Keypoint Detection Model
Predict skeletons on objects.
v3
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@v3 to add the block as a step in your workflow.
Properties
Name
Type
Description
Refs
name
str
Enter a unique identifier for this step..
❌
model_id
str
Roboflow model identifier..
✅
confidence_mode
str
not available.
✅
custom_confidence
float
not available.
✅
keypoint_confidence
float
Confidence threshold to predict a keypoint as visible..
✅
class_filter
List[str]
List of accepted classes. Classes must exist in the model's training set..
✅
iou_threshold
float
Minimum overlap threshold between boxes to combine them into a single detection, used in NMS. Learn more..
✅
max_detections
int
Maximum number of detections to return..
✅
class_agnostic_nms
bool
Boolean flag to specify if NMS is to be used in class-agnostic mode..
✅
max_candidates
int
Maximum number of candidates as NMS input to be taken into account..
✅
disable_active_learning
bool
Boolean flag to disable project-level active learning for this block..
✅
active_learning_target_dataset
str
Target dataset for active learning, if enabled..
✅
The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.
Input and Output Bindings
The available connections depend on its binding kinds. Check what binding kinds Keypoint Detection Model in version v3 has.
Input and output bindings
input
images(image): The image to infer on..model_id(roboflow_model_id): Roboflow model identifier..confidence_mode(string): not available.custom_confidence(float_zero_to_one): not available.keypoint_confidence(float_zero_to_one): Confidence threshold to predict a keypoint as visible..class_filter(list_of_values): List of accepted classes. Classes must exist in the model's training set..iou_threshold(float_zero_to_one): Minimum overlap threshold between boxes to combine them into a single detection, used in NMS. Learn more..max_detections(integer): Maximum number of detections to return..class_agnostic_nms(boolean): Boolean flag to specify if NMS is to be used in class-agnostic mode..max_candidates(integer): Maximum number of candidates as NMS input to be taken into account..disable_active_learning(boolean): Boolean flag to disable project-level active learning for this block..active_learning_target_dataset(roboflow_project): Target dataset for active learning, if enabled..
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.
v2
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@v2 to add the block as a step in your workflow.
Properties
Name
Type
Description
Refs
name
str
Enter a unique identifier for this step..
❌
model_id
str
Roboflow model identifier..
✅
confidence
float
Confidence threshold for predictions..
✅
keypoint_confidence
float
Confidence threshold to predict a keypoint as visible..
✅
class_filter
List[str]
List of accepted classes. Classes must exist in the model's training set..
✅
iou_threshold
float
Minimum overlap threshold between boxes to combine them into a single detection, used in NMS. Learn more..
✅
max_detections
int
Maximum number of detections to return..
✅
class_agnostic_nms
bool
Boolean flag to specify if NMS is to be used in class-agnostic mode..
✅
max_candidates
int
Maximum number of candidates as NMS input to be taken into account..
✅
disable_active_learning
bool
Boolean flag to disable project-level active learning for this block..
✅
active_learning_target_dataset
str
Target dataset for active learning, if enabled..
✅
The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.
Input and Output Bindings
The available connections depend on its binding kinds. Check what binding kinds Keypoint Detection Model in version v2 has.
Input and output bindings
input
images(image): The image to infer on..model_id(roboflow_model_id): Roboflow model identifier..confidence(float_zero_to_one): Confidence threshold for predictions..keypoint_confidence(float_zero_to_one): Confidence threshold to predict a keypoint as visible..class_filter(list_of_values): List of accepted classes. Classes must exist in the model's training set..iou_threshold(float_zero_to_one): Minimum overlap threshold between boxes to combine them into a single detection, used in NMS. Learn more..max_detections(integer): Maximum number of detections to return..class_agnostic_nms(boolean): Boolean flag to specify if NMS is to be used in class-agnostic mode..max_candidates(integer): Maximum number of candidates as NMS input to be taken into account..disable_active_learning(boolean): Boolean flag to disable project-level active learning for this block..active_learning_target_dataset(roboflow_project): Target dataset for active learning, if enabled..
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.
v1
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@v1 to add the block as a step in your workflow.
Properties
Name
Type
Description
Refs
name
str
Enter a unique identifier for this step..
❌
model_id
str
Roboflow model identifier..
✅
confidence
float
Confidence threshold for predictions..
✅
keypoint_confidence
float
Confidence threshold to predict a keypoint as visible..
✅
class_filter
List[str]
List of accepted classes. Classes must exist in the model's training set..
✅
iou_threshold
float
Minimum overlap threshold between boxes to combine them into a single detection, used in NMS. Learn more..
✅
max_detections
int
Maximum number of detections to return..
✅
class_agnostic_nms
bool
Boolean flag to specify if NMS is to be used in class-agnostic mode..
✅
max_candidates
int
Maximum number of candidates as NMS input to be taken into account..
✅
disable_active_learning
bool
Boolean flag to disable project-level active learning for this block..
✅
active_learning_target_dataset
str
Target dataset for active learning, if enabled..
✅
The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.
Input and Output Bindings
The available connections depend on its binding kinds. Check what binding kinds Keypoint Detection Model in version v1 has.
Input and output bindings
input
images(image): The image to infer on..model_id(roboflow_model_id): Roboflow model identifier..confidence(float_zero_to_one): Confidence threshold for predictions..keypoint_confidence(float_zero_to_one): Confidence threshold to predict a keypoint as visible..class_filter(list_of_values): List of accepted classes. Classes must exist in the model's training set..iou_threshold(float_zero_to_one): Minimum overlap threshold between boxes to combine them into a single detection, used in NMS. Learn more..max_detections(integer): Maximum number of detections to return..class_agnostic_nms(boolean): Boolean flag to specify if NMS is to be used in class-agnostic mode..max_candidates(integer): Maximum number of candidates as NMS input to be taken into account..disable_active_learning(boolean): Boolean flag to disable project-level active learning for this block..active_learning_target_dataset(roboflow_project): Target dataset for active learning, if enabled..
output
inference_id(string): String value.predictions(keypoint_detection_prediction): Prediction with detected bounding boxes and detected keypoints in form of sv.Detections(...) object.
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