> 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/yolo-world.md).

# YOLO-World

YOLO-World는 훈련 없이 임의의 텍스트 클래스 이름으로 객체를 감지하는 오픈 보캐브러러리 객체 탐지 모델입니다. 우리는 우리의 [Serverless Hosted API](/roboflow/roboflow-ko/deploy/serverless-hosted-api-v2.md).

YOLO-World 실행에 대한 자세한 내용은 [Inference 문서](https://inference.roboflow.com/).

## 코드 샘플

다음을 `/yolo_world/infer` 엔드포인트에 직접 호출합니다 `curl`:

```bash
curl --location 'https://serverless.roboflow.com/yolo_world/infer' \
  --header 'Content-Type: application/json' \
  --data '{
    "api_key": "YOUR_API_KEY",
    "image": {"type": "url", "value": "https://storage.googleapis.com/com-roboflow-marketing/notebooks/examples/cars-highway.png"},
    "text": ["car", "truck"],
    "yolo_world_version_id": "v2-s",
    "confidence": 0.05
  }'
```

SDK를 통해 동일한 호출을 할 수 있습니다. 이를 설치하고 [supervision](https://supervision.roboflow.com/):

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

사용자 지정 class\_names로 YOLO-World를 실행한 다음, supervision으로 예측을 디코딩하고 시각화합니다. 전달하세요 [Roboflow의 API Key](https://app.roboflow.com/settings/api) 를 `API_KEY` 환경 변수를 통해.

```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"),
)

results = client.infer_from_yolo_world(
    inference_input=image_path,
    class_names=["car", "truck"],
    model_version="v2-s",
    confidence=0.05,
)

detections = sv.Detections.from_inference(results[0])

image = cv2.imread(image_path)
labels = [
    f"{name} {conf:.2f}"
    for name, conf in zip(detections.data["class_name"], detections.confidence)
]
annotated = sv.BoxAnnotator().annotate(scene=image.copy(), detections=detections)
annotated = sv.LabelAnnotator().annotate(scene=annotated, detections=detections, labels=labels)
cv2.imwrite("annotated.png", annotated)
```

<figure><img src="/files/ca7b88c6c3015ed0d8264fa16d573601491ed9ae" alt=""><figcaption></figcaption></figure>

해당 `class_names` 인수는 모든 클래스 이름 목록을 허용합니다. 사용 가능한 `model_version` 값: `v2-s`, `v2-m`, `v2-l`, `v2-x`, `s`, `m`, `l`, `x`.

{% 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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