> 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/roboflow-3.md).

# Roboflow 3.0

Roboflow 3.0은 Roboflow의 자체 모델 아키텍처입니다. 객체 탐지, 인스턴스 세분화, 분류, 키포인트 탐지를 지원합니다. Roboflow 플랫폼에서 Roboflow 3.0 모델을 학습하고, 다음을 통해 배포합니다 [Serverless Hosted API](/roboflow/roboflow-ko/deploy/serverless-hosted-api-v2.md).

자체 호스팅 배포에 대해서는 다음을 참조하세요 [Roboflow Inference](https://inference.roboflow.com/).

{% hint style="info" %}
Roboflow 3.0에는 공개 기본 COCO 별칭이 없습니다. 학습한 자체 모델은 다음을 사용하여 호출합니다 `project/version` 식별자 [Roboflow Project](https://app.roboflow.com/).
{% endhint %}

## 코드 샘플

다음 패키지를 설치하세요: [Inference SDK](https://inference.roboflow.com/) 및 [supervision](https://supervision.roboflow.com/):

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

아래 샘플은 여러분이 학습한 Roboflow 3.0 모델에 대해 inference를 실행합니다. 다음을 바꾸세요 `your-project/1` 를 모델 URL과 버전으로. 다음을 전달하세요 [Roboflow API Key](https://app.roboflow.com/settings/api) 다음을 통해 `API_KEY` 환경 변수입니다.

### 객체 탐지

```python
import os
import cv2
import urllib.request
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}"
    for class_name, confidence
    in zip(detections.data.get("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("output.png", annotated)
```

### 인스턴스 세분화

```python
import os
import cv2
import urllib.request
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)

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}"
    for class_name, confidence
    in zip(detections.data.get("class_name", []), detections.confidence)
]

annotated = sv.MaskAnnotator().annotate(scene=image.copy(), detections=detections)
annotated = sv.LabelAnnotator().annotate(scene=annotated, detections=detections, labels=labels)
cv2.imwrite("output.png", annotated)
```

### 키포인트 탐지

```python
import os
import cv2
import urllib.request
import supervision as sv
from inference_sdk import InferenceHTTPClient

image_url = "https://storage.googleapis.com/com-roboflow-marketing/notebooks/examples/person-walking.png"
image_path = "person-walking.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")

key_points = sv.KeyPoints.from_inference(result)

annotated = sv.EdgeAnnotator(color=sv.Color.BLUE, thickness=2).annotate(
    scene=image.copy(), key_points=key_points
)
annotated = sv.VertexAnnotator(color=sv.Color.GREEN, radius=5).annotate(
    scene=annotated, key_points=key_points
)
cv2.imwrite("output.png", annotated)
```

### Classification

분류 응답에는 신뢰도를 포함한 클래스 예측 목록이 들어 있으므로 시각화는 적용할 수 없습니다. 응답에서 상위 클래스를 직접 읽으세요.

```python
import os
import cv2
import urllib.request
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")

print(f"Top class: {result['top']} ({result['confidence']:.4f})")
```

{% hint style="info" %}
설정 `api_url` 을 배포 대상에 맞게 다음으로 설정하세요:

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


---

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