> 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/hi/roboflow-cloud/serverless-api.md). # Serverless Cloud API Roboflow cloud में GPU-accelerated auto-scaling infrastructure पर Workflows और Model Inference चलाएँ। ## के बारे में Roboflow पर परिनियोजित मॉडलों के लिए एक REST API उपलब्ध है, जिसके माध्यम से आप छवियों पर इनफ़ेरेंस चला सकते हैं। यह परिनियोजन विधि उन वातावरणों के लिए आदर्श है जहाँ आपके परिनियोजन डिवाइस पर स्थायी इंटरनेट कनेक्शन हो। ऐप में, इस एंडपॉइंट को "Serverless Cloud API" कहा जाता है, या जहाँ जगह कम हो (जैसे: Workflow editor runtime picker) वहाँ "Cloud API"। एक [समर्पित परिनियोजन](/deployment/hi/roboflow-cloud/dedicated-deployments.md) एंडपॉइंट (`*.roboflow.cloud`) को "Dedicated Cloud API" कहा जाता है, और पुराने v1 एंडपॉइंट को "Hosted API (Legacy)" कहा जाता है। ये लेबल पहले के "Serverless Hosted API" और "Serverless API V2" नामों की जगह लेते हैं। आप Serverless Cloud API का उपयोग कर सकते हैं: * [Workflows में](/deployment/hi/roboflow-cloud/serverless-api/use-in-a-workflow.md) * [REST API के साथ](#http-api) * के साथ [Inference Python SDK](#python-sdk) ### Inference सर्वर हमारा Serverless Cloud API द्वारा संचालित है [Inference Server](https://docs.roboflow.com/reference/platform/rest-api/inference-server-openapi). इसका मतलब है कि आप नीचे दिखाए अनुसार हमारे Serverless Cloud API और self-hosting विकल्प के बीच और वापस आसानी से स्विच कर सकते हैं: ```python from inference_sdk import InferenceHTTPClient, InferenceConfiguration CLIENT = InferenceHTTPClient( # api_url="http://localhost:9001" # स्व-होस्टेड Inference सर्वर api_url="https://serverless.roboflow.com", # हमारा Serverless Cloud API api_key="API_KEY" # अपने निजी मॉडलों और डेटा तक पहुँचने के लिए वैकल्पिक ).configure(InferenceConfiguration(api_key_transport="header")) result = CLIENT.infer("image.jpg", model_id="model-id/1") print(result) ``` यह `api_key_transport="header"` सेटिंग कुंजी को केवल एक `Authorization: Bearer` हेडर के रूप में भेजती है, जिससे यह URLs और लॉग्स में नहीं जाता: सभी नए कोड के लिए अनुशंसित। Inference 1.5.0 से पुराने सर्वर हेडर नहीं पढ़ते; उपयोग करें `api_key_transport="both"` जब तक आप अभी भी एक को कॉल करते हैं। देखें [API key ट्रांसपोर्ट](https://docs.roboflow.com/reference/inference/inference-sdk/configuration#api-key-transport). ### सीमाएँ छवि इनफ़ेरेंस का बिल प्रति छवि क्रेडिट्स में, मॉडल द्वारा निर्धारित दर पर लगाया जाता है। देखें [मूल्य निर्धारण](/deployment/hi/roboflow-cloud/serverless-api/pricing.md) और [मॉडल मूल्य निर्धारण](/deployment/hi/roboflow-cloud/serverless-api/model-pricing.md). हमारा Serverless Cloud API 20MB तक की फ़ाइल अपलोड का समर्थन करता है। उच्च रिज़ॉल्यूशन वाली छवियों के साथ आपको सीमाएँ आ सकती हैं। यदि आपको कोई समस्या हो, तो कृपया अपने एंटरप्राइज़ सपोर्ट संपर्क से संपर्क करें या संदेश पोस्ट करें [फोरम](https://discuss.roboflow.com). {% hint style="info" %} ऐसे मामलों में जब अनुरोध बहुत बड़े हों, हम संलग्न छवियों का आकार घटाने की सलाह देते हैं। इससे आमतौर पर प्रदर्शन खराब नहीं होता क्योंकि हमारी सर्वरों पर प्राप्त होने के बाद छवियों का आकार वैसे भी घटाया जाता है ताकि वे मॉडल आर्किटेक्चर द्वारा स्वीकार किए जाने वाले इनपुट आकार में आ जाएँ.\ \ हमारे कुछ SDK, जैसे Python SDK, API को भेजे जाने से पहले स्वतः छवियों का आकार मॉडल आर्किटेक्चर के इनपुट आकार तक घटा देते हैं। {% endhint %} *** देखें [Serverless Cloud API v1](/deployment/hi/legacy/legacy-serverless.md) पुराने API दस्तावेज़ के लिए। ## HTTP API ### REST API के साथ उपयोग करें Serverless Cloud API में सभी मॉडलों और Workflows के लिए एक ही एंडपॉइंट है: ``` https://serverless.roboflow.com ``` #### HTTP एंडपॉइंट ## 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":"1.8.2"},"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":"class_filter","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"description":"Action recognition only: comma separated classes. The subset of a fine-tuned model's classes to report. A zero-shot model answers in its own words and ignores it.","title":"Class Filter"},"description":"Action recognition only: comma separated classes. The subset of a fine-tuned model's classes to report. A zero-shot model answers in its own words and ignores it."},{"name":"include_candidates","in":"query","required":false,"schema":{"type":"boolean","description":"Action recognition: return raw scored candidates","default":false,"title":"Include Candidates"},"description":"Action recognition: return raw scored candidates"},{"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":"allow_reduced_mask_resolution","in":"query","required":false,"schema":{"type":"boolean","description":"Opt into mask_decode_mode and tradeoff_factor on inference_models. Otherwise masks stay at image resolution. Legacy backend decoding is unchanged.","default":false,"title":"Allow Reduced Mask Resolution"},"description":"Opt into mask_decode_mode and tradeoff_factor on inference_models. Otherwise masks stay at image resolution. Legacy backend decoding is unchanged."},{"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":"include_anomaly_map","in":"query","required":false,"schema":{"anyOf":[{"type":"boolean"},{"type":"null"}],"description":"Anomaly detection only: include the raw anomaly heatmap at the network input resolution","default":false,"title":"Include Anomaly Map"},"description":"Anomaly detection only: include the raw anomaly heatmap at the network input resolution"},{"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/AnomalyDetectionResponse"},{"$ref":"#/components/schemas/ClassificationInferenceResponse"},{"$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":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}}},"components":{"schemas":{"InstanceSegmentationInferenceResponse":{"properties":{"visualization":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Visualization","description":"Base64 encoded string containing prediction visualization image data"},"inference_id":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Inference Id","description":"Unique identifier of inference"},"frame_id":{"anyOf":[{"type":"integer"},{"type":"null"}],"title":"Frame Id","description":"The frame id of the image used in inference if the input was a video"},"time":{"anyOf":[{"type":"number"},{"type":"null"}],"title":"Time","description":"The time in seconds it took to produce the predictions including image preprocessing"},"resolved_model":{"anyOf":[{"$ref":"#/components/schemas/ResolvedModel"},{"type":"null"}],"description":"Model identity and available package details for this result."},"image":{"anyOf":[{"items":{"$ref":"#/components/schemas/InferenceResponseImage"},"type":"array"},{"$ref":"#/components/schemas/InferenceResponseImage"}],"title":"Image","description":"Dimensions of the coordinate frame shared by bounding boxes, polygon points and RLE masks. With opt-in this is the selected mask grid; otherwise it is the original image. Multi-image responses may provide a list."},"predictions":{"items":{"anyOf":[{"$ref":"#/components/schemas/InstanceSegmentationPrediction"},{"$ref":"#/components/schemas/InstanceSegmentationRLEPrediction"}]},"type":"array","title":"Predictions"},"original_image":{"anyOf":[{"$ref":"#/components/schemas/InferenceResponseImage"},{"type":"null"}],"description":"Original image dimensions retained for opted-in responses, including when the selected grid has the same dimensions."},"mask_metadata":{"anyOf":[{"$ref":"#/components/schemas/MaskCoordinateMetadata"},{"type":"null"}],"description":"Present for opted-in responses in either format. All prediction geometry uses this grid. Multiply x coordinates and widths by scale_x, and y coordinates and heights by scale_y, to map to original_image."}},"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."},"ResolvedModel":{"properties":{"model_id":{"type":"string","title":"Model Id","description":"ID of the loaded model, using the canonical ID when available."},"model_package_id":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Model Package Id","description":"ID of the package that loaded successfully and produced this result, including after a loading fallback."},"backend":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Backend","description":"Backend of the loaded package, such as onnx, trt, or torch."},"quantization":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Quantization","description":"Package quantization, such as fp32 or fp16, or unknown when unavailable. This does not specify the input tensor dtype or the precision of every runtime operation."}},"type":"object","required":["model_id"],"title":"ResolvedModel","description":"Identity of the model and available package details used for inference.\n\nAttributes:\n model_id (str): ID of the loaded model, using the canonical ID when available.\n model_package_id (Optional[str]): ID of the loaded model package.\n backend (Optional[str]): Backend of the loaded package.\n quantization (Optional[str]): Quantization of the loaded package."},"InferenceResponseImage":{"properties":{"width":{"type":"integer","title":"Width","description":"The width of the represented image coordinate frame"},"height":{"type":"integer","title":"Height","description":"The height of the represented image coordinate frame"}},"type":"object","required":["width","height"],"title":"InferenceResponseImage","description":"Inference response image information.\n\nAttributes:\n width (int): The width of the represented image coordinate frame.\n height (int): The height of the represented image coordinate frame."},"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"},"MaskCoordinateMetadata":{"properties":{"coordinate_system":{"type":"string","const":"mask_grid","title":"Coordinate System","description":"All prediction geometry uses this grid, matching response image dimensions.","default":"mask_grid"},"width":{"type":"integer","exclusiveMinimum":0,"title":"Width","description":"Mask-grid width in pixels."},"height":{"type":"integer","exclusiveMinimum":0,"title":"Height","description":"Mask-grid height in pixels."},"scale_x":{"type":"number","exclusiveMinimum":0,"title":"Scale X","description":"Original image width divided by output-grid width."},"scale_y":{"type":"number","exclusiveMinimum":0,"title":"Scale Y","description":"Original image height divided by output-grid height."}},"type":"object","required":["width","height","scale_x","scale_y"],"title":"MaskCoordinateMetadata","description":"Describe the prediction grid and its mapping to the original image.\n\nAttributes:\n coordinate_system (str): Coordinate frame shared by boxes, polygons and encoded RLE masks.\n width (int): Width of the encoded mask grid.\n height (int): Height of the encoded mask grid.\n scale_x (float): Multiply mask x coordinates by this to obtain image x.\n scale_y (float): Multiply mask y coordinates by this to obtain image y."},"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"},"resolved_model":{"anyOf":[{"$ref":"#/components/schemas/ResolvedModel"},{"type":"null"}],"description":"Model identity and available package details for this result."},"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"},"resolved_model":{"anyOf":[{"$ref":"#/components/schemas/ResolvedModel"},{"type":"null"}],"description":"Model identity and available package details for this result."},"image":{"anyOf":[{"items":{"$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"},"AnomalyDetectionResponse":{"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"},"resolved_model":{"anyOf":[{"$ref":"#/components/schemas/ResolvedModel"},{"type":"null"}],"description":"Model identity and available package details for this result."},"image":{"anyOf":[{"items":{"$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"},"anomaly_score":{"type":"number","title":"Anomaly Score","description":"Raw anomaly score; larger means more anomalous"},"anomaly_threshold":{"type":"number","title":"Anomaly Threshold","description":"Decision threshold fitted on validation images"},"anomaly_scale":{"type":"number","title":"Anomaly Scale","description":"Calibration scale: the anomalous confidence is sigmoid((anomaly_score - anomaly_threshold) / anomaly_scale)"},"is_anomalous":{"type":"boolean","title":"Is Anomalous","description":"Whether the anomalous confidence reached the request confidence"},"anomaly_map":{"anyOf":[{"$ref":"#/components/schemas/AnomalyMapPayload"},{"type":"null"}],"description":"Raw local anomaly evidence at the network input resolution"}},"type":"object","required":["image","predictions","anomaly_score","anomaly_threshold","anomaly_scale","is_anomalous"],"title":"AnomalyDetectionResponse"},"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."},"AnomalyMapPayload":{"properties":{"shape":{"items":{"type":"integer"},"type":"array","title":"Shape","description":"[height, width] of the map"},"dtype":{"type":"string","const":"float32","title":"Dtype","description":"Element type of the decoded `data`"},"data":{"type":"string","title":"Data","description":"Base64 of the row-major little-endian float32 map values"}},"type":"object","required":["shape","dtype","data"],"title":"AnomalyMapPayload","description":"Raw anomaly heatmap at the network input resolution.\n\n`data` is base64 of the row-major little-endian float32 values. The map covers\nthe whole image as the network saw it (resized to a square), so resizing it to\nthe image's width and height is a plain stretch. Values share the units of\n`anomaly_score` and are unbounded above."},"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"},"resolved_model":{"anyOf":[{"$ref":"#/components/schemas/ResolvedModel"},{"type":"null"}],"description":"Model identity and available package details for this result."},"image":{"anyOf":[{"items":{"$ref":"#/components/schemas/InferenceResponseImage"},"type":"array"},{"$ref":"#/components/schemas/InferenceResponseImage"}],"title":"Image"},"predictions":{"items":{"$ref":"#/components/schemas/ClassificationPrediction"},"type":"array","title":"Predictions"},"top":{"type":"string","title":"Top","description":"The top predicted class label","default":""},"confidence":{"type":"number","title":"Confidence","description":"The confidence of the top predicted class label","default":0},"parent_id":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Parent Id","description":"Identifier of parent image region. Useful when stack of detection-models is in use to refer the RoI being the input to inference"}},"type":"object","required":["image","predictions"],"title":"ClassificationInferenceResponse","description":"Classification inference response.\n\nAttributes:\n predictions (List[inference.core.entities.responses.inference.ClassificationPrediction]): List of classification predictions.\n top (str): The top predicted class label.\n confidence (float): The confidence of the top predicted class label."},"MultiLabelClassificationInferenceResponse":{"properties":{"visualization":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Visualization","description":"Base64 encoded string containing prediction visualization image data"},"inference_id":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Inference Id","description":"Unique identifier of inference"},"frame_id":{"anyOf":[{"type":"integer"},{"type":"null"}],"title":"Frame Id","description":"The frame id of the image used in inference if the input was a video"},"time":{"anyOf":[{"type":"number"},{"type":"null"}],"title":"Time","description":"The time in seconds it took to produce the predictions including image preprocessing"},"resolved_model":{"anyOf":[{"$ref":"#/components/schemas/ResolvedModel"},{"type":"null"}],"description":"Model identity and available package details for this result."},"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"},"resolved_model":{"anyOf":[{"$ref":"#/components/schemas/ResolvedModel"},{"type":"null"}],"description":"Model identity and available package details for this result."},"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"},"resolved_model":{"anyOf":[{"$ref":"#/components/schemas/ResolvedModel"},{"type":"null"}],"description":"Model identity and available package details for this result."},"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"}}}} ``` ### किसी छवि पर मॉडल चलाएँ Roboflow कई runtime के माध्यम से inference उपलब्ध कराता है - सही विकल्प इस पर निर्भर करता है कि आप एक अकेले मॉडल को कॉल कर रहे हैं या एक Workflow को, आपको कितने throughput की ज़रूरत है, और workload कहाँ चलता है। यह पृष्ठ एक संक्षिप्त अवलोकन है। विस्तृत inference संदर्भ में उपलब्ध है [उत्पाद दस्तावेज़ीकरण](/deployment/hi/readme.md), जो उसी docs साइट का हिस्सा है। गहन सामग्री जहाँ है, वहाँ क्रॉस-लिंक दिए गए हैं। #### Inference रनटाइम | रनटाइम | इसका उपयोग कब करें | संदर्भ | | ---------------------------------------------------- | --------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Serverless Cloud API** (`serverless.roboflow.com`) | डिफ़ॉल्ट। होस्टेड, ऑटो-स्केलिंग, मॉडलों और Workflows का समर्थन करता है। | [Serverless Cloud API](/deployment/hi/roboflow-cloud/serverless-api.md) | | **समर्पित परिनियोजन** | आपको पूर्वानुमेय latency, उच्च throughput, या pinned GPU प्रकार की आवश्यकता है। Roboflow द्वारा प्रबंधित। | [समर्पित परिनियोजन](/deployment/hi/roboflow-cloud/dedicated-deployments.md#http-api) और [उत्पाद अवलोकन](/deployment/hi/roboflow-cloud/dedicated-deployments.md) | | **Roboflow Inference** (self-hosted) | ऑन-प्रेम, edge डिवाइस, air-gapped वातावरण, या ऐसे workload जो आपके VPC से बाहर नहीं जा सकते। ओपन सोर्स। | [स्व-होस्टेड परिनियोजन](/deployment/hi/self-hosted/self-hosted.md) | #### Serverless Cloud API को कॉल करना एक मॉडल चलाएँ: ```bash curl -F "file=@photo.jpg" \ -H "Authorization: Bearer $ROBOFLOW_API_KEY" \ "https://serverless.roboflow.com//?confidence=0.5" ``` एक Workflow चलाएँ: ```bash curl -X POST "https://serverless.roboflow.com/infer/workflows//" \ -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" %} कुंजी को एक के रूप में भेजना `?api_key=` क्वेरी पैरामीटर या `api_key` बॉडी फ़ील्ड पुराना चैनल है। यह अभी भी काम करता है, लेकिन `Authorization: Bearer` हेडर आपकी कुंजी को URLs और लॉग्स से बाहर रखता है। देखें [REST API के साथ प्रमाणीकरण करें](https://docs.roboflow.com/reference/platform/rest-api/authenticate-with-the-rest-api). {% endhint %} लाइव वीडियो के लिए, देखें [Serverless Video Streaming API](/deployment/hi/roboflow-cloud/serverless-api/serverless-video-streaming-api.md). बड़ी छवि और वीडियो सेटों के असिंक्रोनस प्रसंस्करण के लिए, देखें [बैच प्रोसेसिंग](/deployment/hi/roboflow-cloud/batch-processing.md). #### अप्रचलित: Serverless v1 पुराने, कार्य-विशिष्ट एंडपॉइंट - `detect.roboflow.com`, `classify.roboflow.com`, `outline.roboflow.com`, `segment.roboflow.com` - हैं **अप्रचलित**. वे पिछड़ी संगतता के लिए अभी भी प्रतिक्रिया देते हैं, लेकिन नए कोड को उपयोग करना चाहिए `serverless.roboflow.com` की जगह। यदि आपको किसी स्निपेट में एक `*.roboflow.com` कार्य होस्ट की ओर इशारा करते हुए मिले, तो उसे पुराने रूप में मानें और ऊपर दिए गए Serverless Cloud API रूप में अनुवाद करें। ## Python SDK ### Python SDK के साथ उपयोग करें यदि आप Python में काम कर रहे हैं, तो Serverless Cloud API के साथ इंटरैक्ट करने का सबसे सुविधाजनक तरीका Inference Python SDK का उपयोग करना है। का उपयोग करने के लिए [Inference SDK](https://docs.roboflow.com/reference/inference/inference-sdk), पहले इसे इंस्टॉल करें: ``` pip install inference-sdk ``` Serverless Cloud API को अनुरोध भेजने के लिए, निम्न कोड का उपयोग करें: 
from inference_sdk import InferenceHTTPClient, InferenceConfiguration

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)
ऊपर, अपना [model ID](https://docs.roboflow.com/reference/authentication/authentication/workspace-and-project-ids) और [API key](https://docs.roboflow.com/reference/authentication/authentication/find-your-roboflow-api-key). यह कोड आपका मॉडल चलाएगा और परिणाम लौटाएगा। #### Roboflow Instant Model Serverless Cloud API Roboflow को चलाने का भी समर्थन करता है [Instant Model](https://docs.roboflow.com/models/train/roboflow-instant). आप Instant Model को किसी भी अन्य मॉडल की तरह चला सकते हैं, बस ध्यान दें कि confidence threshold Instant Models के लिए संवेदनशील हो सकता है। {% hint style="info" %} एक सर्वोत्तम confidence मॉडल के प्रशिक्षित होने वाली छवियों की संख्या पर निर्भर करता है। सर्वोत्तम confidence thresholds आमतौर पर 0.85 से 0.99 के बीच होते हैं। {% 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(...)` पूरी कॉन्फ़िगरेशन को बदल देता है, इसलिए `api_key_transport` को किसी भी कॉन्फ़िगरेशन में रखें जिसे आप लागू करते हैं। ### Python SDK के साथ वीडियो स्ट्रीम करें वीडियो पर एक object detection model चलाने के लिए Inference SDK WebRTC client का उपयोग करें। Serverless Video Streaming API वीडियो को Roboflow Cloud में संसाधित करता है और प्रत्येक frame के लिए predictions लौटाता है। SDK को इसके WebRTC dependencies और `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() ``` बदलें `API_KEY` और `model-id/1` को अपनी API key और model ID से। जानें कि वेबकैम और RTSP कैमरों से स्ट्रीम कैसे करें, हर frame को कैसे संसाधित करें, या [Serverless Video Streaming API गाइड](/deployment/hi/roboflow-cloud/serverless-api/serverless-video-streaming-api.md). ## CLI आप Roboflow CLI का उपयोग Roboflow पर प्रशिक्षित मॉडल या Roboflow Universe पर उपलब्ध open source models के साथ चलाने के लिए कर सकते हैं। [Roboflow Universe](https://universe.roboflow.com). चलाकर `roboflow infer` कमांड लाइन में, CLI छवि को Roboflow API पर भेजता है और predictions प्रिंट करता है। ### कमांड ```bash roboflow infer -m ``` #### विकल्प | फ़्लैग | विवरण | | -------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- | | `-m`, `--model` | में model ID `project/version` फ़ॉर्मेट (आवश्यक) | | `-c`, `--confidence` | विश्वास सीमा, 0.0–1.0 (डिफ़ॉल्ट: 0.5) | | `-o`, `--overlap` | ओवरलैप/NMS सीमा, 0.0–1.0 (डिफ़ॉल्ट: 0.5) | | `-t`, `--type` | मॉडल प्रकार (स्वतः-पहचान छोड़ें): `object-detection`, `classification`, `instance-segmentation`, `semantic-segmentation`, `keypoint-detection` | ### उदाहरण Roboflow Universe के एक open source model का उपयोग करके inference चलाएँ - उदाहरण के लिए, [poker-cards](https://universe.roboflow.com/roboflow-100/poker-cards-cxcvz/model/1) डेटासेट: ```bash roboflow infer ~/Downloads/ace.jpg -m poker-cards-cxcvz/1 -c 0.7 ``` वर्कस्पेस डिफ़ॉल्ट रूप से आपका कॉन्फ़िगर किया हुआ workspace होता है। किसी अलग workspace के मॉडल का उपयोग करने के लिए: ```bash roboflow infer photo.jpg -m poker-cards-cxcvz/1 -w roboflow-100 ``` स्वतः-पहचान API कॉल को छोड़ने के लिए model type निर्दिष्ट करें: ```bash roboflow infer photo.jpg -m my-project/3 -t object-detection ``` ### JSON आउटपुट उपयोग करें `--json` स्क्रिप्टिंग और ऑटोमेशन के लिए संरचित prediction data प्राप्त करने हेतु: ```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 } ] } ``` सभी समर्थित पैरामीटर के लिए देखें `roboflow infer --help`. ## MCP सर्वर अपने AI agent को [MCP सर्वर](https://docs.roboflow.com/agents/mcp-server) से जोड़ें और यह इन टूल्स के साथ किसी छवि पर मॉडल चला सकता है:
टूलविवरण
models_inferप्रशिक्षित मॉडल का उपयोग करके किसी छवि पर hosted inference चलाएँ।
workflows_runएक या अधिक छवियों पर सहेजा गया Workflow निष्पादित करें।
project_deployment_runप्रोजेक्ट के स्थिर live endpoint के माध्यम से inference चलाएँ।