> 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/yolov12.md).

# YOLOv12

우리는 YOLOv12 객체 탐지 추론을 지원합니다 [Serverless Hosted API](/roboflow/roboflow-ko/deploy/serverless-hosted-api-v2.md). YOLOv12는 다섯 가지 크기(`n`, `s`, `m`, `l`, `x`) 객체 탐지 작업을 위해.

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

YOLOv12 입력 크기는 Roboflow에서 모델을 학습할 때 설정됩니다(일반적인 값: 640x640 또는 1280x1280).

{% hint style="info" %}
YOLOv12는 Serverless Hosted API에 사전 학습된 COCO 별칭과 함께 제공되지 않습니다. YOLOv12를 사용하려면 Roboflow에서 자체 모델을 학습하고, 다음 형식의 모델 URL을 사용해 호출하세요 `your-project/version`.
{% endhint %}

## 코드 샘플

설치하세요. [Inference SDK](https://inference.roboflow.com/inference_helpers/inference_sdk/) 및 [supervision](https://supervision.roboflow.com/) 예측을 디코딩하고 시각화하기 위해:

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

학습한 YOLOv12 모델로 탐지를 실행한 다음, 응답을 다음으로 디코딩하세요: `sv.Detections.from_inference`, 바운딩 박스와 레이블을 그리고, 주석이 추가된 이미지를 저장하세요. 다음을 전달하세요 [Roboflow API Key](https://app.roboflow.com/settings/api) 를 `API_KEY` 환경 변수에 전달하고, 다음을 바꾸세요 `model_id` 자신의 `project/version`.

```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)

image = cv2.imread(image_path)

client = InferenceHTTPClient(
    api_url="https://serverless.roboflow.com",
    api_key=os.getenv("API_KEY"),
)
result = client.infer(image, model_id="your-project/1")

detections = sv.Detections.from_inference(result)

labels = [
    f"{class_name} {confidence:.2f}"
    class_name, confidence에 대해
    in zip(detections.data.get("class_name", []), detections.confidence)
]

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, labels=labels)

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

{% 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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