> For the complete documentation index, see [llms.txt](https://docs.roboflow.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.roboflow.com/roboflow/roboflow-jp/deploy/supported-models/yolo11.md).

# YOLO11

当社の [Serverless Hosted API](/roboflow/roboflow-jp/deploy/serverless-hosted-api-v2.md). YOLO11 は COCO で事前学習された 2 つのタスクバリアントで利用できます:

* 物体検出（入力サイズ 640 と 1280）
* インスタンスセグメンテーション（入力サイズ 640）

セルフホスト型デプロイについては、 [Roboflow Inference](https://inference.roboflow.com/).

## コードサンプル

依存関係をインストールします:

```bash
pip install inference-sdk supervision
```

以下の各サンプルは Serverless Hosted API を介して推論を実行し、応答を [`supervision`](https://supervision.roboflow.com/)でデコードし、注釈付き画像をディスクに書き出します。あなたの [Roboflow API Key](https://app.roboflow.com/settings/api) を `API_KEY` 環境変数。

### Object detection

実行 `yolov11n-640` サンプル画像に適用し、バウンディングボックスとラベルを注釈します。

```python
import os
import urllib.request

import cv2
import supervision as sv
from inference_sdk import InferenceHTTPClient

image_url = "https://storage.googleapis.com/com-roboflow-marketing/notebooks/examples/cars-highway.png"
image_path = "cars-highway.png"
urllib.request.urlretrieve(image_url, image_path)

client = InferenceHTTPClient(
    api_url="https://serverless.roboflow.com",
    api_key=os.getenv("API_KEY"),
)

image = cv2.imread(image_path)
results = client.infer(image, model_id="yolov11n-640")
detections = sv.Detections.from_inference(results)

box_annotator = sv.BoxAnnotator()
label_annotator = sv.LabelAnnotator()

annotated = box_annotator.annotate(scene=image.copy(), detections=detections)
annotated = label_annotator.annotate(scene=annotated, detections=detections)

cv2.imwrite("cars-highway-annotated.png", annotated)
```

<figure><img src="/files/d3cfba5914053d3a03829a0b52c449635324f723" alt=""><figcaption></figcaption></figure>

### Instance segmentation

実行 `yolov11n-seg-640` サンプル画像に適用し、マスクとラベルを注釈します。

```python
import os
import urllib.request

import cv2
import supervision as sv
from inference_sdk import InferenceHTTPClient

image_url = "https://storage.googleapis.com/com-roboflow-marketing/notebooks/examples/bicycle.png"
image_path = "bicycle.png"
urllib.request.urlretrieve(image_url, image_path)

client = InferenceHTTPClient(
    api_url="https://serverless.roboflow.com",
    api_key=os.getenv("API_KEY"),
)

image = cv2.imread(image_path)
results = client.infer(image, model_id="yolov11n-seg-640")
detections = sv.Detections.from_inference(results)

mask_annotator = sv.MaskAnnotator()
label_annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER_OF_MASS)

annotated = mask_annotator.annotate(scene=image.copy(), detections=detections)
annotated = label_annotator.annotate(scene=annotated, detections=detections)

cv2.imwrite("bicycle-annotated.png", annotated)
```

<figure><img src="/files/02297aef2de810407c9166a775c3c84a817f1d85" alt=""><figcaption></figcaption></figure>

## デフォルトの COCO エイリアス

次のいずれかのエイリアスを `model_id`として渡すことができます。 `inference-sdk` 各エイリアスを事前学習済みの Roboflow Universe モデルに解決します。

| タスク       | エイリアス              |
| --------- | ------------------ |
| Detection | `yolov11n-640`     |
| Detection | `yolov11s-640`     |
| Detection | `yolov11m-640`     |
| Detection | `yolov11l-640`     |
| Detection | `yolov11x-640`     |
| Detection | `yolov11n-1280`    |
| Detection | `yolov11s-1280`    |
| Detection | `yolov11m-1280`    |
| Detection | `yolov11l-1280`    |
| Detection | `yolov11x-1280`    |
| セグメンテーション | `yolov11n-seg-640` |
| セグメンテーション | `yolov11s-seg-640` |
| セグメンテーション | `yolov11m-seg-640` |
| セグメンテーション | `yolov11l-seg-640` |
| セグメンテーション | `yolov11x-seg-640` |

この `yolo11*` プレフィックスのバリアントは同じモデルに解決されます。

{% hint style="info" %}
設定 `api_url` をデプロイ先に合わせてください:

* `https://serverless.roboflow.com` は Serverless Hosted API 用です。
* `http://localhost:9001` ローカル [Inference](https://inference.roboflow.com/) サーバー用です。
* あなたの [Dedicated Deployment](/roboflow/roboflow-jp/deploy/dedicated-deployments.md) の URL はプライベートエンドポイント用です。
  {% endhint %}


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