> 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/rf-detr.md).

# RF-DETR

RF-DETR은 Roboflow의 transformer 기반 실시간 객체 탐지, segmentation, keypoint detection 모델입니다. COCO로 사전 학습된 RF-DETR 체크포인트에 대해 추론을 수행하려면 다음을 통해 [Serverless Hosted API](/roboflow/roboflow-ko/deploy/serverless-hosted-api-v2.md), 또는 다음을 사용해 자체 호스팅 [Roboflow Inference](https://inference.roboflow.com/).

## 코드 샘플

아래 예제는 RF-DETR을 다음을 통해 실행합니다 [Serverless Hosted API](/roboflow/roboflow-ko/deploy/serverless-hosted-api-v2.md) 그리고 결과를 다음으로 시각화합니다 [supervision](https://supervision.roboflow.com/). 전달하세요 [Roboflow의 API Key](https://app.roboflow.com/settings/api) 를 `API_KEY` 환경 변수를 통해.

### 객체 탐지

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

```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"),
)
result = client.infer(image_path, model_id="rfdetr-base")

image = cv2.imread(image_path)
detections = sv.Detections.from_inference(result)

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/239c9dfcee05cce618ecdf2628a3bf93261c6911" alt=""><figcaption></figcaption></figure>

### 인스턴스 분할

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

```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"),
)
result = client.infer(image_path, model_id="rfdetr-seg-preview")

image = cv2.imread(image_path)
detections = sv.Detections.from_inference(result)

mask_annotator = sv.MaskAnnotator()
label_annotator = sv.LabelAnnotator()

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/b58691840a6a1da1912ed8f990b16415ce06f346" alt=""><figcaption></figcaption></figure>

## 기본 COCO 학습 모델

다음 별칭 중 하나를 전달하세요 `model_id` 추론을 실행할 때. SDK는 각 별칭을 해당하는 Roboflow 프로젝트 버전으로 해석합니다. 입력 해상도는 변형에 따라 다릅니다.

| 별칭                 | 작업      | 입력 크기   |
| ------------------ | ------- | ------- |
| rfdetr-base        | 객체 감지   | 560x560 |
| rfdetr-nano        | 객체 감지   | 384x384 |
| rfdetr-small       | 객체 감지   | 512x512 |
| rfdetr-medium      | 객체 감지   | 576x576 |
| rfdetr-large       | 객체 감지   | 704x704 |
| rfdetr-xlarge      | 객체 감지   | 700x700 |
| rfdetr-2xlarge     | 객체 감지   | 880x880 |
| rfdetr-seg-preview | 인스턴스 분할 | 560x560 |
| rfdetr-seg-nano    | 인스턴스 분할 | 312x312 |
| rfdetr-seg-small   | 인스턴스 분할 | 384x384 |
| rfdetr-seg-medium  | 인스턴스 분할 | 432x432 |
| rfdetr-seg-large   | 인스턴스 분할 | 504x504 |
| rfdetr-seg-xlarge  | 인스턴스 분할 | 624x624 |
| rfdetr-seg-2xlarge | 인스턴스 분할 | 768x768 |

{% 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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