Keypoint Detection Model¶

v2¶

Class: RoboflowKeypointDetectionModelBlockV2 (there are multiple versions of this block)

Source: inference.core.workflows.core_steps.models.roboflow.keypoint_detection.v2.RoboflowKeypointDetectionModelBlockV2

Warning: This block has multiple versions. Please refer to the specific version for details. You can learn more about how versions work here: Versioning

Run inference on a keypoint detection model hosted on or uploaded to Roboflow.

You can query any model that is private to your account, or any public model available on Roboflow Universe.

You will need to set your Roboflow API key in your Inference environment to use this block. To learn more about setting your Roboflow API key, refer to the Inference documentation.

Type identifier¶

Use the following identifier in step "type" field: roboflow_core/roboflow_keypoint_detection_model@v2to add the block as as step in your workflow.

Properties¶

Name	Type	Description	Refs
`name`	`str`	Enter a unique identifier for this step..	❌
`model_id`	`str`	Roboflow model identifier.	✅
`class_agnostic_nms`	`bool`	Value to decide if NMS is to be used in class-agnostic mode..	✅
`class_filter`	`List[str]`	List of classes to retrieve from predictions (to define subset of those which was used while model training).	✅
`confidence`	`float`	Confidence threshold for predictions.	✅
`iou_threshold`	`float`	Parameter of NMS, to decide on minimum box intersection over union to merge boxes.	✅
`max_detections`	`int`	Maximum number of detections to return.	✅
`max_candidates`	`int`	Maximum number of candidates as NMS input to be taken into account..	✅
`keypoint_confidence`	`float`	Confidence threshold to predict keypoint as visible..	✅
`disable_active_learning`	`bool`	Parameter to decide if Active Learning data sampling is disabled for the model.	✅
`active_learning_target_dataset`	`str`	Target dataset for Active Learning data sampling - see Roboflow Active Learning docs for more information.	✅

The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.

Available Connections¶

Compatible Blocks

Check what blocks you can connect to Keypoint Detection Model in version v2.

Input and Output Bindings¶

The available connections depend on its binding kinds. Check what binding kinds Keypoint Detection Model in version v2 has.

Bindings

input
- images (image): The image to infer on.
- model_id (roboflow_model_id): Roboflow model identifier.
- class_agnostic_nms (boolean): Value to decide if NMS is to be used in class-agnostic mode..
- class_filter (list_of_values): List of classes to retrieve from predictions (to define subset of those which was used while model training).
- confidence (float_zero_to_one): Confidence threshold for predictions.
- iou_threshold (float_zero_to_one): Parameter of NMS, to decide on minimum box intersection over union to merge boxes.
- max_detections (integer): Maximum number of detections to return.
- max_candidates (integer): Maximum number of candidates as NMS input to be taken into account..
- keypoint_confidence (float_zero_to_one): Confidence threshold to predict keypoint as visible..
- disable_active_learning (boolean): Parameter to decide if Active Learning data sampling is disabled for the model.
- active_learning_target_dataset (roboflow_project): Target dataset for Active Learning data sampling - see Roboflow Active Learning docs for more information.
output
- inference_id (inference_id): Inference identifier.
- predictions (keypoint_detection_prediction): Prediction with detected bounding boxes and detected keypoints in form of sv.Detections(...) object.
- model_id (roboflow_model_id): Roboflow model id.

Example JSON definition of step Keypoint Detection Model in version v2

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/roboflow_keypoint_detection_model@v2",
    "images": "$inputs.image",
    "model_id": "my_project/3",
    "class_agnostic_nms": true,
    "class_filter": [
        "a",
        "b",
        "c"
    ],
    "confidence": 0.3,
    "iou_threshold": 0.4,
    "max_detections": 300,
    "max_candidates": 3000,
    "keypoint_confidence": 0.3,
    "disable_active_learning": true,
    "active_learning_target_dataset": "my_project"
}

v1¶

Class: RoboflowKeypointDetectionModelBlockV1 (there are multiple versions of this block)

Source: inference.core.workflows.core_steps.models.roboflow.keypoint_detection.v1.RoboflowKeypointDetectionModelBlockV1

Warning: This block has multiple versions. Please refer to the specific version for details. You can learn more about how versions work here: Versioning

Run inference on a keypoint detection model hosted on or uploaded to Roboflow.

You can query any model that is private to your account, or any public model available on Roboflow Universe.

You will need to set your Roboflow API key in your Inference environment to use this block. To learn more about setting your Roboflow API key, refer to the Inference documentation.

Type identifier¶

Use the following identifier in step "type" field: roboflow_core/roboflow_keypoint_detection_model@v1to add the block as as step in your workflow.

Properties¶

Name	Type	Description	Refs
`name`	`str`	Enter a unique identifier for this step..	❌
`model_id`	`str`	Roboflow model identifier.	✅
`class_agnostic_nms`	`bool`	Value to decide if NMS is to be used in class-agnostic mode..	✅
`class_filter`	`List[str]`	List of classes to retrieve from predictions (to define subset of those which was used while model training).	✅
`confidence`	`float`	Confidence threshold for predictions.	✅
`iou_threshold`	`float`	Parameter of NMS, to decide on minimum box intersection over union to merge boxes.	✅
`max_detections`	`int`	Maximum number of detections to return.	✅
`max_candidates`	`int`	Maximum number of candidates as NMS input to be taken into account..	✅
`keypoint_confidence`	`float`	Confidence threshold to predict keypoint as visible..	✅
`disable_active_learning`	`bool`	Parameter to decide if Active Learning data sampling is disabled for the model.	✅
`active_learning_target_dataset`	`str`	Target dataset for Active Learning data sampling - see Roboflow Active Learning docs for more information.	✅

The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.

Available Connections¶

Compatible Blocks

Check what blocks you can connect to Keypoint Detection Model in version v1.

Input and Output Bindings¶

The available connections depend on its binding kinds. Check what binding kinds Keypoint Detection Model in version v1 has.

Bindings

input
- images (image): The image to infer on.
- model_id (roboflow_model_id): Roboflow model identifier.
- class_agnostic_nms (boolean): Value to decide if NMS is to be used in class-agnostic mode..
- class_filter (list_of_values): List of classes to retrieve from predictions (to define subset of those which was used while model training).
- confidence (float_zero_to_one): Confidence threshold for predictions.
- iou_threshold (float_zero_to_one): Parameter of NMS, to decide on minimum box intersection over union to merge boxes.
- max_detections (integer): Maximum number of detections to return.
- max_candidates (integer): Maximum number of candidates as NMS input to be taken into account..
- keypoint_confidence (float_zero_to_one): Confidence threshold to predict keypoint as visible..
- disable_active_learning (boolean): Parameter to decide if Active Learning data sampling is disabled for the model.
- active_learning_target_dataset (roboflow_project): Target dataset for Active Learning data sampling - see Roboflow Active Learning docs for more information.
output
- inference_id (string): String value.
- predictions (keypoint_detection_prediction): Prediction with detected bounding boxes and detected keypoints in form of sv.Detections(...) object.

Example JSON definition of step Keypoint Detection Model in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/roboflow_keypoint_detection_model@v1",
    "images": "$inputs.image",
    "model_id": "my_project/3",
    "class_agnostic_nms": true,
    "class_filter": [
        "a",
        "b",
        "c"
    ],
    "confidence": 0.3,
    "iou_threshold": 0.4,
    "max_detections": 300,
    "max_candidates": 3000,
    "keypoint_confidence": 0.3,
    "disable_active_learning": true,
    "active_learning_target_dataset": "my_project"
}