> 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/yolov5.md).

# YOLOv5

당사는 다음을 통해 YOLOv5 추론을 지원합니다 [Serverless Hosted API](/roboflow/roboflow-ko/deploy/serverless-hosted-api-v2.md). YOLOv5는 두 가지 작업 변형으로 제공됩니다:

* 객체 탐지
* 인스턴스 분할

{% hint style="info" %}
Roboflow에서 YOLOv5를 학습하는 것은 새 프로젝트의 경우 더 이상 지원되지 않습니다. 자체 YOLOv5 가중치를 Roboflow Project에 업로드하고 Serverless Hosted API를 대상으로 추론을 실행하는 것은 계속 지원됩니다.
{% endhint %}

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

YOLOv5 입력 크기는 모델을 학습할 때 설정됩니다(일반적인 값: 640x640 또는 1280x1280).

## 기본 COCO 별칭

Serverless Hosted API에서 YOLOv5에는 사전 학습된 COCO 별칭이 없습니다. YOLOv5 추론을 실행하려면, 다른 곳에서 모델을 학습하고, 가중치를 다음에 업로드한 뒤 [Roboflow Project](/roboflow/roboflow-ko/workspaces/key-concepts.md), 그리고 해당 `model_id` 및 `version`.

## 코드 샘플

SDK를 설치하고 [supervision](https://supervision.roboflow.com/) 시각화를 위해:

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

다음 값을 전달하세요 [Roboflow API Key](https://app.roboflow.com/settings/api) 를 `API_KEY` 환경 변수, 그리고 교체하세요 `model_id` 당신의 workspace, project, 및 version으로.

### 객체 탐지

```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)

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("output.png", annotated)
```

### 인스턴스 분할

```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)

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)

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("output.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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