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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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Harga belum dikonfirmasi★ 28 Star GitHubDirektori diperbarui · 7 Okt 2026agent-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.

Lihat sumber ↗

Metadata berkas

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.

Lihat teks asli

---
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.

Tinjau sumber

Harga dan biaya penggunaan

Dapatkan skill: Harga belum dikonfirmasi
Jalankan: Persyaratan belum dikonfirmasi. Periksa biaya agen, API, dan layanan di sumbernya.
Lisensi: AGPL-3.0
Harga belum dikonfirmasi: Harga belum dikonfirmasi. Tautan sumber dan instalasi yang ada tetap tersedia.

Gratis diperoleh bukan berarti gratis dijalankan. Harga bukan penilaian keamanan. Kirim informasi harga →

Sumber perlu ditinjau

Sumber berubah atau gagal disinkronkan. Tinjau sumber terbaru sebelum memasang.

Tinjau sebelum memasang: Hindari pemasangan otomatis

Lisensi: AGPL-3.0

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
Persetujuan tinjauan AI belum ada
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

Buka audit lengkap

Daftar alat adalah petunjuk metadata, bukan kompatibilitas teruji. Prompt adalah saran.

Mulai dengan tugas kecil

1Baca sumber dan pastikan masukan, keluaran, dependensi, serta izin.
2Minta rencana dari agent. Setujui pengaturan dan biaya sebelum uji terisolasi.
3Periksa hasil dan berkas yang berubah. Laporkan hanya yang dijalankan dan simpan revisi sumber.

Periksa dependensi, kunci API, dan biaya layanan pihak ketiga pada sumber. Repositori publik tidak berarti semua layanan gratis.

Sumber dan catatan penggunaan

Terindeks

Metadata dan tinjauan bersifat saran. Popularitas, penemuan sumber, dan keberhasilan eksekusi adalah fakta berbeda.

Repositori sumber: ultralytics/skills
Lisensi: AGPL-3.0
Versi: 0.5.0
Push GitHub terakhir: 6 Okt 2026
Direktori diperbarui: 7 Okt 2026
Jalur instruksi: skills/yolo/SKILL.md @ acfe53ce376f

Versi dilaporkan dalam metadata direktori; periksa rilis sumber.

Kualitas

56/100

Menjanjikan

Kepercayaan

60/100

Hanya sandbox

Audit

72/100

Perlu ditinjau

Buka audit lengkap Lihat laporan evaluasi

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
Persetujuan tinjauan AI belum ada
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

Verified installs: —
Hasil: —

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Detail lainnya

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  "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"
  }
}

Untuk kreator

Sumber listing

Diindeks Registry

Dapat diklaim

Listing ini diindeks dari sumber publik dan belum ditandai resmi hingga klaim pemelihara disetujui.

Kreator: ultralytics
Sumber: ultralytics/skills
Diindeks oleh: Indeks komunitas OpenAgentSkill

Atribusi menautkan ke repositori publik atau profil kreator. Kreator dapat mengklaim listing untuk memperbarui sinyal kepemilikan.

Klaim skill ini

Klaim pemilik

Klaim listing skill ini

Listing Diindeks Registry ini dikaitkan dengan ultralytics, tetapi belum ditandai resmi. Klaim untuk menambahkan sinyal pemilik terverifikasi dan membuat pembaruan peluncuran, pemasangan, serta audit berikutnya lebih tepercaya.

Kit berbagi

Kit backlink kreator

Tambahkan badge bukti ke README Anda

Tampilkan listing kanonis, sinyal kepercayaan dan audit saat ini, serta bukti Agent-Proven nyata di tempat pengembang mengevaluasi repositori.

[![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)

Pratinjau badge Buka audit Kit kreator

Sinyal komunitas

Bagikan apakah skill ini bermanfaat untuk alur kerja Agent Anda. Masukan gabungan meningkatkan peringkat dari waktu ke waktu.