Detections Consensus¶

Class: DetectionsConsensusBlockV1

Source: inference.core.workflows.core_steps.fusion.detections_consensus.v1.DetectionsConsensusBlockV1

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.

Type identifier¶

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

Properties¶

Name	Type	Description	Refs
`name`	`str`	Enter a unique identifier for 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. This mode is not used for masks aggregation..	❌
`detections_merge_mask_aggregation`	`MaskAggregationMode`	Mode dictating aggregation of masks. One of `union`, `intersection`, `max`, `min`. Default: `union`. `union` means taking union of all masks, `intersection` - taking intersection of all masks, `max` - taking largest mask, `min` - taking smallest mask..	❌

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 Detections Consensus in version v1.

Input and Output Bindings¶

The available connections depend on its binding kinds. Check what binding kinds Detections Consensus in version v1 has.

Bindings

input
- predictions_batches (Union[object_detection_prediction, keypoint_detection_prediction, instance_segmentation_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
- predictions (Union[object_detection_prediction, instance_segmentation_prediction]): Prediction with detected bounding boxes in form of sv.Detections(...) object if object_detection_prediction or Prediction with detected bounding boxes and segmentation masks in form of sv.Detections(...) object if instance_segmentation_prediction.
- object_present (Union[boolean, dictionary]): Boolean flag if boolean or Dictionary if dictionary.
- presence_confidence (Union[float_zero_to_one, dictionary]): float value in range [0.0, 1.0] if float_zero_to_one or Dictionary if dictionary.

Example JSON definition of step Detections Consensus in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/detections_consensus@v1",
    "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",
    "detections_merge_mask_aggregation": "union"
}