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yolo

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

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价格未确认★ 28 GitHub Stars目录更新于 · 2026年10月7日agent-skill

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Ultralytics YOLO

Run the same lifecycle on three complementary surfaces:

Ultralytics Platform — 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).

ul cloud train|predict|export upload local inputs as needed and run on Platform. Training returns after submission; add watch to follow and download results. ul cloud download uses the printed model URI and waits if needed; Ctrl-C stops waiting without canceling training. All four shortcuts need ultralytics installed; the ul cloud <resource> <operation> commands do not.

ul cloud train model=yolo26n.pt data=ul://username/datasets/helmets epochs=100 project=helmets name=exp1 # → run URI and download command
ul cloud download model=ul://username/helmets/exp1                                                       # → weights/best.pt and results
ul cloud predict model=ul://username/helmets/exp1 source=video.mp4                                       # → annotated output
ul cloud export model=ul://username/helmets/exp1 format=onnx                                             # → downloaded artifact

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.

The yolo CLI and Python share one API. The CLI grammar is yolo TASK MODE arg=value ..., and Python takes 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)

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

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

Open Platform and choose the data region during onboarding.
Clone a public dataset from Explore, or create one under Annotate and upload images, videos, an archive, or NDJSON.
Label in the fullscreen editor; use SAM or a compatible YOLO model in Smart mode where available.
Create a project, click New Model, select the dataset, pretrained model, GPU, and epochs, then monitor the run.
Use the completed model's Predict, Export, or Deploy tab.

Start with the 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 <resource> <operation>` commands, `ultralytics-platform`, scripted changes to resources, trash, deployments, and cloud runs	`platform-cli`

CLI specifics

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:

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 loads the file's values: CLI arguments before or after it 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*/sam3*/mobile_sam → SAM, FastSAM-* → FastSAM, yolo_nas_* → NAS, yoloe-*/*-world* → promptable YOLO (accepts classes="person, bus"), everything else → YOLO.

Global directives

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.
Always fine-tune from pretrained .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.
Use best.pt (not last.pt) from runs/<task>/<name>/weights/ after training.
After export, verify parity: yolo val the exported artifact against the .pt baseline.
Prefer built-ins over custom code: dataset converters and checkers (ultralytics.data), trackers, and Solutions modules replace whole categories of hand-written glue.
Trust the installed version over memory — if an argument is rejected, the API has moved: yolo cfg and the error text list valid arguments, and yolo checks shows the version. Prefer them over any table in these skills.

查看来源 ↗

文件元数据

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.

查看原始文本

---
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

Run the same lifecycle on 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`).

`ul cloud train|predict|export` upload local inputs as needed and run on Platform. Training returns after submission; add `watch` to follow and download results. `ul cloud download` uses the printed model URI and waits if needed; Ctrl-C stops waiting without canceling training. All four shortcuts need `ultralytics` installed; the `ul cloud <resource> <operation>` commands do not.

```bash
ul cloud train model=yolo26n.pt data=ul://username/datasets/helmets epochs=100 project=helmets name=exp1 # → run URI and download command
ul cloud download model=ul://username/helmets/exp1                                                       # → weights/best.pt and results
ul cloud predict model=ul://username/helmets/exp1 source=video.mp4                                       # → annotated output
ul cloud export model=ul://username/helmets/exp1 format=onnx                                             # → downloaded artifact
```

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.

The `yolo` CLI and Python share one API. The CLI grammar is `yolo TASK MODE arg=value ...`, and Python takes 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 <resource> <operation>` commands, `ultralytics-platform`, scripted changes to resources, trash, deployments, and cloud runs | `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` loads the file's values: CLI arguments before or after it 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*`/`sam3*`/`mobile_sam` → SAM, `FastSAM-*` → FastSAM, `yolo_nas_*` → NAS, `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, the API has moved: `yolo cfg` and the error text list valid arguments, and `yolo checks` shows the version. Prefer them over any table in these skills.

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许可证: AGPL-3.0

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
缺少 AI 审查批准
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 28 GitHub stars
Stars/forks activity: 28 stars, 0 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

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仓库元数据和审核信号仅供参考。受欢迎、已发现来源、成功运行是不同的事实。

来源仓库: ultralytics/skills
许可证: AGPL-3.0
版本: 0.5.0
最近 GitHub 推送: 2026年10月6日
目录更新于: 2026年10月7日
技能指令路径: skills/yolo/SKILL.md @ acfe53ce376f

版本来自目录元数据，使用前请核实来源发布记录。

质量

56/100

有潜力

信任

60/100

仅限沙盒

审计

72/100

需审查

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Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
缺少 AI 审查批准
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 28 GitHub stars
Stars/forks activity: 28 stars, 0 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

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    "risk": "Needs review"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "Low GitHub adoption signal",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "AI review approval is missing",
    "Quality score needs review"
  ],
  "agent_contract": {
    "task_input": "Use yolo in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 68/100 Manual review",
      "Audit: 72/100 Needs review",
      "Safety: 28/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "ultralytics-yolo (yolo)",
      "install_command": "",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "ultralytics-yolo",
      "task": "Use yolo in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/ultralytics-yolo",
    "api": "https://www.openagentskill.com/api/agent/skills/ultralytics-yolo",
    "audit": "https://www.openagentskill.com/skills/ultralytics-yolo/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=ultralytics-yolo&task=Use%20yolo%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20yolo%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20yolo%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/ultralytics-yolo/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/ultralytics-yolo"
  }
}

创作者工具

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此列表来自公开来源，维护者认领获批前不会标记为官方。

创作者: ultralytics
来源: ultralytics/skills
收录方: OpenAgentSkill 社区索引

归属链接指向公开仓库或创作者主页。创作者可认领列表以更新所有权信号。

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这条 Registry 收录列表归属于 ultralytics，但尚未标记为官方。认领后可增加已验证所有者信号，使后续发布、安装和审计更新更值得信赖。

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[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/ultralytics-yolo?metric=listed&label=Listed)](https://www.openagentskill.com/skills/ultralytics-yolo?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/ultralytics-yolo?metric=trust&label=Trust)](https://www.openagentskill.com/skills/ultralytics-yolo?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/ultralytics-yolo?metric=audit&label=Audit)](https://www.openagentskill.com/skills/ultralytics-yolo/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/ultralytics-yolo?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/ultralytics-yolo?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)

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