> 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/deployment/ja/roboflow-cloud/serverless-api.md).

# Serverless Cloud API

## 概要

Roboflow にデプロイされたモデルには REST API があり、それを通じて画像に対して推論を実行できます。このデプロイ方法は、デプロイ先デバイスで常時インターネット接続がある環境に最適です。

アプリ内では、このエンドポイントは「Serverless Cloud API」、またはスペースが限られている場合（例: Workflow エディタのランタイムピッカー）には「Cloud API」と表示されます。A [専用デプロイ](/deployment/ja/roboflow-cloud/dedicated-deployments.md) エンドポイント（`*.roboflow.cloud`）は「Dedicated Cloud API」と表示され、旧来の v1 エンドポイントは「Hosted API (Legacy)」と表示されます。これらのラベルは、以前の「Serverless Hosted API」と「Serverless API V2」という名称に置き換わるものです。

Serverless Cloud API は以下で使用できます:

* [Workflows で](/deployment/ja/roboflow-cloud/serverless-api/use-in-a-workflow.md)
* [REST API とともに](#http-api)
* とともに [Inference Python SDK](#python-sdk)

### Inference サーバー

私たちの Serverless Cloud API は、 [Inference Server](https://docs.roboflow.com/reference/platform/rest-api/inference-server-openapi)によって支えられています。つまり、以下に示すように、Serverless Cloud API とセルフホスティングの विकल्पを簡単に切り替えることができます。

```python
from inference_sdk import InferenceHTTPClient, InferenceConfiguration

CLIENT = InferenceHTTPClient(
    # api_url="http://localhost:9001" # セルフホストの Inference サーバー
    api_url="https://serverless.roboflow.com", # 私たちの Serverless Cloud API
    api_key="API_KEY" # プライベートなモデルとデータにアクセスするためのオプション
).configure(InferenceConfiguration(api_key_transport="header"))

result = CLIENT.infer("image.jpg", model_id="model-id/1")
print(result)
```

その `api_key_transport="header"` 設定はキーを次の形式でのみ送信します: `Authorization: Bearer` ヘッダー。URL やログに鍵を残さないようにします。新規コードではこれが推奨です。Inference 1.5.0 より前のサーバーはこのヘッダーを読み取れないため、 `api_key_transport="both"` を使いつつ、呼び出しはそのままにしてください。参照先は [API キーの送信](https://docs.roboflow.com/reference/inference/inference-sdk/configuration#api-key-transport).

### 制限

私たちの Serverless Cloud API は、最大 20MB までのファイルアップロードをサポートしています。解像度の高い画像では制限に達することがあります。問題が発生した場合は、エンタープライズサポートの担当者に連絡するか、 [フォーラム](https://discuss.roboflow.com).

{% hint style="info" %}
リクエストが大きすぎる場合は、添付画像を縮小することを推奨します。画像はサーバーで受信された後、モデルアーキテクチャが受け付ける入力サイズに自動で縮小されるため、通常これは性能の低下にはつながりません。\
\
Python SDK などの一部の SDK では、API に送信する前に画像をモデルアーキテクチャの入力サイズへ自動的に縮小します。
{% endhint %}

***

参照 [Serverless Cloud API v1](/deployment/ja/regash/legacy-serverless.md) レガシー API ドキュメントを参照してください。

## HTTP API

### REST API で使用する

Serverless Cloud API には、すべてのモデルと Workflows に対して 1 つのエンドポイントがあります:

```
https://serverless.roboflow.com
```

#### HTTP エンドポイント

## Legacy Infer From Request

> Legacy inference endpoint for object detection, instance segmentation, and classification.\
> \
> Args:\
>     background\_tasks: (BackgroundTasks) pool of fastapi background tasks\
>     dataset\_id (str): ID of a Roboflow dataset corresponding to the model to use for inference OR workspace ID\
>     version\_id (str): ID of a Roboflow dataset version corresponding to the model to use for inference OR model ID\
>     api\_key (Optional\[str], default None): Roboflow API Key passed to the model during initialization for artifact retrieval.\
>     \# Other parameters described in the function signature...\
> \
> Returns:\
>     Union\[InstanceSegmentationInferenceResponse, KeypointsDetectionInferenceRequest, ObjectDetectionInferenceResponse, ClassificationInferenceResponse, MultiLabelClassificationInferenceResponse, SemanticSegmentationInferenceResponse, Any]: The response containing the inference results.

```json
{"openapi":"3.1.0","info":{"title":"Roboflow Inference Server","version":"1.5.0-post1"},"paths":{"/{dataset_id}/{version_id}":{"post":{"summary":"Legacy Infer From Request","description":"Legacy inference endpoint for object detection, instance segmentation, and classification.\n\nArgs:\n    background_tasks: (BackgroundTasks) pool of fastapi background tasks\n    dataset_id (str): ID of a Roboflow dataset corresponding to the model to use for inference OR workspace ID\n    version_id (str): ID of a Roboflow dataset version corresponding to the model to use for inference OR model ID\n    api_key (Optional[str], default None): Roboflow API Key passed to the model during initialization for artifact retrieval.\n    # Other parameters described in the function signature...\n\nReturns:\n    Union[InstanceSegmentationInferenceResponse, KeypointsDetectionInferenceRequest, ObjectDetectionInferenceResponse, ClassificationInferenceResponse, MultiLabelClassificationInferenceResponse, SemanticSegmentationInferenceResponse, Any]: The response containing the inference results.","operationId":"legacy_infer_from_request__dataset_id___version_id__post","parameters":[{"name":"dataset_id","in":"path","required":true,"schema":{"type":"string","description":"ID of a Roboflow dataset corresponding to the model to use for inference OR workspace ID","title":"Dataset Id"},"description":"ID of a Roboflow dataset corresponding to the model to use for inference OR workspace ID"},{"name":"version_id","in":"path","required":true,"schema":{"type":"string","description":"ID of a Roboflow dataset version corresponding to the model to use for inference OR model ID","title":"Version Id"},"description":"ID of a Roboflow dataset version corresponding to the model to use for inference OR model ID"},{"name":"api_key","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"description":"Roboflow API Key that will be passed to the model during initialization for artifact retrieval","title":"Api Key"},"description":"Roboflow API Key that will be passed to the model during initialization for artifact retrieval"},{"name":"confidence","in":"query","required":false,"schema":{"anyOf":[{"type":"number"},{"enum":["best","default"],"type":"string"}],"description":"The confidence threshold used to filter out predictions. Pass a float in [0, 1], or \"best\" to use F1-optimal thresholds from model evaluation, or \"default\" to use the model's built-in default.","default":0.4,"title":"Confidence"},"description":"The confidence threshold used to filter out predictions. Pass a float in [0, 1], or \"best\" to use F1-optimal thresholds from model evaluation, or \"default\" to use the model's built-in default."},{"name":"keypoint_confidence","in":"query","required":false,"schema":{"type":"number","description":"The confidence threshold used to filter out keypoints that are not visible based on model confidence","default":0,"title":"Keypoint Confidence"},"description":"The confidence threshold used to filter out keypoints that are not visible based on model confidence"},{"name":"format","in":"query","required":false,"schema":{"type":"string","description":"One of 'json' or 'image'. If 'json' prediction data is return as a JSON string. If 'image' prediction data is visualized and overlayed on the original input image.","default":"json","title":"Format"},"description":"One of 'json' or 'image'. If 'json' prediction data is return as a JSON string. If 'image' prediction data is visualized and overlayed on the original input image."},{"name":"image","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"description":"The publically accessible URL of an image to use for inference.","title":"Image"},"description":"The publically accessible URL of an image to use for inference."},{"name":"image_type","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"description":"One of base64 or numpy. Note, numpy input is not supported for Roboflow Hosted Inference.","default":"base64","title":"Image Type"},"description":"One of base64 or numpy. Note, numpy input is not supported for Roboflow Hosted Inference."},{"name":"labels","in":"query","required":false,"schema":{"anyOf":[{"type":"boolean"},{"type":"null"}],"description":"If true, labels will be include in any inference visualization.","default":false,"title":"Labels"},"description":"If true, labels will be include in any inference visualization."},{"name":"mask_decode_mode","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"description":"One of 'accurate' or 'fast'. If 'accurate' the mask will be decoded using the original image size. If 'fast' the mask will be decoded using the original mask size. 'accurate' is slower but more accurate.","default":"accurate","title":"Mask Decode Mode"},"description":"One of 'accurate' or 'fast'. If 'accurate' the mask will be decoded using the original image size. If 'fast' the mask will be decoded using the original mask size. 'accurate' is slower but more accurate."},{"name":"tradeoff_factor","in":"query","required":false,"schema":{"anyOf":[{"type":"number"},{"type":"null"}],"description":"The amount to tradeoff between 0='fast' and 1='accurate'","default":0,"title":"Tradeoff Factor"},"description":"The amount to tradeoff between 0='fast' and 1='accurate'"},{"name":"max_detections","in":"query","required":false,"schema":{"type":"integer","description":"The maximum number of detections to return. This is used to limit the number of predictions returned by the model. The model may return more predictions than this number, but only the top `max_detections` predictions will be returned.","default":300,"title":"Max Detections"},"description":"The maximum number of detections to return. This is used to limit the number of predictions returned by the model. The model may return more predictions than this number, but only the top `max_detections` predictions will be returned."},{"name":"overlap","in":"query","required":false,"schema":{"type":"number","description":"The IoU threhsold that must be met for a box pair to be considered duplicate during NMS","default":0.3,"title":"Overlap"},"description":"The IoU threhsold that must be met for a box pair to be considered duplicate during NMS"},{"name":"stroke","in":"query","required":false,"schema":{"type":"integer","description":"The stroke width used when visualizing predictions","default":1,"title":"Stroke"},"description":"The stroke width used when visualizing predictions"},{"name":"disable_preproc_auto_orient","in":"query","required":false,"schema":{"anyOf":[{"type":"boolean"},{"type":"null"}],"description":"If true, disables automatic image orientation","default":false,"title":"Disable Preproc Auto Orient"},"description":"If true, disables automatic image orientation"},{"name":"disable_preproc_contrast","in":"query","required":false,"schema":{"anyOf":[{"type":"boolean"},{"type":"null"}],"description":"If true, disables automatic contrast adjustment","default":false,"title":"Disable Preproc Contrast"},"description":"If true, disables automatic contrast adjustment"},{"name":"disable_preproc_grayscale","in":"query","required":false,"schema":{"anyOf":[{"type":"boolean"},{"type":"null"}],"description":"If true, disables automatic grayscale conversion","default":false,"title":"Disable Preproc Grayscale"},"description":"If true, disables automatic grayscale conversion"},{"name":"disable_preproc_static_crop","in":"query","required":false,"schema":{"anyOf":[{"type":"boolean"},{"type":"null"}],"description":"If true, disables automatic static crop","default":false,"title":"Disable Preproc Static Crop"},"description":"If true, disables automatic static crop"},{"name":"disable_active_learning","in":"query","required":false,"schema":{"anyOf":[{"type":"boolean"},{"type":"null"}],"description":"If true, the predictions will be prevented from registration by Active Learning (if the functionality is enabled)","default":false,"title":"Disable Active Learning"},"description":"If true, the predictions will be prevented from registration by Active Learning (if the functionality is enabled)"},{"name":"active_learning_target_dataset","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"description":"Parameter to be used when Active Learning data registration should happen against different dataset than the one pointed by model_id","title":"Active Learning Target Dataset"},"description":"Parameter to be used when Active Learning data registration should happen against different dataset than the one pointed by model_id"},{"name":"source","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"description":"The source of the inference request","default":"external","title":"Source"},"description":"The source of the inference request"},{"name":"source_info","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"description":"The detailed source information of the inference request","default":"external","title":"Source Info"},"description":"The detailed source information of the inference request"},{"name":"response_mask_format","in":"query","required":false,"schema":{"anyOf":[{"enum":["polygon","rle"],"type":"string"},{"type":"null"}],"description":"The format of the prediction mask - polygon (default) or rle - applicable for instance segmentation models.","default":"polygon","title":"Response Mask Format"},"description":"The format of the prediction mask - polygon (default) or rle - applicable for instance segmentation models."}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"anyOf":[{"$ref":"#/components/schemas/InstanceSegmentationInferenceResponse"},{"$ref":"#/components/schemas/KeypointsDetectionInferenceResponse"},{"$ref":"#/components/schemas/ObjectDetectionInferenceResponse"},{"$ref":"#/components/schemas/ClassificationInferenceResponse"},{"$ref":"#/components/schemas/MultiLabelClassificationInferenceResponse"},{"$ref":"#/components/schemas/SemanticSegmentationInferenceResponse"},{"$ref":"#/components/schemas/StubResponse"},{}],"title":"Response Legacy Infer From Request  Dataset Id   Version Id  Post"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}}},"components":{"schemas":{"InstanceSegmentationInferenceResponse":{"properties":{"visualization":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Visualization","description":"Base64 encoded string containing prediction visualization image data"},"inference_id":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Inference Id","description":"Unique identifier of inference"},"frame_id":{"anyOf":[{"type":"integer"},{"type":"null"}],"title":"Frame Id","description":"The frame id of the image used in inference if the input was a video"},"time":{"anyOf":[{"type":"number"},{"type":"null"}],"title":"Time","description":"The time in seconds it took to produce the predictions including image preprocessing"},"image":{"anyOf":[{"items":{"$ref":"#/components/schemas/InferenceResponseImage"},"type":"array"},{"$ref":"#/components/schemas/InferenceResponseImage"}],"title":"Image"},"predictions":{"items":{"anyOf":[{"$ref":"#/components/schemas/InstanceSegmentationPrediction"},{"$ref":"#/components/schemas/InstanceSegmentationRLEPrediction"}]},"type":"array","title":"Predictions"}},"type":"object","required":["image","predictions"],"title":"InstanceSegmentationInferenceResponse","description":"Instance Segmentation inference response.\n\nAttributes:\n    predictions (List[Union[\n        inference.core.entities.responses.inference.InstanceSegmentationPrediction,\n        inference.core.entities.responses.inference.InstanceSegmentationRLEPrediction\n    ]]): List of instance segmentation predictions."},"InferenceResponseImage":{"properties":{"width":{"type":"integer","title":"Width","description":"The original width of the image used in inference"},"height":{"type":"integer","title":"Height","description":"The original height of the image used in inference"}},"type":"object","required":["width","height"],"title":"InferenceResponseImage","description":"Inference response image information.\n\nAttributes:\n    width (int): The original width of the image used in inference.\n    height (int): The original height of the image used in inference."},"InstanceSegmentationPrediction":{"properties":{"x":{"type":"number","title":"X","description":"The center x-axis pixel coordinate of the prediction"},"y":{"type":"number","title":"Y","description":"The center y-axis pixel coordinate of the prediction"},"width":{"type":"number","title":"Width","description":"The width of the prediction bounding box in number of pixels"},"height":{"type":"number","title":"Height","description":"The height of the prediction bounding box in number of pixels"},"confidence":{"type":"number","title":"Confidence","description":"The detection confidence as a fraction between 0 and 1"},"class":{"type":"string","title":"Class","description":"The predicted class label"},"class_id":{"type":"integer","title":"Class Id","description":"The class id of the prediction"},"detection_id":{"type":"string","title":"Detection Id","description":"Unique identifier of detection"},"parent_id":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Parent Id","description":"Identifier of parent image region"},"class_confidence":{"anyOf":[{"type":"number"},{"type":"null"}],"title":"Class Confidence","description":"The class label confidence as a fraction between 0 and 1"},"points":{"items":{"$ref":"#/components/schemas/Point-Output"},"type":"array","title":"Points","description":"The list of points that make up the instance polygon"},"mask_format":{"type":"string","const":"polygon","title":"Mask Format","description":"Type of mask format","default":"polygon"}},"type":"object","required":["x","y","width","height","confidence","class","class_id","points"],"title":"InstanceSegmentationPrediction"},"Point-Output":{"properties":{"x":{"type":"number","title":"X","description":"The x-axis pixel coordinate of the point"},"y":{"type":"number","title":"Y","description":"The y-axis pixel coordinate of the point"}},"type":"object","required":["x","y"],"title":"Point","description":"Point coordinates.\n\nAttributes:\n    x (float): The x-axis pixel coordinate of the point.\n    y (float): The y-axis pixel coordinate of the point."},"InstanceSegmentationRLEPrediction":{"properties":{"x":{"type":"number","title":"X","description":"The center x-axis pixel coordinate of the prediction"},"y":{"type":"number","title":"Y","description":"The center y-axis pixel coordinate of the prediction"},"width":{"type":"number","title":"Width","description":"The width of the prediction bounding box in number of pixels"},"height":{"type":"number","title":"Height","description":"The height of the prediction bounding box in number of pixels"},"confidence":{"type":"number","title":"Confidence","description":"The detection confidence as a fraction between 0 and 1"},"class":{"type":"string","title":"Class","description":"The predicted class label"},"class_id":{"type":"integer","title":"Class Id","description":"The class id of the prediction"},"detection_id":{"type":"string","title":"Detection Id","description":"Unique identifier of detection"},"parent_id":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Parent Id","description":"Identifier of parent image region"},"rle":{"additionalProperties":true,"type":"object","title":"Rle","description":"RLE-encoded mask in COCO format: {'size': [H, W], 'counts': '...'}"},"mask_format":{"type":"string","const":"rle","title":"Mask Format","description":"Type of mask format","default":"rle"}},"type":"object","required":["x","y","width","height","confidence","class","class_id","rle"],"title":"InstanceSegmentationRLEPrediction"},"KeypointsDetectionInferenceResponse":{"properties":{"visualization":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Visualization","description":"Base64 encoded string containing prediction visualization image data"},"inference_id":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Inference Id","description":"Unique identifier of inference"},"frame_id":{"anyOf":[{"type":"integer"},{"type":"null"}],"title":"Frame Id","description":"The frame id of the image used in inference if the input was a video"},"time":{"anyOf":[{"type":"number"},{"type":"null"}],"title":"Time","description":"The time in seconds it took to produce the predictions including image preprocessing"},"image":{"anyOf":[{"items":{"$ref":"#/components/schemas/InferenceResponseImage"},"type":"array"},{"$ref":"#/components/schemas/InferenceResponseImage"}],"title":"Image"},"predictions":{"items":{"$ref":"#/components/schemas/KeypointsPrediction"},"type":"array","title":"Predictions"}},"type":"object","required":["image","predictions"],"title":"KeypointsDetectionInferenceResponse"},"KeypointsPrediction":{"properties":{"x":{"type":"number","title":"X","description":"The center x-axis pixel coordinate of the prediction"},"y":{"type":"number","title":"Y","description":"The center y-axis pixel coordinate of the prediction"},"width":{"type":"number","title":"Width","description":"The width of the prediction bounding box in number of pixels"},"height":{"type":"number","title":"Height","description":"The height of the prediction bounding box in number of pixels"},"confidence":{"type":"number","title":"Confidence","description":"The detection confidence as a fraction between 0 and 1"},"class":{"type":"string","title":"Class","description":"The predicted class label"},"class_confidence":{"anyOf":[{"type":"number"},{"type":"null"}],"title":"Class Confidence","description":"The class label confidence as a fraction between 0 and 1"},"class_id":{"type":"integer","title":"Class Id","description":"The class id of the prediction"},"tracker_id":{"anyOf":[{"type":"integer"},{"type":"null"}],"title":"Tracker Id","description":"The tracker id of the prediction if tracking is enabled"},"detection_id":{"type":"string","title":"Detection Id","description":"Unique identifier of detection"},"parent_id":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Parent Id","description":"Identifier of parent image region. Useful when stack of detection-models is in use to refer the RoI being the input to inference"},"keypoints":{"items":{"$ref":"#/components/schemas/Keypoint"},"type":"array","title":"Keypoints"}},"type":"object","required":["x","y","width","height","confidence","class","class_id","keypoints"],"title":"KeypointsPrediction"},"Keypoint":{"properties":{"x":{"type":"number","title":"X","description":"The x-axis pixel coordinate of the point"},"y":{"type":"number","title":"Y","description":"The y-axis pixel coordinate of the point"},"confidence":{"type":"number","title":"Confidence","description":"Model confidence regarding keypoint visibility."},"class_id":{"type":"integer","title":"Class Id","description":"Identifier of keypoint."},"class":{"type":"string","title":"Class","description":"Type of keypoint."}},"type":"object","required":["x","y","confidence","class_id","class"],"title":"Keypoint"},"ObjectDetectionInferenceResponse":{"properties":{"visualization":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Visualization","description":"Base64 encoded string containing prediction visualization image data"},"inference_id":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Inference Id","description":"Unique identifier of inference"},"frame_id":{"anyOf":[{"type":"integer"},{"type":"null"}],"title":"Frame Id","description":"The frame id of the image used in inference if the input was a video"},"time":{"anyOf":[{"type":"number"},{"type":"null"}],"title":"Time","description":"The time in seconds it took to produce the predictions including image preprocessing"},"image":{"anyOf":[{"items":{"$ref":"#/components/schemas/InferenceResponseImage"},"type":"array"},{"$ref":"#/components/schemas/InferenceResponseImage"}],"title":"Image"},"predictions":{"items":{"$ref":"#/components/schemas/ObjectDetectionPrediction"},"type":"array","title":"Predictions"}},"type":"object","required":["image","predictions"],"title":"ObjectDetectionInferenceResponse","description":"Object Detection inference response.\n\nAttributes:\n    predictions (List[inference.core.entities.responses.inference.ObjectDetectionPrediction]): List of object detection predictions."},"ObjectDetectionPrediction":{"properties":{"x":{"type":"number","title":"X","description":"The center x-axis pixel coordinate of the prediction"},"y":{"type":"number","title":"Y","description":"The center y-axis pixel coordinate of the prediction"},"width":{"type":"number","title":"Width","description":"The width of the prediction bounding box in number of pixels"},"height":{"type":"number","title":"Height","description":"The height of the prediction bounding box in number of pixels"},"confidence":{"type":"number","title":"Confidence","description":"The detection confidence as a fraction between 0 and 1"},"class":{"type":"string","title":"Class","description":"The predicted class label"},"class_confidence":{"anyOf":[{"type":"number"},{"type":"null"}],"title":"Class Confidence","description":"The class label confidence as a fraction between 0 and 1"},"class_id":{"type":"integer","title":"Class Id","description":"The class id of the prediction"},"tracker_id":{"anyOf":[{"type":"integer"},{"type":"null"}],"title":"Tracker Id","description":"The tracker id of the prediction if tracking is enabled"},"detection_id":{"type":"string","title":"Detection Id","description":"Unique identifier of detection"},"parent_id":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Parent Id","description":"Identifier of parent image region. Useful when stack of detection-models is in use to refer the RoI being the input to inference"}},"type":"object","required":["x","y","width","height","confidence","class","class_id"],"title":"ObjectDetectionPrediction","description":"Object Detection prediction.\n\nAttributes:\n    x (float): The center x-axis pixel coordinate of the prediction.\n    y (float): The center y-axis pixel coordinate of the prediction.\n    width (float): The width of the prediction bounding box in number of pixels.\n    height (float): The height of the prediction bounding box in number of pixels.\n    confidence (float): The detection confidence as a fraction between 0 and 1.\n    class_name (str): The predicted class label.\n    class_confidence (Union[float, None]): The class label confidence as a fraction between 0 and 1.\n    class_id (int): The class id of the prediction"},"ClassificationInferenceResponse":{"properties":{"visualization":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Visualization","description":"Base64 encoded string containing prediction visualization image data"},"inference_id":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Inference Id","description":"Unique identifier of inference"},"frame_id":{"anyOf":[{"type":"integer"},{"type":"null"}],"title":"Frame Id","description":"The frame id of the image used in inference if the input was a video"},"time":{"anyOf":[{"type":"number"},{"type":"null"}],"title":"Time","description":"The time in seconds it took to produce the predictions including image preprocessing"},"image":{"anyOf":[{"items":{"$ref":"#/components/schemas/InferenceResponseImage"},"type":"array"},{"$ref":"#/components/schemas/InferenceResponseImage"}],"title":"Image"},"predictions":{"items":{"$ref":"#/components/schemas/ClassificationPrediction"},"type":"array","title":"Predictions"},"top":{"type":"string","title":"Top","description":"The top predicted class label","default":""},"confidence":{"type":"number","title":"Confidence","description":"The confidence of the top predicted class label","default":0},"parent_id":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Parent Id","description":"Identifier of parent image region. Useful when stack of detection-models is in use to refer the RoI being the input to inference"}},"type":"object","required":["image","predictions"],"title":"ClassificationInferenceResponse","description":"Classification inference response.\n\nAttributes:\n    predictions (List[inference.core.entities.responses.inference.ClassificationPrediction]): List of classification predictions.\n    top (str): The top predicted class label.\n    confidence (float): The confidence of the top predicted class label."},"ClassificationPrediction":{"properties":{"class":{"type":"string","title":"Class","description":"The predicted class label"},"class_id":{"type":"integer","title":"Class Id","description":"Numeric ID associated with the class label"},"confidence":{"type":"number","title":"Confidence","description":"The class label confidence as a fraction between 0 and 1"}},"type":"object","required":["class","class_id","confidence"],"title":"ClassificationPrediction","description":"Classification prediction.\n\nAttributes:\n    class_name (str): The predicted class label.\n    class_id (int): Numeric ID associated with the class label.\n    confidence (float): The class label confidence as a fraction between 0 and 1."},"MultiLabelClassificationInferenceResponse":{"properties":{"visualization":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Visualization","description":"Base64 encoded string containing prediction visualization image data"},"inference_id":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Inference Id","description":"Unique identifier of inference"},"frame_id":{"anyOf":[{"type":"integer"},{"type":"null"}],"title":"Frame Id","description":"The frame id of the image used in inference if the input was a video"},"time":{"anyOf":[{"type":"number"},{"type":"null"}],"title":"Time","description":"The time in seconds it took to produce the predictions including image preprocessing"},"image":{"anyOf":[{"items":{"$ref":"#/components/schemas/InferenceResponseImage"},"type":"array"},{"$ref":"#/components/schemas/InferenceResponseImage"}],"title":"Image"},"predictions":{"additionalProperties":{"$ref":"#/components/schemas/MultiLabelClassificationPrediction"},"type":"object","title":"Predictions"},"predicted_classes":{"items":{"type":"string"},"type":"array","title":"Predicted Classes","description":"The list of predicted classes"},"parent_id":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Parent Id","description":"Identifier of parent image region. Useful when stack of detection-models is in use to refer the RoI being the input to inference"}},"type":"object","required":["image","predictions","predicted_classes"],"title":"MultiLabelClassificationInferenceResponse","description":"Multi-label Classification inference response.\n\nAttributes:\n    predictions (Dict[str, inference.core.entities.responses.inference.MultiLabelClassificationPrediction]): Dictionary of multi-label classification predictions.\n    predicted_classes (List[str]): The list of predicted classes."},"MultiLabelClassificationPrediction":{"properties":{"confidence":{"type":"number","title":"Confidence","description":"The class label confidence as a fraction between 0 and 1"},"class_id":{"type":"integer","title":"Class Id","description":"Numeric ID associated with the class label"}},"type":"object","required":["confidence","class_id"],"title":"MultiLabelClassificationPrediction","description":"Multi-label Classification prediction.\n\nAttributes:\n    confidence (float): The class label confidence as a fraction between 0 and 1."},"SemanticSegmentationInferenceResponse":{"properties":{"visualization":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Visualization","description":"Base64 encoded string containing prediction visualization image data"},"inference_id":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Inference Id","description":"Unique identifier of inference"},"frame_id":{"anyOf":[{"type":"integer"},{"type":"null"}],"title":"Frame Id","description":"The frame id of the image used in inference if the input was a video"},"time":{"anyOf":[{"type":"number"},{"type":"null"}],"title":"Time","description":"The time in seconds it took to produce the predictions including image preprocessing"},"image":{"anyOf":[{"items":{"$ref":"#/components/schemas/InferenceResponseImage"},"type":"array"},{"$ref":"#/components/schemas/InferenceResponseImage"}],"title":"Image"},"predictions":{"$ref":"#/components/schemas/SemanticSegmentationPrediction"}},"type":"object","required":["image","predictions"],"title":"SemanticSegmentationInferenceResponse","description":"Semantic Segmentation inference response.\n\nAttributes:\n    predictions (inference.core.entities.responses.inference.SemanticSegmentationPrediction): Semantic segmentation predictions."},"SemanticSegmentationPrediction":{"properties":{"segmentation_mask":{"type":"string","title":"Segmentation Mask","description":"base64-encoded PNG of predicted class label at each pixel. When the request sets response_mask_format='numpy' (in-process fast path), this carries the raw uint8 numpy label map instead; JSON serialization always yields the base64 PNG string."},"class_map":{"additionalProperties":{"type":"string"},"type":"object","title":"Class Map","description":"Map of pixel intensity value to class label"},"confidence_mask":{"type":"string","title":"Confidence Mask","description":"base64-encoded PNG of predicted class confidence at each pixel. When the request sets response_mask_format='numpy' (in-process fast path), this carries the raw uint8 numpy confidence map instead; JSON serialization always yields the base64 PNG string."},"present_class_ids":{"anyOf":[{"items":{"type":"integer"},"type":"array"},{"type":"null"}],"title":"Present Class Ids","description":"Sorted list of pixel values present in segmentation_mask, including background (0) when present. Optimization hint that lets consumers skip scanning the full-resolution mask; consumers must fall back to scanning when this field is absent."}},"type":"object","required":["segmentation_mask","class_map","confidence_mask"],"title":"SemanticSegmentationPrediction"},"StubResponse":{"properties":{"visualization":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Visualization","description":"Base64 encoded string containing prediction visualization image data"},"inference_id":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Inference Id","description":"Unique identifier of inference"},"frame_id":{"anyOf":[{"type":"integer"},{"type":"null"}],"title":"Frame Id","description":"The frame id of the image used in inference if the input was a video"},"time":{"anyOf":[{"type":"number"},{"type":"null"}],"title":"Time","description":"The time in seconds it took to produce the predictions including image preprocessing"},"is_stub":{"type":"boolean","title":"Is Stub","description":"Field to mark prediction type as stub"},"model_id":{"type":"string","title":"Model Id","description":"Identifier of a model stub that was called"},"task_type":{"type":"string","title":"Task Type","description":"Task type of the project"}},"type":"object","required":["is_stub","model_id","task_type"],"title":"StubResponse"},"HTTPValidationError":{"properties":{"detail":{"items":{"$ref":"#/components/schemas/ValidationError"},"type":"array","title":"Detail"}},"type":"object","title":"HTTPValidationError"},"ValidationError":{"properties":{"loc":{"items":{"anyOf":[{"type":"string"},{"type":"integer"}]},"type":"array","title":"Location"},"msg":{"type":"string","title":"Message"},"type":{"type":"string","title":"Error Type"},"input":{"title":"Input"},"ctx":{"type":"object","title":"Context"}},"type":"object","required":["loc","msg","type"],"title":"ValidationError"}}}}
```

### 画像上でモデルを実行

Roboflow は複数のランタイムで推論を公開しています。正しい選択は、単一のモデルを呼び出すのか Workflow を呼び出すのか、必要なスループット、そしてワークロードがどこで実行されるかによって決まります。

このページは簡単な概要です。詳細な推論リファレンスは、 [製品ドキュメント](/deployment/ja/readme.md)にあります。これは同じドキュメントサイトの一部です。より詳しい内容がある場所へのクロスリンクを用意しています。

#### 推論ランタイム

| ランタイム                                                | 次の場合に使用                                                  | 参照                                                                                                                                              |
| ---------------------------------------------------- | -------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------- |
| **Serverless Cloud API** (`serverless.roboflow.com`) | デフォルト。ホスト型、自動スケーリング対応、モデルと Workflows をサポートします。           | [Serverless Cloud API](/deployment/ja/roboflow-cloud/serverless-api.md)                                                                         |
| **専用デプロイメント**                                        | 予測可能なレイテンシ、高いスループット、または固定 GPU タイプが必要な場合。Roboflow が管理します。 | [専用デプロイメント](/deployment/ja/roboflow-cloud/dedicated-deployments.md#http-api) および [製品概要](/deployment/ja/roboflow-cloud/dedicated-deployments.md) |
| **Roboflow Inference** （セルフホスト）                      | オンプレミス、エッジデバイス、エアギャップ環境、または VPC の外に出せないワークロード。オープンソースです。 | [セルフホストデプロイ](/deployment/ja/serufuhosuto/self-hosted.md)                                                                                        |

#### Serverless Cloud API の呼び出し

モデルを実行:

```bash
curl -F "file=@photo.jpg" \
  -H "Authorization: Bearer $ROBOFLOW_API_KEY" \
  "https://serverless.roboflow.com/<project>/<version>?confidence=0.5"
```

Workflow を実行:

```bash
curl -X POST "https://serverless.roboflow.com/infer/workflows/<workspace>/<workflow>" \
  -H "Authorization: Bearer $ROBOFLOW_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "inputs": { "image": { "type": "url", "value": "https://example.com/photo.jpg" } }
  }'
```

{% hint style="info" %}
キーを `?api_key=` クエリパラメータまたは `api_key` body フィールドとして送る方法はレガシーな手段です。今でも動作しますが、 `Authorization: Bearer` ヘッダーを使えば、キーを URL やログに残さずに済みます。参照は [REST API で認証する](https://docs.roboflow.com/reference/platform/rest-api/authenticate-with-the-rest-api).
{% endhint %}

ライブ動画については、 [Serverless Video Streaming API](/deployment/ja/roboflow-cloud/serverless-api/serverless-video-streaming-api.md)をご覧ください。大規模な画像・動画セットの非同期処理については、 [バッチ処理](/deployment/ja/roboflow-cloud/batch-processing.md).

#### 非推奨: Serverless v1

旧来のタスク固有エンドポイント - `detect.roboflow.com`, `classify.roboflow.com`, `outline.roboflow.com`, `segment.roboflow.com` - は **非推奨です**. 後方互換性のため今でも応答しますが、新しいコードでは `serverless.roboflow.com` を使うべきです。

もしスニペットが `*.roboflow.com` のタスクホストを指しているなら、それはレガシーとして扱い、上記の Serverless Cloud API 形式に置き換えてください。

## Python SDK

### Python SDK で使用する

Python で作業しているなら、Serverless Cloud API とやり取りする最も簡単な方法は Inference Python SDK を使うことです。

使用するには [Inference SDK](https://docs.roboflow.com/reference/inference/inference-sdk)、まずインストールします:

```
pip install inference-sdk
```

Serverless Cloud API にリクエストを送るには、次のコードを使います:

<pre class="language-python"><code class="lang-python"><strong>from inference_sdk import InferenceHTTPClient, InferenceConfiguration
</strong>
CLIENT = InferenceHTTPClient(
    api_url="https://serverless.roboflow.com",
    api_key="API_KEY"
).configure(InferenceConfiguration(api_key_transport="header"))

result = CLIENT.infer("image.jpg", model_id="model-id/1")
print(result)
</code></pre>

上では、 [モデル ID](https://docs.roboflow.com/reference/authentication/authentication/workspace-and-project-ids) および [API キー](https://docs.roboflow.com/reference/authentication/authentication/find-your-roboflow-api-key)を指定します。このコードはモデルを実行し、結果を返します。

#### Roboflow Instant Model

Serverless Cloud API は Roboflow の [Instant Model](https://docs.roboflow.com/models/train/roboflow-instant)の実行もサポートしています。Instant Model も他のモデルと同様に実行できますが、信頼度しきい値は Instant Model では敏感に反応する場合がある点に注意してください。

{% hint style="info" %}
最適な confidence は、モデルが学習された画像枚数によって異なります。最適なしきい値は通常 0.85 から 0.99 の範囲です。
{% endhint %}

```python
configuration = InferenceConfiguration(
    confidence_threshold=0.95,
    api_key_transport="header",
)
CLIENT.configure(configuration)

result = CLIENT.infer("image.jpg", model_id="roboflow-instant-model-id/1")
```

`configure(...)` は設定全体を置き換えるため、 `api_key_transport` を、適用するすべての設定に含めてください。

### Python SDK で動画をストリーミングする

Inference SDK の WebRTC クライアントを使って、動画上で物体検出モデルを実行します。Serverless Video Streaming API は Roboflow Cloud で動画を処理し、各フレームごとの予測を返します。

SDK とその WebRTC 依存関係をインストールして `supervision`:

```bash
pip install "inference-sdk[webrtc]" supervision
```

```python
import cv2
import supervision as sv
from inference_sdk import InferenceHTTPClient
from inference_sdk.webrtc import VideoFileSource

client = InferenceHTTPClient(
    api_url="https://serverless.roboflow.com",
    api_key="API_KEY",
)

session = client.webrtc.stream(
    source=VideoFileSource("video.mp4"),
    model_id="model-id/1",
)

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

@session.on_frame
def show(frame, data):
    if data is None:
        return

    detections = sv.Detections.from_inference(data)
    annotated = box_annotator.annotate(frame.copy(), detections)
    annotated = label_annotator.annotate(annotated, detections)
    cv2.imshow("Predictions", annotated)

    if cv2.waitKey(1) & 0xFF == ord("q"):
        session.close()

session.run()
cv2.destroyAllWindows()
```

置き換えてください `API_KEY` および `model-id/1` を API キーとモデル ID に置き換えてください。Web カメラや RTSP カメラからのストリーミング、各フレームの処理、または Workflow の実行方法は、 [Serverless Video Streaming API ガイド](/deployment/ja/roboflow-cloud/serverless-api/serverless-video-streaming-api.md).

## CLI

Roboflow 上で学習したモデル、または [Roboflow Universe](https://universe.roboflow.com).

で利用できるオープンソースモデルを実行するには、 `roboflow infer` をコマンドラインで実行すると、CLI は画像を Roboflow API に送信し、予測を出力します。

### コマンド

```bash
roboflow infer <image-path> -m <project/version>
```

#### オプション

| フラグ                  | 説明                                                                                                                            |
| -------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
| `-m`, `--model`      | のモデル ID `project/version` 形式（必須）                                                                                              |
| `-c`, `--confidence` | 信頼度しきい値、0.0–1.0（デフォルト: 0.5）                                                                                                   |
| `-o`, `--overlap`    | 重なり/NMS しきい値、0.0–1.0（デフォルト: 0.5）                                                                                              |
| `-t`, `--type`       | モデルタイプ（自動検出を省略）: `object-detection`, `classification`, `instance-segmentation`, `semantic-segmentation`, `keypoint-detection` |

### 例

Roboflow Universe のオープンソースモデルを使って推論を実行します。たとえば、 [poker-cards](https://universe.roboflow.com/roboflow-100/poker-cards-cxcvz/model/1) データセット:

```bash
roboflow infer ~/Downloads/ace.jpg -m poker-cards-cxcvz/1 -c 0.7
```

ワークスペースは、設定済みのワークスペースがデフォルトになります。別のワークスペースのモデルを使うには:

```bash
roboflow infer photo.jpg -m poker-cards-cxcvz/1 -w roboflow-100
```

自動検出 API 呼び出しを省略するにはモデルタイプを指定します:

```bash
roboflow infer photo.jpg -m my-project/3 -t object-detection
```

### JSON出力

使用 `--json` して、スクリプトや自動化のための構造化された予測データを取得します:

```bash
roboflow infer photo.jpg -m my-project/3 --json
```

```json
{
  "predictions": [
    {
      "x": 1230.0,
      "y": 814.5,
      "width": 840.0,
      "height": 1273.0,
      "confidence": 0.882,
      "class": "Scissors",
      "class_id": 2
    }
  ]
}
```

対応しているすべてのパラメータは、 `roboflow infer --help`.

## MCP サーバー

AI エージェントを [MCP サーバー](https://docs.roboflow.com/agents/mcp-server) に接続すると、これらのツールで画像上のモデルを実行できます:

<table data-search="false"><thead><tr><th width="290">ツール</th><th>説明</th></tr></thead><tbody><tr><td><code>models_infer</code></td><td>学習済みモデルを使って、画像に対してホスト型推論を実行します。</td></tr><tr><td><code>workflows_run</code></td><td>1 枚または複数の画像に対して保存済みの Workflow を実行します。</td></tr><tr><td><code>project_deployment_run</code></td><td>プロジェクトの安定したライブエンドポイントを通じて推論を実行します。</td></tr></tbody></table>