> 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-instant.md).

# Roboflow Instant

Roboflow Instant는 소량의 라벨링된 이미지로 몇 분 만에 학습되는 few-shot object detection 모델입니다. 빠른 Proof of Concept 작업을 위해 նախատես되어 있으며 Object Detection 프로젝트만 지원합니다. 다음에서 학습하는 방법을 알아보세요 [Roboflow Instant](/roboflow/roboflow-ko/train/roboflow-instant.md).

학습이 완료되면 Instant 모델은 다음에서 사용할 수 있습니다 [Serverless Hosted API](/roboflow/roboflow-ko/deploy/serverless-hosted-api-v2.md) 다른 모든 Roboflow 모델과 동일한 endpoint 패턴을 사용하여.

## 사용 방법

Roboflow Instant 모델은 모델 ID(`<project-id>/<version>`)로 호출합니다. Roboflow 3.0 모델을 호출하는 방식과 동일합니다. 사전 설정된/default Instant 모델은 없습니다. 먼저 Workspace 안에서 하나를 학습해야 합니다.

{% hint style="info" %}
Instant 모델의 confidence threshold는 민감할 수 있습니다. 최적의 값은 일반적으로 학습 세트의 크기에 따라 0.85에서 0.99 사이입니다.
{% endhint %}

## 코드 샘플

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

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

다음 값을 전달하세요 [Roboflow API key](https://app.roboflow.com/settings/api) 를 `API_KEY` 환경 변수에 설정하고 `your-instant-model-id/1` 를 학습한 Instant 모델 ID로 바꾸세요. 아래 스크립트는 inference를 실행하고, 응답을 `sv.Detections` 객체로 변환한 뒤, bounding box와 label을 그려서 주석이 달린 이미지를 디스크에 저장합니다.

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

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"),
)
results = client.infer(image, model_id="your-instant-model-id/1")

detections = sv.Detections.from_inference(results)

box_annotator = sv.BoxAnnotator()
label_annotator = sv.LabelAnnotator()

annotated_image = box_annotator.annotate(scene=image.copy(), detections=detections)
annotated_image = label_annotator.annotate(scene=annotated_image, detections=detections)

cv2.imwrite("annotated.png", annotated_image)
```

{% 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 %}

더 많은 배포 옵션과 자체 호스팅에 대해서는 다음을 참조하세요 [Inference 문서](https://inference.roboflow.com/).


---

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