> 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-ko/deploy/supported-models/yolo11.md).

# YOLO11

저희는 다음을 통해 YOLO11 추론을 지원합니다 [Serverless Hosted API](/roboflow/roboflow-ko/deploy/serverless-hosted-api-v2.md). YOLO11은 COCO에서 사전 학습된 두 가지 작업 변형으로 제공됩니다:

* 객체 탐지(640 및 1280 입력 크기)
* 인스턴스 분할(640 입력 크기)

셀프 호스팅 배포는 다음을 참조하세요 [Roboflow Inference](https://inference.roboflow.com/).

## 코드 샘플

의존성을 설치하세요:

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

아래 각 예제는 Serverless Hosted API를 통해 추론을 실행하고, 응답을 다음으로 디코딩합니다 [`supervision`](https://supervision.roboflow.com/), 그리고 주석이 달린 이미지를 디스크에 저장합니다. 다음을 전달하세요: [Roboflow API Key](https://app.roboflow.com/settings/api) 를 `API_KEY` 환경 변수.

### 객체 탐지

실행 `yolov11n-640` 샘플 이미지에서 실행하고 박스와 레이블에 주석을 추가합니다.

```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/a9f89425ab395d5afbd859b41b9c9e366810d012" alt=""><figcaption></figcaption></figure>

### 인스턴스 분할

실행 `yolov11n-seg-640` 샘플 이미지에서 실행하고 마스크와 레이블에 주석을 추가합니다.

```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/03448525b12b04bbb55d49d432d108e6992929fc" alt=""><figcaption></figcaption></figure>

## 기본 COCO 별칭

다음 별칭 중 하나를 다음으로 전달할 수 있습니다 `model_id`. 이 `inference-sdk` 각 별칭을 사전 학습된 Roboflow Universe 모델로 매핑합니다.

| 작업 | 별칭                 |
| -- | ------------------ |
| 감지 | `yolov11n-640`     |
| 감지 | `yolov11s-640`     |
| 감지 | `yolov11m-640`     |
| 감지 | `yolov11l-640`     |
| 감지 | `yolov11x-640`     |
| 감지 | `yolov11n-1280`    |
| 감지 | `yolov11s-1280`    |
| 감지 | `yolov11m-1280`    |
| 감지 | `yolov11l-1280`    |
| 감지 | `yolov11x-1280`    |
| 분할 | `yolov11n-seg-640` |
| 분할 | `yolov11s-seg-640` |
| 분할 | `yolov11m-seg-640` |
| 분할 | `yolov11l-seg-640` |
| 분할 | `yolov11x-seg-640` |

해당 `yolo11*` 접두사 변형은 동일한 모델로 해석됩니다.

{% hint style="info" %}
설정하세요 `api_url` 를 배포 대상에 맞게:

* `https://serverless.roboflow.com` Serverless Hosted API용.
* `http://localhost:9001` 로컬 [Inference](https://inference.roboflow.com/) 서버용.
* 귀하의 [Dedicated Deployment](/roboflow/roboflow-ko/deploy/dedicated-deployments.md) 개인 엔드포인트용 URL.
  {% endhint %}


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