> 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/yolov7.md).

# YOLOv7

저희를 통해 YOLOv7 instance segmentation 추론을 지원합니다 [Serverless Hosted API](/roboflow/roboflow-ko/deploy/serverless-hosted-api-v2.md). YOLOv7 학습은 Roboflow에서 지원되지 않지만, 대신 [자신의 가중치를 업로드할 수 있습니다](/roboflow/roboflow-ko/deploy/upload-custom-weights.md) 그리고 그것들에 대해 추론을 실행할 수 있습니다.

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

YOLOv7 입력 크기는 Roboflow 외부에서 모델을 학습할 때 설정됩니다(일반적인 값: 640x640 또는 1280x1280).

## 기본 COCO 별칭

사전 학습된 YOLOv7 별칭은 없습니다. Roboflow 외부에서 YOLOv7 instance segmentation을 학습하고, 가중치를 Project에 업로드한 다음, 자신의 `model_id` Serverless Hosted API에 대해.

## 코드 샘플

Inference SDK를 설치하고 [supervision](https://supervision.roboflow.com/) 마스크를 디코딩하고 그리는 데:

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

자신의 YOLOv7 instance segmentation 모델에 대해 추론을 실행한 다음, supervision을 사용해 예측된 마스크와 레이블을 원본 이미지에 렌더링합니다. 다음을 교체하세요 `model_id` 여러분의 Project 값으로 바꾸고, 여러분의 [Roboflow API Key](https://app.roboflow.com/settings/api) 를 `API_KEY` 환경 변수.

```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"),
)
results = client.infer(image_path, model_id="your-project/1")

detections = sv.Detections.from_inference(results)

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

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

annotated = mask_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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