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DetectionsConsensus

Combine detections from multiple detection-based models based on a majority vote strategy.

This block is useful if you have multiple specialized models that you want to consult to determine whether a certain object is present in an image.

See the table below to explore the values you can use to configure the consensus block.

Properties

Name Type Description Refs
name str The unique name of this step..
required_votes int Required number of votes for single detection from different models to accept detection as output detection.
class_aware bool Flag to decide if merging detections is class-aware or only bounding boxes aware.
iou_threshold float IoU threshold to consider detections from different models as matching (increasing votes for region).
confidence float Confidence threshold for merged detections.
classes_to_consider List[str] Optional list of classes to consider in consensus procedure..
required_objects Optional[Dict[str, int], int] If given, it holds the number of objects that must be present in merged results, to assume that object presence is reached. Can be selector to InferenceParameter, integer value or dictionary with mapping of class name into minimal number of merged detections of given class to assume consensus..
presence_confidence_aggregation AggregationMode Mode dictating aggregation of confidence scores and classes both in case of object presence deduction procedure..
detections_merge_confidence_aggregation AggregationMode Mode dictating aggregation of confidence scores and classes both in case of boxes consensus procedure. One of average, max, min. Default: average. While using for merging overlapping boxes, against classes - average equals to majority vote, max - for the class of detection with max confidence, min - for the class of detection with min confidence..
detections_merge_coordinates_aggregation AggregationMode Mode dictating aggregation of bounding boxes. One of average, max, min. Default: average. average means taking mean from all boxes coordinates, min - taking smallest box, max - taking largest box..

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

Available Connections

Check what blocks you can connect to DetectionsConsensus.

The available connections depend on its binding kinds. Check what binding kinds DetectionsConsensus has.

Bindings
  • input

    • predictions_batches (Union[Batch[instance_segmentation_prediction], Batch[keypoint_detection_prediction], Batch[object_detection_prediction]]): Reference to detection-like model predictions made against single image to agree on model consensus.
    • required_votes (integer): Required number of votes for single detection from different models to accept detection as output detection.
    • class_aware (boolean): Flag to decide if merging detections is class-aware or only bounding boxes aware.
    • iou_threshold (float_zero_to_one): IoU threshold to consider detections from different models as matching (increasing votes for region).
    • confidence (float_zero_to_one): Confidence threshold for merged detections.
    • classes_to_consider (list_of_values): Optional list of classes to consider in consensus procedure..
    • required_objects (Union[dictionary, integer]): If given, it holds the number of objects that must be present in merged results, to assume that object presence is reached. Can be selector to InferenceParameter, integer value or dictionary with mapping of class name into minimal number of merged detections of given class to assume consensus..
  • output

Example JSON definition of DetectionsConsensus step
{
    "name": "<your_step_name_here>",
    "type": "DetectionsConsensus",
    "predictions_batches": [
        "$steps.a.predictions",
        "$steps.b.predictions"
    ],
    "required_votes": 2,
    "class_aware": true,
    "iou_threshold": 0.3,
    "confidence": 0.1,
    "classes_to_consider": [
        "a",
        "b"
    ],
    "required_objects": 3,
    "presence_confidence_aggregation": "max",
    "detections_merge_confidence_aggregation": "min",
    "detections_merge_coordinates_aggregation": "min"
}