Detections List Roll-Up
Roll up multiple levels of dimensionality back to a single dimension.
Rolls up dimensionality from children to parent detections
Useful in scenarios like:
rolling up results from a secondary model run on crops back to parent images
rolling up OCR results for dynamically cropped images
Type identifier
Use the following identifier in step "type" field: roboflow_core/detections_list_rollup@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..
❌
confidence_strategy
str
Strategy to use when merging confidence scores from child detections. Options are 'max', 'mean', or 'min'..
✅
overlap_threshold
float
Minimum overlap ratio (IoU) to consider when merging overlapping detections from child crops. A value of 0.0 merges any overlapping detections, while higher values require greater overlap to merge. Specify between 0.0 and 1.0. A value of 1.0 only merges completely overlapping detections..
✅
keypoint_merge_threshold
float
Keypoint distance (in pixels) to merge keypoint detections if the child detections contain keypoint data..
✅
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 Detections List Roll-Up in version v1 has.
Input and output bindings
input
parent_detection(Union[instance_segmentation_prediction,keypoint_detection_prediction,object_detection_prediction]): The parent detection the dimensionality inherits from..child_detections(list_of_values): A list of child detections resulting from higher dimensionality, such as predictions made on dynamic crops. Use the "Dimension Collapse" to reduce the higher dimensionality result to one that can be used with this. Example: Prediction -> Dimension Collapse -> Detections List Roll-Up.confidence_strategy(list_of_values): Strategy to use when merging confidence scores from child detections. Options are 'max', 'mean', or 'min'..overlap_threshold(float_zero_to_one): Minimum overlap ratio (IoU) to consider when merging overlapping detections from child crops. A value of 0.0 merges any overlapping detections, while higher values require greater overlap to merge. Specify between 0.0 and 1.0. A value of 1.0 only merges completely overlapping detections..keypoint_merge_threshold(float): Keypoint distance (in pixels) to merge keypoint detections if the child detections contain keypoint data..
output
rolled_up_detections(Union[instance_segmentation_prediction,object_detection_prediction,keypoint_detection_prediction]): Prediction with detected bounding boxes and segmentation masks in form of sv.Detections(...) object ifinstance_segmentation_predictionor Prediction with detected bounding boxes in form of sv.Detections(...) object ifobject_detection_predictionor Prediction with detected bounding boxes and detected keypoints in form of sv.Detections(...) object ifkeypoint_detection_prediction.crop_zones(list_of_values): List of values of any type.
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