audit
70/100
Needs review
Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.