Inference Server OpenAPI
セルフホストの Roboflow Inference Server の HTTP API を閲覧・テストします。
pip install inference-cli
inference server startサーバー
Successful Response
Server version information.
Attributes: name (str): Server name. version (str): Server version. uuid (str): Server UUID.
Roboflow Inference Server0.0.19c18c6f4-2266-41fb-8a0f-c12ae28f6fbeGET /info HTTP/1.1
Host: localhost:9001
Accept: */*
Successful Response
{
"name": "Roboflow Inference Server",
"version": "0.0.1",
"uuid": "9c18c6f4-2266-41fb-8a0f-c12ae28f6fbe"
}Maximum number of log entries to return
100Filter by log level (DEBUG, INFO, WARNING, ERROR, CRITICAL)
Return logs since this ISO timestamp
Successful Response
Validation Error
GET /logs HTTP/1.1
Host: localhost:9001
Accept: */*
コンテンツなし
Successful Response
Total estimated VRAM consumed by all loaded models in bytes.
Current GPU memory in use in bytes (device-level, includes all runtimes).
Total GPU memory available in bytes.
Live tensor memory allocated by PyTorch's CUDA allocator in bytes.
Total memory reserved by PyTorch's CUDA allocator in bytes.
Reserved but currently unallocated PyTorch CUDA memory in bytes.
Device memory not reserved by PyTorch in bytes. This includes native runtimes, CUDA context overhead, and allocations from other processes.
GET /model/registry HTTP/1.1
Host: localhost:9001
Accept: */*
Successful Response
{
"models": [
{
"model_id": "some-project/3",
"task_type": "classification",
"batch_size": 1,
"input_height": 1,
"input_width": 1,
"vram_bytes": 1,
"request_aliases": [
"text"
],
"request_paths": [
"text"
]
}
],
"total_vram_bytes": 1,
"gpu_memory_used": 1,
"gpu_memory_total": 1,
"torch_cuda_allocated": 1,
"torch_cuda_reserved": 1,
"torch_cuda_allocator_cache": 1,
"non_torch_gpu_memory": 1
}コア推論
ID of a Roboflow dataset corresponding to the model to use for inference OR workspace ID
ID of a Roboflow dataset version corresponding to the model to use for inference OR model ID
Roboflow API Key that will be passed to the model during initialization for artifact retrieval
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.
0.4The confidence threshold used to filter out keypoints that are not visible based on model confidence
0One 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.
jsonThe publically accessible URL of an image to use for inference.
One of base64 or numpy. Note, numpy input is not supported for Roboflow Hosted Inference.
base64If true, labels will be include in any inference visualization.
falseOne 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.
accurateThe amount to tradeoff between 0='fast' and 1='accurate'
0The 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.
300The IoU threhsold that must be met for a box pair to be considered duplicate during NMS
0.3The stroke width used when visualizing predictions
1If true, disables automatic image orientation
falseIf true, disables automatic contrast adjustment
falseIf true, disables automatic grayscale conversion
falseIf true, disables automatic static crop
falseIf true, the predictions will be prevented from registration by Active Learning (if the functionality is enabled)
falseParameter to be used when Active Learning data registration should happen against different dataset than the one pointed by model_id
The source of the inference request
externalThe detailed source information of the inference request
externalThe format of the prediction mask - polygon (default) or rle - applicable for instance segmentation models.
polygon可能な値: Successful Response
Validation Error
GET /{dataset_id}/{version_id} HTTP/1.1
Host: localhost:9001
Accept: */*
{
"visualization": "text",
"inference_id": "text",
"frame_id": 1,
"time": 1,
"image": [
{
"width": 1,
"height": 1
}
],
"predictions": [
{
"x": 1,
"y": 1,
"width": 1,
"height": 1,
"confidence": 1,
"class": "text",
"class_id": 1,
"detection_id": "text",
"parent_id": "text",
"class_confidence": 1,
"points": [
{
"x": 1,
"y": 1
}
],
"mask_format": "polygon"
}
]
}ID of a Roboflow dataset corresponding to the model to use for inference OR workspace ID
ID of a Roboflow dataset version corresponding to the model to use for inference OR model ID
Roboflow API Key that will be passed to the model during initialization for artifact retrieval
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.
0.4The confidence threshold used to filter out keypoints that are not visible based on model confidence
0One 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.
jsonThe publically accessible URL of an image to use for inference.
One of base64 or numpy. Note, numpy input is not supported for Roboflow Hosted Inference.
base64If true, labels will be include in any inference visualization.
falseOne 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.
accurateThe amount to tradeoff between 0='fast' and 1='accurate'
0The 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.
300The IoU threhsold that must be met for a box pair to be considered duplicate during NMS
0.3The stroke width used when visualizing predictions
1If true, disables automatic image orientation
falseIf true, disables automatic contrast adjustment
falseIf true, disables automatic grayscale conversion
falseIf true, disables automatic static crop
falseIf true, the predictions will be prevented from registration by Active Learning (if the functionality is enabled)
falseParameter to be used when Active Learning data registration should happen against different dataset than the one pointed by model_id
The source of the inference request
externalThe detailed source information of the inference request
externalThe format of the prediction mask - polygon (default) or rle - applicable for instance segmentation models.
polygon可能な値: Successful Response
Validation Error
POST /{dataset_id}/{version_id} HTTP/1.1
Host: localhost:9001
Accept: */*
{
"visualization": "text",
"inference_id": "text",
"frame_id": 1,
"time": 1,
"image": [
{
"width": 1,
"height": 1
}
],
"predictions": [
{
"x": 1,
"y": 1,
"width": 1,
"height": 1,
"confidence": 1,
"class": "text",
"class_id": 1,
"detection_id": "text",
"parent_id": "text",
"class_confidence": 1,
"points": [
{
"x": 1,
"y": 1
}
],
"mask_format": "polygon"
}
]
}Roboflow API Key that will be passed to the model during initialization for artifact retrieval
Request for depth estimation.
Attributes: image (Union[List[InferenceRequestImage], InferenceRequestImage]): Image(s) to be estimated. model_id (str): The model ID to use for depth estimation. depth_version_id (Optional[str]): The version ID of the depth estimation model. depth_map_format (Literal["json", "png16", "png8"]): Serialization format for the normalized depth map in the response.
Roboflow API Key that will be passed to the model during initialization for artifact retrieval
trueInternal stream-pipeline frame pairing id. Not part of the public API.
If true, disables model monitoring for this request
falseThe type of the model, usually referring to what task the model performs
object-detectionThe version ID of the depth estimation model
smallExample: smallSerialization format for normalized_depth in the response: json (default, wire-compatible with older clients) returns the nested float list; png16 returns a base64 16-bit grayscale PNG (quantization step 1/65535, typically >10x smaller payload - inference_sdk decodes it back to a numpy array when requested via depth_map_format='png16'); png8 returns a base64 8-bit grayscale PNG (256 depth levels, roughly another order of magnitude smaller - fine for visualization/thresholding, lossy for geometric use).
json可能な値: Successful Response
Response for depth estimation inference.
Attributes:
normalized_depth (Union[str, List[List[float]]]): The per-image normalized ordinal
depth map as a 2D array of floats between 0 and 1. Higher values
indicate nearer predictions.
serialized according to the request's depth_map_format: a 2D array of
floats between 0 and 1 (json, the default) or a base64 grayscale PNG
string (16-bit for png16, 8-bit for png8).
depth_map_format (Literal["json", "png16", "png8"]): The serialization
format used for normalized_depth.
image (Optional[str]): Base64 encoded visualization of the depth map if visualize_predictions is True.
time (float): The processing time in seconds.
visualization (Optional[str]): Base64 encoded visualization of the depth map if visualize_predictions is True.
Per-image normalized ordinal depth as a 2D array of floats between 0 and 1, where 1 is nearest and 0 is farthest. Values are not physical distances or directly comparable across images or model families without calibration. The normalized depth map: a 2D array of floats between 0 and 1 (json format, default) or a base64 grayscale PNG string (png16/png8), per the request's depth_map_format
The serialization format used for normalized_depth
json可能な値: Base64 encoded visualization of the depth map if visualize_predictions is True
Validation Error
POST /infer/depth-estimation HTTP/1.1
Host: localhost:9001
Content-Type: application/json
Accept: */*
Content-Length: 339
{
"id": "text",
"api_key": "text",
"usage_billable": true,
"start": 1,
"source": "text",
"source_info": "text",
"stream_pipeline_context_id": "text",
"disable_model_monitoring": false,
"model_id": "text",
"model_type": "object-detection",
"image": [
{
"type": "url",
"value": "http://www.example-image-url.com"
}
],
"depth_version_id": "small",
"depth_map_format": "json"
}{
"normalized_depth": "text",
"depth_map_format": "json",
"image": "text"
}Roboflow API Key that will be passed to the model during initialization for artifact retrieval
Request for depth estimation.
Attributes: image (Union[List[InferenceRequestImage], InferenceRequestImage]): Image(s) to be estimated. model_id (str): The model ID to use for depth estimation. depth_version_id (Optional[str]): The version ID of the depth estimation model. depth_map_format (Literal["json", "png16", "png8"]): Serialization format for the normalized depth map in the response.
Roboflow API Key that will be passed to the model during initialization for artifact retrieval
trueInternal stream-pipeline frame pairing id. Not part of the public API.
If true, disables model monitoring for this request
falseThe type of the model, usually referring to what task the model performs
object-detectionThe version ID of the depth estimation model
smallExample: smallSerialization format for normalized_depth in the response: json (default, wire-compatible with older clients) returns the nested float list; png16 returns a base64 16-bit grayscale PNG (quantization step 1/65535, typically >10x smaller payload - inference_sdk decodes it back to a numpy array when requested via depth_map_format='png16'); png8 returns a base64 8-bit grayscale PNG (256 depth levels, roughly another order of magnitude smaller - fine for visualization/thresholding, lossy for geometric use).
json可能な値: Successful Response
Response for depth estimation inference.
Attributes:
normalized_depth (Union[str, List[List[float]]]): The per-image normalized ordinal
depth map as a 2D array of floats between 0 and 1. Higher values
indicate nearer predictions.
serialized according to the request's depth_map_format: a 2D array of
floats between 0 and 1 (json, the default) or a base64 grayscale PNG
string (16-bit for png16, 8-bit for png8).
depth_map_format (Literal["json", "png16", "png8"]): The serialization
format used for normalized_depth.
image (Optional[str]): Base64 encoded visualization of the depth map if visualize_predictions is True.
time (float): The processing time in seconds.
visualization (Optional[str]): Base64 encoded visualization of the depth map if visualize_predictions is True.
Per-image normalized ordinal depth as a 2D array of floats between 0 and 1, where 1 is nearest and 0 is farthest. Values are not physical distances or directly comparable across images or model families without calibration. The normalized depth map: a 2D array of floats between 0 and 1 (json format, default) or a base64 grayscale PNG string (png16/png8), per the request's depth_map_format
The serialization format used for normalized_depth
json可能な値: Base64 encoded visualization of the depth map if visualize_predictions is True
Validation Error
POST /infer/depth-estimation/{model_id} HTTP/1.1
Host: localhost:9001
Content-Type: application/json
Accept: */*
Content-Length: 339
{
"id": "text",
"api_key": "text",
"usage_billable": true,
"start": 1,
"source": "text",
"source_info": "text",
"stream_pipeline_context_id": "text",
"disable_model_monitoring": false,
"model_id": "text",
"model_type": "object-detection",
"image": [
{
"type": "url",
"value": "http://www.example-image-url.com"
}
],
"depth_version_id": "small",
"depth_map_format": "json"
}{
"normalized_depth": "text",
"depth_map_format": "json",
"image": "text"
}Roboflow API Key that will be passed to the model during initialization for artifact retrieval
Roboflow API Key that will be passed to the model during initialization for artifact retrieval
trueInternal stream-pipeline frame pairing id. Not part of the public API.
If true, disables model monitoring for this request
falseA unique model identifier
raccoon-detector-1The type of the model, usually referring to what task the model performs
object-detectionIf true, the auto orient preprocessing step is disabled for this call.
falseIf true, the auto contrast preprocessing step is disabled for this call.
falseIf true, the grayscale preprocessing step is disabled for this call.
falseIf true, the static crop preprocessing step is disabled for this call.
falseIf set, use this prompt to guide the LMM
captionIf true, enables thinking/reasoning mode for models that support it (e.g. Qwen3.5). The model's reasoning will be included in the response.
falseMaximum number of tokens to generate. If not set, the model's default will be used.
Successful Response
Validation Error
POST /infer/lmm HTTP/1.1
Host: localhost:9001
Content-Type: application/json
Accept: */*
Content-Length: 501
{
"id": "text",
"api_key": "text",
"usage_billable": true,
"start": 1,
"source": "text",
"source_info": "text",
"stream_pipeline_context_id": "text",
"disable_model_monitoring": false,
"model_id": "raccoon-detector-1",
"model_type": "object-detection",
"image": [
{
"type": "url",
"value": "http://www.example-image-url.com"
}
],
"disable_preproc_auto_orient": false,
"disable_preproc_contrast": false,
"disable_preproc_grayscale": false,
"disable_preproc_static_crop": false,
"prompt": "caption",
"enable_thinking": false,
"max_new_tokens": 1
}{
"inference_id": "text",
"frame_id": 1,
"time": 1,
"image": [
{
"width": 1,
"height": 1
}
],
"response": "text"
}Roboflow API Key that will be passed to the model during initialization for artifact retrieval
Roboflow API Key that will be passed to the model during initialization for artifact retrieval
trueInternal stream-pipeline frame pairing id. Not part of the public API.
If true, disables model monitoring for this request
falseA unique model identifier
raccoon-detector-1The type of the model, usually referring to what task the model performs
object-detectionIf true, the auto orient preprocessing step is disabled for this call.
falseIf true, the auto contrast preprocessing step is disabled for this call.
falseIf true, the grayscale preprocessing step is disabled for this call.
falseIf true, the static crop preprocessing step is disabled for this call.
falseIf set, use this prompt to guide the LMM
captionIf true, enables thinking/reasoning mode for models that support it (e.g. Qwen3.5). The model's reasoning will be included in the response.
falseMaximum number of tokens to generate. If not set, the model's default will be used.
Successful Response
Validation Error
POST /infer/lmm/{model_id} HTTP/1.1
Host: localhost:9001
Content-Type: application/json
Accept: */*
Content-Length: 501
{
"id": "text",
"api_key": "text",
"usage_billable": true,
"start": 1,
"source": "text",
"source_info": "text",
"stream_pipeline_context_id": "text",
"disable_model_monitoring": false,
"model_id": "raccoon-detector-1",
"model_type": "object-detection",
"image": [
{
"type": "url",
"value": "http://www.example-image-url.com"
}
],
"disable_preproc_auto_orient": false,
"disable_preproc_contrast": false,
"disable_preproc_grayscale": false,
"disable_preproc_static_crop": false,
"prompt": "caption",
"enable_thinking": false,
"max_new_tokens": 1
}{
"inference_id": "text",
"frame_id": 1,
"time": 1,
"image": [
{
"width": 1,
"height": 1
}
],
"response": "text"
}埋め込みと比較
Roboflow API Key that will be passed to the model during initialization for artifact retrieval
Request for CLIP comparison.
Attributes: subject (Union[InferenceRequestImage, str]): The type of image data provided, one of 'url' or 'base64'. subject_type (str): The type of subject, one of 'image' or 'text'. prompt (Union[List[InferenceRequestImage], InferenceRequestImage, str, List[str], Dict[str, Union[InferenceRequestImage, str]]]): The prompt for comparison. prompt_type (str): The type of prompt, one of 'image' or 'text'.
Roboflow API Key that will be passed to the model during initialization for artifact retrieval
trueInternal stream-pipeline frame pairing id. Not part of the public API.
If true, disables model monitoring for this request
falseThe version ID of CLIP to be used for this request. Must be one of RN101, RN50, RN50x16, RN50x4, RN50x64, ViT-B-16, ViT-B-32, ViT-L-14-336px, and ViT-L-14.
ViT-B-16Example: ViT-B-16The type of image data provided, one of 'url' or 'base64'
urlThe type of subject, one of 'image' or 'text'
imageExample: imageThe type of prompt, one of 'image' or 'text'
textExample: textSuccessful Response
Response for CLIP comparison.
Attributes: similarity (Union[List[float], Dict[str, float]]): Similarity scores. time (float): The time in seconds it took to produce the similarity scores including preprocessing.
Unique identifier of inference
The frame id of the image used in inference if the input was a video
The time in seconds it took to produce the similarity scores including preprocessing
Identifier of parent image region. Useful when stack of detection-models is in use to refer the RoI being the input to inference
Validation Error
POST /clip/compare HTTP/1.1
Host: localhost:9001
Content-Type: application/json
Accept: */*
Content-Length: 344
{
"id": "text",
"api_key": "text",
"usage_billable": true,
"start": 1,
"source": "text",
"source_info": "text",
"stream_pipeline_context_id": "text",
"disable_model_monitoring": false,
"clip_version_id": "ViT-B-16",
"model_id": "text",
"subject": "url",
"subject_type": "image",
"prompt": [
{
"type": "url",
"value": "http://www.example-image-url.com"
}
],
"prompt_type": "text"
}{
"inference_id": "text",
"frame_id": 1,
"time": 1,
"similarity": [
1
],
"parent_id": "text"
}Roboflow API Key that will be passed to the model during initialization for artifact retrieval
Request for CLIP image embedding.
Attributes: image (Union[List[InferenceRequestImage], InferenceRequestImage]): Image(s) to be embedded.
Roboflow API Key that will be passed to the model during initialization for artifact retrieval
trueInternal stream-pipeline frame pairing id. Not part of the public API.
If true, disables model monitoring for this request
falseThe version ID of CLIP to be used for this request. Must be one of RN101, RN50, RN50x16, RN50x4, RN50x64, ViT-B-16, ViT-B-32, ViT-L-14-336px, and ViT-L-14.
ViT-B-16Example: ViT-B-16Successful Response
Response for CLIP embedding.
Attributes: embeddings (List[List[float]]): A list of embeddings, each embedding is a list of floats. time (float): The time in seconds it took to produce the embeddings including preprocessing.
Unique identifier of inference
The frame id of the image used in inference if the input was a video
The time in seconds it took to produce the embeddings including preprocessing
Validation Error
POST /clip/embed_image HTTP/1.1
Host: localhost:9001
Content-Type: application/json
Accept: */*
Content-Length: 283
{
"id": "text",
"api_key": "text",
"usage_billable": true,
"start": 1,
"source": "text",
"source_info": "text",
"stream_pipeline_context_id": "text",
"disable_model_monitoring": false,
"clip_version_id": "ViT-B-16",
"model_id": "text",
"image": [
{
"type": "url",
"value": "http://www.example-image-url.com"
}
]
}{
"inference_id": "text",
"frame_id": 1,
"time": 1,
"embeddings": "[[0.12, 0.23, 0.34, ..., 0.43]]"
}Roboflow API Key that will be passed to the model during initialization for artifact retrieval
Request for CLIP text embedding.
Attributes: text (Union[List[str], str]): A string or list of strings.
Roboflow API Key that will be passed to the model during initialization for artifact retrieval
trueInternal stream-pipeline frame pairing id. Not part of the public API.
If true, disables model monitoring for this request
falseThe version ID of CLIP to be used for this request. Must be one of RN101, RN50, RN50x16, RN50x4, RN50x64, ViT-B-16, ViT-B-32, ViT-L-14-336px, and ViT-L-14.
ViT-B-16Example: ViT-B-16A string or list of strings
The quick brown fox jumps over the lazy dogSuccessful Response
Response for CLIP embedding.
Attributes: embeddings (List[List[float]]): A list of embeddings, each embedding is a list of floats. time (float): The time in seconds it took to produce the embeddings including preprocessing.
Unique identifier of inference
The frame id of the image used in inference if the input was a video
The time in seconds it took to produce the embeddings including preprocessing
Validation Error
POST /clip/embed_text HTTP/1.1
Host: localhost:9001
Content-Type: application/json
Accept: */*
Content-Length: 268
{
"id": "text",
"api_key": "text",
"usage_billable": true,
"start": 1,
"source": "text",
"source_info": "text",
"stream_pipeline_context_id": "text",
"disable_model_monitoring": false,
"clip_version_id": "ViT-B-16",
"model_id": "text",
"text": "The quick brown fox jumps over the lazy dog"
}{
"inference_id": "text",
"frame_id": 1,
"time": 1,
"embeddings": "[[0.12, 0.23, 0.34, ..., 0.43]]"
}Roboflow API Key that will be passed to the model during initialization for artifact retrieval
Request for PERCEPTION_ENCODER comparison.
Attributes: subject (Union[InferenceRequestImage, str]): The type of image data provided, one of 'url' or 'base64'. subject_type (str): The type of subject, one of 'image' or 'text'. prompt (Union[List[InferenceRequestImage], InferenceRequestImage, str, List[str], Dict[str, Union[InferenceRequestImage, str]]]): The prompt for comparison. prompt_type (str): The type of prompt, one of 'image' or 'text'.
Roboflow API Key that will be passed to the model during initialization for artifact retrieval
trueInternal stream-pipeline frame pairing id. Not part of the public API.
If true, disables model monitoring for this request
falseThe version ID of PERCEPTION_ENCODER to be used for this request. Must be one of RN101, RN50, RN50x16, RN50x4, RN50x64, ViT-B-16, ViT-B-32, ViT-L-14-336px, and ViT-L-14.
PE-Core-L14-336Example: PE-Core-L14-336The type of image data provided, one of 'url' or 'base64'
urlThe type of subject, one of 'image' or 'text'
imageExample: imageThe type of prompt, one of 'image' or 'text'
textExample: textSuccessful Response
Response for PERCEPTION_ENCODER comparison.
Attributes: similarity (Union[List[float], Dict[str, float]]): Similarity scores. time (float): The time in seconds it took to produce the similarity scores including preprocessing.
Unique identifier of inference
The frame id of the image used in inference if the input was a video
The time in seconds it took to produce the similarity scores including preprocessing
Identifier of parent image region. Useful when stack of detection-models is in use to refer the RoI being the input to inference
Validation Error
POST /perception_encoder/compare HTTP/1.1
Host: localhost:9001
Content-Type: application/json
Accept: */*
Content-Length: 365
{
"id": "text",
"api_key": "text",
"usage_billable": true,
"start": 1,
"source": "text",
"source_info": "text",
"stream_pipeline_context_id": "text",
"disable_model_monitoring": false,
"perception_encoder_version_id": "PE-Core-L14-336",
"model_id": "text",
"subject": "url",
"subject_type": "image",
"prompt": [
{
"type": "url",
"value": "http://www.example-image-url.com"
}
],
"prompt_type": "text"
}{
"inference_id": "text",
"frame_id": 1,
"time": 1,
"similarity": [
1
],
"parent_id": "text"
}Roboflow API Key that will be passed to the model during initialization for artifact retrieval
Request for PERCEPTION_ENCODER image embedding.
Attributes: image (Union[List[InferenceRequestImage], InferenceRequestImage]): Image(s) to be embedded.
Roboflow API Key that will be passed to the model during initialization for artifact retrieval
trueInternal stream-pipeline frame pairing id. Not part of the public API.
If true, disables model monitoring for this request
falseThe version ID of PERCEPTION_ENCODER to be used for this request. Must be one of RN101, RN50, RN50x16, RN50x4, RN50x64, ViT-B-16, ViT-B-32, ViT-L-14-336px, and ViT-L-14.
PE-Core-L14-336Example: PE-Core-L14-336Successful Response
Response for PERCEPTION_ENCODER embedding.
Attributes: embeddings (List[List[float]]): A list of embeddings, each embedding is a list of floats. time (float): The time in seconds it took to produce the embeddings including preprocessing.
Unique identifier of inference
The frame id of the image used in inference if the input was a video
The time in seconds it took to produce the embeddings including preprocessing
Validation Error
POST /perception_encoder/embed_image HTTP/1.1
Host: localhost:9001
Content-Type: application/json
Accept: */*
Content-Length: 304
{
"id": "text",
"api_key": "text",
"usage_billable": true,
"start": 1,
"source": "text",
"source_info": "text",
"stream_pipeline_context_id": "text",
"disable_model_monitoring": false,
"perception_encoder_version_id": "PE-Core-L14-336",
"model_id": "text",
"image": [
{
"type": "url",
"value": "http://www.example-image-url.com"
}
]
}{
"inference_id": "text",
"frame_id": 1,
"time": 1,
"embeddings": "[[0.12, 0.23, 0.34, ..., 0.43]]"
}Roboflow API Key that will be passed to the model during initialization for artifact retrieval
Request for PERCEPTION_ENCODER text embedding.
Attributes: text (Union[List[str], str]): A string or list of strings.
Roboflow API Key that will be passed to the model during initialization for artifact retrieval
trueInternal stream-pipeline frame pairing id. Not part of the public API.
If true, disables model monitoring for this request
falseThe version ID of PERCEPTION_ENCODER to be used for this request. Must be one of RN101, RN50, RN50x16, RN50x4, RN50x64, ViT-B-16, ViT-B-32, ViT-L-14-336px, and ViT-L-14.
PE-Core-L14-336Example: PE-Core-L14-336A string or list of strings
The quick brown fox jumps over the lazy dogSuccessful Response
Response for PERCEPTION_ENCODER embedding.
Attributes: embeddings (List[List[float]]): A list of embeddings, each embedding is a list of floats. time (float): The time in seconds it took to produce the embeddings including preprocessing.
Unique identifier of inference
The frame id of the image used in inference if the input was a video
The time in seconds it took to produce the embeddings including preprocessing
Validation Error
POST /perception_encoder/embed_text HTTP/1.1
Host: localhost:9001
Content-Type: application/json
Accept: */*
Content-Length: 289
{
"id": "text",
"api_key": "text",
"usage_billable": true,
"start": 1,
"source": "text",
"source_info": "text",
"stream_pipeline_context_id": "text",
"disable_model_monitoring": false,
"perception_encoder_version_id": "PE-Core-L14-336",
"model_id": "text",
"text": "The quick brown fox jumps over the lazy dog"
}{
"inference_id": "text",
"frame_id": 1,
"time": 1,
"embeddings": "[[0.12, 0.23, 0.34, ..., 0.43]]"
}セグメンテーションと検出
Roboflow API Key that will be passed to the model during initialization for artifact retrieval
Request for Grounding DINO zero-shot predictions.
Attributes: text (List[str]): A list of strings.
Roboflow API Key that will be passed to the model during initialization for artifact retrieval
trueInternal stream-pipeline frame pairing id. Not part of the public API.
If true, disables model monitoring for this request
falseThe type of the model, usually referring to what task the model performs
object-detectionIf true, the auto orient preprocessing step is disabled for this call.
falseIf true, the auto contrast preprocessing step is disabled for this call.
falseIf true, the grayscale preprocessing step is disabled for this call.
falseIf true, the static crop preprocessing step is disabled for this call.
falseA list of strings
["person","dog","cat"]0.5default0.5falseSuccessful Response
Object Detection inference response.
Attributes: predictions (List[inference.core.entities.responses.inference.ObjectDetectionPrediction]): List of object detection predictions.
Base64 encoded string containing prediction visualization image data
Unique identifier of inference
The frame id of the image used in inference if the input was a video
The time in seconds it took to produce the predictions including image preprocessing
Validation Error
POST /grounding_dino/infer HTTP/1.1
Host: localhost:9001
Content-Type: application/json
Accept: */*
Content-Length: 561
{
"id": "text",
"api_key": "text",
"usage_billable": true,
"start": 1,
"source": "text",
"source_info": "text",
"stream_pipeline_context_id": "text",
"disable_model_monitoring": false,
"model_id": "text",
"model_type": "object-detection",
"image": [
{
"type": "url",
"value": "http://www.example-image-url.com"
}
],
"disable_preproc_auto_orient": false,
"disable_preproc_contrast": false,
"disable_preproc_grayscale": false,
"disable_preproc_static_crop": false,
"text": [
"person",
"dog",
"cat"
],
"box_threshold": 0.5,
"grounding_dino_version_id": "default",
"text_threshold": 0.5,
"class_agnostic_nms": false
}{
"visualization": "text",
"inference_id": "text",
"frame_id": 1,
"time": 1,
"image": [
{
"width": 1,
"height": 1
}
],
"predictions": [
{
"x": 1,
"y": 1,
"width": 1,
"height": 1,
"confidence": 1,
"class": "text",
"class_confidence": 1,
"class_id": 1,
"tracker_id": 1,
"detection_id": "text",
"parent_id": "text"
}
]
}Roboflow API Key that will be passed to the model during initialization for artifact retrieval
Request for OwlV2 inference.
Attributes: api_key (Optional[str]): Roboflow API Key. owlv2_version_id (Optional[str]): The version ID of OwlV2 to be used for this request. image (Union[List[InferenceRequestImage], InferenceRequestImage]): Image(s) for inference. training_data (List[TrainingImage]): Training data to ground the model on confidence (float): Confidence threshold to filter predictions by
Roboflow API Key that will be passed to the model during initialization for artifact retrieval
trueInternal stream-pipeline frame pairing id. Not part of the public API.
If true, disables model monitoring for this request
falseThe version ID of owlv2 to be used for this request.
owlv2-large-patch14-ensembleExample: owlv2-base-patch16-ensembleModel id to be used in the request.
Images to run the model on
Default confidence threshold for owlvit predictions. Needs to be much higher than you're used to, probably 0.99 - 0.9999
0.99Example: 0.99If true, the predictions will be drawn on the original image and returned as a base64 string
falseIf true, labels will be rendered on prediction visualizations
falseThe stroke width used when visualizing predictions
1Example: 1Successful Response
Object Detection inference response.
Attributes: predictions (List[inference.core.entities.responses.inference.ObjectDetectionPrediction]): List of object detection predictions.
Base64 encoded string containing prediction visualization image data
Unique identifier of inference
The frame id of the image used in inference if the input was a video
The time in seconds it took to produce the predictions including image preprocessing
Validation Error
POST /owlv2/infer HTTP/1.1
Host: localhost:9001
Content-Type: application/json
Accept: */*
Content-Length: 563
{
"id": "text",
"api_key": "text",
"usage_billable": true,
"start": 1,
"source": "text",
"source_info": "text",
"stream_pipeline_context_id": "text",
"disable_model_monitoring": false,
"owlv2_version_id": "owlv2-base-patch16-ensemble",
"model_id": "text",
"image": [
{
"type": "url",
"value": "http://www.example-image-url.com"
}
],
"training_data": [
{
"boxes": [
{
"x": 1,
"y": 1,
"w": 1,
"h": 1,
"cls": "text",
"negative": false
}
],
"image": {
"type": "url",
"value": "http://www.example-image-url.com"
}
}
],
"confidence": 0.99,
"visualize_predictions": false,
"visualization_labels": false,
"visualization_stroke_width": 1
}{
"visualization": "text",
"inference_id": "text",
"frame_id": 1,
"time": 1,
"image": [
{
"width": 1,
"height": 1
}
],
"predictions": [
{
"x": 1,
"y": 1,
"width": 1,
"height": 1,
"confidence": 1,
"class": "text",
"class_confidence": 1,
"class_id": 1,
"tracker_id": 1,
"detection_id": "text",
"parent_id": "text"
}
]
}Roboflow API Key that will be passed to the model during initialization for artifact retrieval
Request for Grounding DINO zero-shot predictions.
Attributes: text (List[str]): A list of strings.
Roboflow API Key that will be passed to the model during initialization for artifact retrieval
trueInternal stream-pipeline frame pairing id. Not part of the public API.
If true, disables model monitoring for this request
falseThe type of the model, usually referring to what task the model performs
object-detectionIf true, the auto orient preprocessing step is disabled for this call.
falseIf true, the auto contrast preprocessing step is disabled for this call.
falseIf true, the grayscale preprocessing step is disabled for this call.
falseIf true, the static crop preprocessing step is disabled for this call.
falseA list of strings
["person","dog","cat"]l0.4Successful Response
Object Detection inference response.
Attributes: predictions (List[inference.core.entities.responses.inference.ObjectDetectionPrediction]): List of object detection predictions.
Base64 encoded string containing prediction visualization image data
Unique identifier of inference
The frame id of the image used in inference if the input was a video
The time in seconds it took to produce the predictions including image preprocessing
Validation Error
POST /yolo_world/infer HTTP/1.1
Host: localhost:9001
Content-Type: application/json
Accept: */*
Content-Length: 500
{
"id": "text",
"api_key": "text",
"usage_billable": true,
"start": 1,
"source": "text",
"source_info": "text",
"stream_pipeline_context_id": "text",
"disable_model_monitoring": false,
"model_id": "text",
"model_type": "object-detection",
"image": [
{
"type": "url",
"value": "http://www.example-image-url.com"
}
],
"disable_preproc_auto_orient": false,
"disable_preproc_contrast": false,
"disable_preproc_grayscale": false,
"disable_preproc_static_crop": false,
"text": [
"person",
"dog",
"cat"
],
"yolo_world_version_id": "l",
"confidence": 0.4
}{
"visualization": "text",
"inference_id": "text",
"frame_id": 1,
"time": 1,
"image": [
{
"width": 1,
"height": 1
}
],
"predictions": [
{
"x": 1,
"y": 1,
"width": 1,
"height": 1,
"confidence": 1,
"class": "text",
"class_confidence": 1,
"class_id": 1,
"tracker_id": 1,
"detection_id": "text",
"parent_id": "text"
}
]
}Roboflow API Key that will be passed to the model during initialization for artifact retrieval
SAM embedding request.
Attributes: image (Optional[inference.core.entities.requests.inference.InferenceRequestImage]): The image to be embedded. image_id (Optional[str]): The ID of the image to be embedded used to cache the embedding. format (Optional[str]): The format of the response. Must be one of json or binary.
Roboflow API Key that will be passed to the model during initialization for artifact retrieval
trueInternal stream-pipeline frame pairing id. Not part of the public API.
If true, disables model monitoring for this request
falseThe version ID of SAM to be used for this request. Must be one of vit_h, vit_l, or vit_b.
vit_hExample: vit_hThe ID of the image to be embedded used to cache the embedding.
image_idThe format of the response. Must be one of json or binary. If binary, embedding is returned as a binary numpy array.
jsonExample: jsonSuccessful Response
SAM embedding response.
Attributes: embeddings (Union[List[List[List[List[float]]]], Any]): The SAM embedding. time (float): The time in seconds it took to produce the embeddings including preprocessing.
If request format is json, embeddings is a series of nested lists representing the SAM embedding. If request format is binary, embeddings is a binary numpy array. The dimensions of the embedding are 1 x 256 x 64 x 64.
[[[[0.1, 0.2, 0.3, ...] ...] ...]]The time in seconds it took to produce the embeddings including preprocessing
Validation Error
POST /sam/embed_image HTTP/1.1
Host: localhost:9001
Content-Type: application/json
Accept: */*
Content-Length: 315
{
"id": "text",
"api_key": "text",
"usage_billable": true,
"start": 1,
"source": "text",
"source_info": "text",
"stream_pipeline_context_id": "text",
"disable_model_monitoring": false,
"sam_version_id": "vit_h",
"model_id": "text",
"image": {
"type": "url",
"value": "http://www.example-image-url.com"
},
"image_id": "image_id",
"format": "json"
}{
"embeddings": "[[[[0.1, 0.2, 0.3, ...] ...] ...]]",
"time": 1
}Roboflow API Key that will be passed to the model during initialization for artifact retrieval
SAM segmentation request.
Attributes: embeddings (Optional[Union[List[List[List[List[float]]]], Any]]): The embeddings to be decoded. embeddings_format (Optional[str]): The format of the embeddings. format (Optional[str]): The format of the response. image (Optional[InferenceRequestImage]): The image to be segmented. image_id (Optional[str]): The ID of the image to be segmented used to retrieve cached embeddings. has_mask_input (Optional[bool]): Whether or not the request includes a mask input. mask_input (Optional[Union[List[List[List[float]]], Any]]): The set of output masks. mask_input_format (Optional[str]): The format of the mask input. orig_im_size (Optional[List[int]]): The original size of the image used to generate the embeddings. point_coords (Optional[List[List[float]]]): The coordinates of the interactive points used during decoding. point_labels (Optional[List[float]]): The labels of the interactive points used during decoding. use_mask_input_cache (Optional[bool]): Whether or not to use the mask input cache.
Roboflow API Key that will be passed to the model during initialization for artifact retrieval
trueInternal stream-pipeline frame pairing id. Not part of the public API.
If true, disables model monitoring for this request
falseThe version ID of SAM to be used for this request. Must be one of vit_h, vit_l, or vit_b.
vit_hExample: vit_hThe embeddings to be decoded. The dimensions of the embeddings are 1 x 256 x 64 x 64. If embeddings is not provided, image must be provided.
[[[[0.1, 0.2, 0.3, ...] ...] ...]]The format of the embeddings. Must be one of json or binary. If binary, embeddings are expected to be a binary numpy array.
jsonExample: jsonThe format of the response. Must be one of json or binary. If binary, masks are returned as binary numpy arrays. If json, masks are converted to polygons, then returned as json.
jsonExample: jsonThe ID of the image to be segmented used to retrieve cached embeddings. If an embedding is cached, it will be used instead of generating a new embedding. If no embedding is cached, a new embedding will be generated and cached.
image_idWhether or not the request includes a mask input. If true, the mask input must be provided.
falseExample: trueThe set of output masks. If request format is json, masks is a list of polygons, where each polygon is a list of points, where each point is a tuple containing the x,y pixel coordinates of the point. If request format is binary, masks is a list of binary numpy arrays. The dimensions of each mask are 256 x 256. This is the same as the output, low resolution mask from the previous inference.
The format of the mask input. Must be one of json or binary. If binary, mask input is expected to be a binary numpy array.
jsonExample: jsonThe original size of the image used to generate the embeddings. This is only required if the image is not provided.
[640,320]The labels of the interactive points used during decoding. A 1 represents a positive point (part of the object to be segmented). A -1 represents a negative point (not part of the object to be segmented). Each label corresponds to a point in point_coords.
[-1]Example: [1]Whether or not to use the mask input cache. If true, the mask input cache will be used if it exists. If false, the mask input cache will not be used.
trueExample: trueSuccessful Response
SAM segmentation response.
Attributes: masks (Union[List[List[List[int]]], Any]): The set of output masks. low_res_masks (Union[List[List[List[int]]], Any]): The set of output low-resolution masks. time (float): The time in seconds it took to produce the segmentation including preprocessing.
The set of output masks. If request format is json, masks is a list of polygons, where each polygon is a list of points, where each point is a tuple containing the x,y pixel coordinates of the point. If request format is binary, masks is a list of binary numpy arrays. The dimensions of each mask are the same as the dimensions of the input image.
The set of output masks. If request format is json, masks is a list of polygons, where each polygon is a list of points, where each point is a tuple containing the x,y pixel coordinates of the point. If request format is binary, masks is a list of binary numpy arrays. The dimensions of each mask are 256 x 256
The time in seconds it took to produce the segmentation including preprocessing
Validation Error
POST /sam/segment_image HTTP/1.1
Host: localhost:9001
Content-Type: application/json
Accept: */*
Content-Length: 559
{
"id": "text",
"api_key": "text",
"usage_billable": true,
"start": 1,
"source": "text",
"source_info": "text",
"stream_pipeline_context_id": "text",
"disable_model_monitoring": false,
"sam_version_id": "vit_h",
"model_id": "text",
"embeddings": "[[[[0.1, 0.2, 0.3, ...] ...] ...]]",
"embeddings_format": "json",
"format": "json",
"image": {
"type": "url",
"value": "http://www.example-image-url.com"
},
"image_id": "image_id",
"has_mask_input": true,
"mask_input": [
[
[
1
]
]
],
"mask_input_format": "json",
"orig_im_size": [
640,
320
],
"point_coords": [
[
10,
10
]
],
"point_labels": [
1
],
"use_mask_input_cache": true
}{
"masks": [
[
[
1
]
]
],
"low_res_masks": [
[
[
1
]
]
],
"time": 1
}Roboflow API Key that will be passed to the model during initialization for artifact retrieval
SAM embedding request.
Attributes: image (Optional[inference.core.entities.requests.inference.InferenceRequestImage]): The image to be embedded. image_id (Optional[str]): The ID of the image to be embedded used to cache the embedding. format (Optional[str]): The format of the response. Must be one of json or binary.
Roboflow API Key that will be passed to the model during initialization for artifact retrieval
trueInternal stream-pipeline frame pairing id. Not part of the public API.
If true, disables model monitoring for this request
falseThe version ID of SAM to be used for this request. Must be one of hiera_tiny, hiera_small, hiera_large, hiera_b_plus
hiera_largeExample: hiera_largeThe ID of the image to be embedded used to cache the embedding.
image_idSuccessful Response
SAM embedding response.
Attributes: embeddings (Union[List[List[List[List[float]]]], Any]): The SAM embedding. time (float): The time in seconds it took to produce the embeddings including preprocessing.
Image id embeddings are cached to
The time in seconds it took to produce the embeddings including preprocessing
Validation Error
POST /sam2/embed_image HTTP/1.1
Host: localhost:9001
Content-Type: application/json
Accept: */*
Content-Length: 306
{
"id": "text",
"api_key": "text",
"usage_billable": true,
"start": 1,
"source": "text",
"source_info": "text",
"stream_pipeline_context_id": "text",
"disable_model_monitoring": false,
"sam2_version_id": "hiera_large",
"model_id": "text",
"image": {
"type": "url",
"value": "http://www.example-image-url.com"
},
"image_id": "image_id"
}{
"image_id": "text",
"time": 1
}Roboflow API Key that will be passed to the model during initialization for artifact retrieval
SAM segmentation request.
Attributes: format (Optional[str]): The format of the response. image (InferenceRequestImage): The image to be segmented. image_id (Optional[str]): The ID of the image to be segmented used to retrieve cached embeddings. point_coords (Optional[List[List[float]]]): The coordinates of the interactive points used during decoding. point_labels (Optional[List[float]]): The labels of the interactive points used during decoding.
Roboflow API Key that will be passed to the model during initialization for artifact retrieval
trueInternal stream-pipeline frame pairing id. Not part of the public API.
If true, disables model monitoring for this request
falseThe version ID of SAM to be used for this request. Must be one of hiera_tiny, hiera_small, hiera_large, hiera_b_plus
hiera_largeExample: hiera_largeThe format of the response. Must be one of 'json', 'rle', or 'binary'. If binary, masks are returned as binary numpy arrays. If json, masks are converted to polygons. If rle, masks are converted to RLE format.
jsonExample: jsonThe ID of the image to be segmented used to retrieve cached embeddings. If an embedding is cached, it will be used instead of generating a new embedding. If no embedding is cached, a new embedding will be generated and cached.
image_idIf true, the model will return three masks. For ambiguous input prompts (such as a single click), this will often produce better masks than a single prediction. If only a single mask is needed, the model's predicted quality score can be used to select the best mask. For non-ambiguous prompts, such as multiple input prompts, multimask_output=False can give better results.
trueExample: trueIf True, saves the low-resolution logits to the cache for potential future use. This can speed up subsequent requests with similar prompts on the same image. This feature is ignored if DISABLE_SAM2_LOGITS_CACHE env variable is set True
falseIf True, attempts to load previously cached low-resolution logits for the given image and prompt set. This can significantly speed up inference when making multiple similar requests on the same image. This feature is ignored if DISABLE_SAM2_LOGITS_CACHE env variable is set True
falseSuccessful Response
The time in seconds it took to produce the segmentation including preprocessing
Validation Error
POST /sam2/segment_image HTTP/1.1
Host: localhost:9001
Content-Type: application/json
Accept: */*
Content-Length: 479
{
"id": "text",
"api_key": "text",
"usage_billable": true,
"start": 1,
"source": "text",
"source_info": "text",
"stream_pipeline_context_id": "text",
"disable_model_monitoring": false,
"sam2_version_id": "hiera_large",
"model_id": "text",
"format": "json",
"image": {
"type": "url",
"value": "http://www.example-image-url.com"
},
"image_id": "image_id",
"prompts": [
{
"prompts": [
{
"points": [
{
"positive": true,
"x": 100,
"y": 100
}
]
}
]
}
],
"multimask_output": true,
"save_logits_to_cache": false,
"load_logits_from_cache": false
}{
"predictions": [
{
"masks": [
[
[
1
]
]
],
"confidence": 1,
"format": "polygon"
}
],
"time": 1
}Roboflow API Key that will be passed to the model during initialization for artifact retrieval
SAM embedding request.
Attributes: image (Optional[inference.core.entities.requests.inference.InferenceRequestImage]): The image to be embedded. image_id (Optional[str]): The ID of the image to be embedded used to cache the embedding. format (Optional[str]): The format of the response. Must be one of json or binary.
Roboflow API Key that will be passed to the model during initialization for artifact retrieval
trueInternal stream-pipeline frame pairing id. Not part of the public API.
If true, disables model monitoring for this request
falseThe version ID of SAM to be used for this request. Must be one of hiera_tiny, hiera_small, hiera_large, hiera_b_plus
hiera_largeExample: hiera_largeThe ID of the image to be embedded used to cache the embedding.
image_idSuccessful Response
Image id embeddings are cached to
The time in seconds it took to produce the embeddings including preprocessing
Validation Error
POST /sam3/embed_image HTTP/1.1
Host: localhost:9001
Content-Type: application/json
Accept: */*
Content-Length: 306
{
"id": "text",
"api_key": "text",
"usage_billable": true,
"start": 1,
"source": "text",
"source_info": "text",
"stream_pipeline_context_id": "text",
"disable_model_monitoring": false,
"sam2_version_id": "hiera_large",
"model_id": "text",
"image": {
"type": "url",
"value": "http://www.example-image-url.com"
},
"image_id": "image_id"
}{
"image_id": "text",
"time": 1
}Roboflow API Key that will be passed to the model during initialization for artifact retrieval
The source of the inference request
The detailed source information of the inference request
Roboflow API Key that will be passed to the model during initialization for artifact retrieval
trueInternal stream-pipeline frame pairing id. Not part of the public API.
If true, disables model monitoring for this request
falseThe model ID of SAM3. Use 'sam3/sam3_final' to target the generic base model.
sam3/sam3_finalOne of 'polygon', 'rle'
polygonOptional ID for caching embeddings.
Score threshold for outputs.
0.5IoU threshold for cross-prompt NMS. If None, NMS is disabled. Must be in [0.0, 1.0] when set.
Successful Response
The time in seconds it took to produce the segmentation including preprocessing
Validation Error
POST /sam3/concept_segment HTTP/1.1
Host: localhost:9001
Content-Type: application/json
Accept: */*
Content-Length: 474
{
"id": "text",
"api_key": "text",
"usage_billable": true,
"start": 1,
"source": "text",
"source_info": "text",
"stream_pipeline_context_id": "text",
"disable_model_monitoring": false,
"model_id": "sam3/sam3_final",
"format": "polygon",
"image": {
"type": "url",
"value": "http://www.example-image-url.com"
},
"image_id": "text",
"output_prob_thresh": 0.5,
"prompts": [
{
"type": "text",
"text": "text",
"output_prob_thresh": 1,
"boxes": [
{
"x": 1,
"y": 1,
"width": 1,
"height": 1
}
],
"box_labels": [
1
]
}
],
"nms_iou_threshold": 1
}{
"prompt_results": [
{
"prompt_index": 1,
"echo": {
"prompt_index": 1,
"type": "text",
"text": "text",
"num_boxes": 1
},
"predictions": [
{
"masks": [
[
[
1
]
]
],
"confidence": 1,
"format": "polygon"
}
]
}
],
"time": 1
}Roboflow API Key that will be passed to the model during initialization for artifact retrieval
The source of the inference request
The detailed source information of the inference request
SAM segmentation request.
Attributes: format (Optional[str]): The format of the response. image (InferenceRequestImage): The image to be segmented. image_id (Optional[str]): The ID of the image to be segmented used to retrieve cached embeddings. point_coords (Optional[List[List[float]]]): The coordinates of the interactive points used during decoding. point_labels (Optional[List[float]]): The labels of the interactive points used during decoding.
Roboflow API Key that will be passed to the model during initialization for artifact retrieval
trueInternal stream-pipeline frame pairing id. Not part of the public API.
If true, disables model monitoring for this request
falseThe version ID of SAM to be used for this request. Must be one of hiera_tiny, hiera_small, hiera_large, hiera_b_plus
hiera_largeExample: hiera_largeThe format of the response. Must be one of 'json', 'rle', or 'binary'. If binary, masks are returned as binary numpy arrays. If json, masks are converted to polygons. If rle, masks are converted to RLE format.
jsonExample: jsonThe ID of the image to be segmented used to retrieve cached embeddings. If an embedding is cached, it will be used instead of generating a new embedding. If no embedding is cached, a new embedding will be generated and cached.
image_idIf true, the model will return three masks. For ambiguous input prompts (such as a single click), this will often produce better masks than a single prediction. If only a single mask is needed, the model's predicted quality score can be used to select the best mask. For non-ambiguous prompts, such as multiple input prompts, multimask_output=False can give better results.
trueExample: trueIf True, saves the low-resolution logits to the cache for potential future use. This can speed up subsequent requests with similar prompts on the same image. This feature is ignored if DISABLE_SAM2_LOGITS_CACHE env variable is set True
falseIf True, attempts to load previously cached low-resolution logits for the given image and prompt set. This can significantly speed up inference when making multiple similar requests on the same image. This feature is ignored if DISABLE_SAM2_LOGITS_CACHE env variable is set True
falseSuccessful Response
The time in seconds it took to produce the segmentation including preprocessing
Validation Error
POST /sam3/visual_segment HTTP/1.1
Host: localhost:9001
Content-Type: application/json
Accept: */*
Content-Length: 479
{
"id": "text",
"api_key": "text",
"usage_billable": true,
"start": 1,
"source": "text",
"source_info": "text",
"stream_pipeline_context_id": "text",
"disable_model_monitoring": false,
"sam2_version_id": "hiera_large",
"model_id": "text",
"format": "json",
"image": {
"type": "url",
"value": "http://www.example-image-url.com"
},
"image_id": "image_id",
"prompts": [
{
"prompts": [
{
"points": [
{
"positive": true,
"x": 100,
"y": 100
}
]
}
]
}
],
"multimask_output": true,
"save_logits_to_cache": false,
"load_logits_from_cache": false
}{
"predictions": [
{
"masks": [
[
[
1
]
]
],
"confidence": 1,
"format": "polygon"
}
],
"time": 1
}Roboflow API Key that will be passed to the model during initialization for artifact retrieval
SAM3D inference request for 3D object generation.
Attributes: api_key (Optional[str]): Roboflow API Key. image (InferenceRequestImage): The input image to be used for 3D generation. mask_input: Mask(s) in any supported format - polygon, binary mask, or RLE.
Roboflow API Key that will be passed to the model during initialization for artifact retrieval
trueInternal stream-pipeline frame pairing id. Not part of the public API.
If true, disables model monitoring for this request
falseMask input in any supported format: polygon [x1,y1,x2,y2,...], binary mask (base64), RLE dict, or list of these.
The model ID for SAM3_3D.
sam3-3d-objectsSAM3 3D always outputs object gaussians, and can optionally output object meshes if output_meshes is True.
trueOutput the combined scene reconstruction in addition to individual object reconstructions.
trueEnable mesh postprocessing.
trueEnable texture baking for meshes.
trueUse the distilled versions of the model components.
falseSuccessful Response
Validation Error
POST /sam3_3d/infer HTTP/1.1
Host: localhost:9001
Content-Type: application/json
Accept: */*
Content-Length: 404
{
"id": "text",
"api_key": "text",
"usage_billable": true,
"start": 1,
"source": "text",
"source_info": "text",
"stream_pipeline_context_id": "text",
"disable_model_monitoring": false,
"image": {
"type": "url",
"value": "http://www.example-image-url.com"
},
"mask_input": null,
"model_id": "sam3-3d-objects",
"output_meshes": true,
"output_scene": true,
"with_mesh_postprocess": true,
"with_texture_baking": true,
"use_distillations": false
}コンテンツなし
OCR
Roboflow API Key that will be passed to the model during initialization for artifact retrieval
DocTR inference request.
Attributes: api_key (Optional[str]): Roboflow API Key.
Roboflow API Key that will be passed to the model during initialization for artifact retrieval
trueInternal stream-pipeline frame pairing id. Not part of the public API.
If true, disables model monitoring for this request
falsedefaultfalseSuccessful Response
Validation Error
POST /doctr/ocr HTTP/1.1
Host: localhost:9001
Content-Type: application/json
Accept: */*
Content-Length: 315
{
"id": "text",
"api_key": "text",
"usage_billable": true,
"start": 1,
"source": "text",
"source_info": "text",
"stream_pipeline_context_id": "text",
"disable_model_monitoring": false,
"image": [
{
"type": "url",
"value": "http://www.example-image-url.com"
}
],
"doctr_version_id": "default",
"model_id": "text",
"generate_bounding_boxes": false
}{
"result": "text",
"image": {
"width": 1,
"height": 1
},
"predictions": [
{
"x": 1,
"y": 1,
"width": 1,
"height": 1,
"confidence": 1,
"class": "text",
"class_confidence": 1,
"class_id": 1,
"tracker_id": 1,
"detection_id": "text",
"parent_id": "text"
}
],
"time": 1,
"parent_id": "text"
}Roboflow API Key that will be passed to the model during initialization for artifact retrieval
EasyOCR inference request.
Attributes: api_key (Optional[str]): Roboflow API Key.
Roboflow API Key that will be passed to the model during initialization for artifact retrieval
trueInternal stream-pipeline frame pairing id. Not part of the public API.
If true, disables model monitoring for this request
falseenglish_g2["en"]Quantized models are smaller and faster, but may be less accurate and won't work correctly on all hardware.
falseSuccessful Response
Validation Error
POST /easy_ocr/ocr HTTP/1.1
Host: localhost:9001
Content-Type: application/json
Accept: */*
Content-Length: 332
{
"id": "text",
"api_key": "text",
"usage_billable": true,
"start": 1,
"source": "text",
"source_info": "text",
"stream_pipeline_context_id": "text",
"disable_model_monitoring": false,
"image": [
{
"type": "url",
"value": "http://www.example-image-url.com"
}
],
"easy_ocr_version_id": "english_g2",
"model_id": "text",
"language_codes": [
"text"
],
"quantize": false
}{
"result": "text",
"image": {
"width": 1,
"height": 1
},
"predictions": [
{
"x": 1,
"y": 1,
"width": 1,
"height": 1,
"confidence": 1,
"class": "text",
"class_confidence": 1,
"class_id": 1,
"tracker_id": 1,
"detection_id": "text",
"parent_id": "text"
}
],
"time": 1,
"parent_id": "text"
}Roboflow API Key that will be passed to the model during initialization for artifact retrieval
PP-OCR inference request.
Attributes: api_key (Optional[str]): Roboflow API Key.
Roboflow API Key that will be passed to the model during initialization for artifact retrieval
trueInternal stream-pipeline frame pairing id. Not part of the public API.
If true, disables model monitoring for this request
false__unset____unset__Successful Response
OCR Inference response.
Attributes: result (str): The combined OCR recognition result. predictions (List[ObjectDetectionPrediction]): List of objects detected by OCR time (float): The time in seconds it took to produce the inference including preprocessing
The combined OCR recognition result.
The time in seconds it took to produce the inference including preprocessing.
Identifier of parent image region. Useful when stack of detection-models is in use to refer the RoI being the input to inference
Validation Error
POST /ocr/pp-ocr HTTP/1.1
Host: localhost:9001
Content-Type: application/json
Accept: */*
Content-Length: 341
{
"id": "text",
"api_key": "text",
"usage_billable": true,
"start": 1,
"source": "text",
"source_info": "text",
"stream_pipeline_context_id": "text",
"disable_model_monitoring": false,
"image": [
{
"type": "url",
"value": "http://www.example-image-url.com"
}
],
"text_detection": "__unset__",
"text_recognition": "__unset__",
"pp_ocr_version_id": "text",
"model_id": "text"
}{
"result": "text",
"image": {
"width": 1,
"height": 1
},
"predictions": [
{
"x": 1,
"y": 1,
"width": 1,
"height": 1,
"confidence": 1,
"class": "text",
"class_confidence": 1,
"class_id": 1,
"tracker_id": 1,
"detection_id": "text",
"parent_id": "text"
}
],
"time": 1,
"parent_id": "text"
}Roboflow API Key that will be passed to the model during initialization for artifact retrieval
TrOCR inference request.
Attributes: api_key (Optional[str]): Roboflow API Key.
Roboflow API Key that will be passed to the model during initialization for artifact retrieval
trueInternal stream-pipeline frame pairing id. Not part of the public API.
If true, disables model monitoring for this request
falsetrocr-base-printedSuccessful Response
OCR Inference response.
Attributes: result (str): The combined OCR recognition result. predictions (List[ObjectDetectionPrediction]): List of objects detected by OCR time (float): The time in seconds it took to produce the inference including preprocessing
The combined OCR recognition result.
The time in seconds it took to produce the inference including preprocessing.
Identifier of parent image region. Useful when stack of detection-models is in use to refer the RoI being the input to inference
Validation Error
POST /ocr/trocr HTTP/1.1
Host: localhost:9001
Content-Type: application/json
Accept: */*
Content-Length: 294
{
"id": "text",
"api_key": "text",
"usage_billable": true,
"start": 1,
"source": "text",
"source_info": "text",
"stream_pipeline_context_id": "text",
"disable_model_monitoring": false,
"image": [
{
"type": "url",
"value": "http://www.example-image-url.com"
}
],
"trocr_version_id": "trocr-base-printed",
"model_id": "text"
}{
"result": "text",
"image": {
"width": 1,
"height": 1
},
"predictions": [
{
"x": 1,
"y": 1,
"width": 1,
"height": 1,
"confidence": 1,
"class": "text",
"class_confidence": 1,
"class_id": 1,
"tracker_id": 1,
"detection_id": "text",
"parent_id": "text"
}
],
"time": 1,
"parent_id": "text"
}視線検出
WebRTC
[EXPERIMENTAL] Establishes WebRTC peer connection and processes video stream in spawned process or modal function
falsetrue3600webrtc-gpu-smallSuccessful Response
Operation status
Validation Error
POST /initialise_webrtc_worker HTTP/1.1
Host: localhost:9001
Content-Type: application/json
Accept: */*
Content-Length: 997
{
"api_key": "text",
"workflow_configuration": {
"type": "text",
"workflow_specification": {
"ANY_ADDITIONAL_PROPERTY": "anything"
},
"workspace_name": "text",
"workflow_id": "text",
"workflow_version_id": "text",
"image_input_name": "image",
"workflows_parameters": {
"ANY_ADDITIONAL_PROPERTY": "anything"
},
"disable_sinks": false,
"workflows_thread_pool_workers": 4,
"cancel_thread_pool_tasks_on_exit": true,
"video_metadata_input_name": "video_metadata"
},
"is_preview": false,
"webrtc_offer": {
"type": "text",
"sdp": "text"
},
"webrtc_config": {
"iceServers": [
{
"urls": "text",
"username": "text",
"credential": "text"
}
]
},
"webrtc_turn_config": {
"urls": "text",
"username": "text",
"credential": "text"
},
"webrtc_realtime_processing": true,
"stream_output": [
"text"
],
"data_output": [
"text"
],
"declared_fps": 1,
"rtsp_url": "text",
"mjpeg_url": "text",
"processing_timeout": 3600,
"processing_session_started": "2026-01-01T00:00:00.000Z",
"requested_plan": "webrtc-gpu-small",
"requested_gpu": "text",
"requested_region": "text",
"workspace_id": "text",
"session_id": "text"
}{
"status": "text",
"context": {
"request_id": "text",
"pipeline_id": "text"
},
"sdp": "text",
"type": "text"
}Request body for WebRTC session heartbeat and end endpoints.
Successful Response
Validation Error
POST /webrtc/session/heartbeat HTTP/1.1
Host: localhost:9001
Content-Type: application/json
Accept: */*
Content-Length: 38
{
"session_id": "text",
"api_key": "text"
}{
"ANY_ADDITIONAL_PROPERTY": "anything"
}Request body for WebRTC session heartbeat and end endpoints.
Successful Response
Validation Error
POST /webrtc/session/heartbeat/end HTTP/1.1
Host: localhost:9001
Content-Type: application/json
Accept: */*
Content-Length: 38
{
"session_id": "text",
"api_key": "text"
}{
"ANY_ADDITIONAL_PROPERTY": "anything"
}最終更新
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