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..	❌

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.

• inputs: Identify Changes, Detection Offset, Line Counter, Detections Filter, Slack Notification, Time in Zone, Local File Sink, YOLO-World Model, Instance Segmentation Model, Roboflow Custom Metadata, Perspective Correction, OpenAI, Distance Measurement, Dimension Collapse, Clip Comparison, OpenAI, Gaze Detection, Line Counter, Size Measurement, Byte Tracker, Email Notification, Detections Classes Replacement, Object Detection Model, Template Matching, LMM, Detections Consensus, Roboflow Dataset Upload, Overlap Filter, Dynamic Crop, VLM as Classifier, VLM as Detector, Dynamic Zone, Velocity, Model Monitoring Inference Aggregator, Buffer, Segment Anything 2 Model, Object Detection Model, Llama 3.2 Vision, Anthropic Claude, Keypoint Detection Model, SIFT Comparison, CogVLM, SmolVLM2, VLM as Detector, Pixel Color Count, Path Deviation, Clip Comparison, Moondream2, Twilio SMS Notification, Google Vision OCR, Time in Zone, JSON Parser, Webhook Sink, Google Gemini, Roboflow Dataset Upload, Qwen2.5-VL, Byte Tracker, Detections Transformation, Florence-2 Model, SIFT Comparison, Detections Stabilizer, Image Contours, Instance Segmentation Model, Florence-2 Model, Detections Merge, Keypoint Detection Model, Detections Stitch, Bounding Rectangle, Byte Tracker, Identify Outliers, Path Deviation, VLM as Classifier
• outputs: Identify Changes, Detection Offset, Line Counter, Single-Label Classification Model, Polygon Zone Visualization, Detections Filter, Slack Notification, Time in Zone, YOLO-World Model, Instance Segmentation Model, Trace Visualization, Roboflow Custom Metadata, Perspective Correction, Distance Measurement, Circle Visualization, Image Slicer, Triangle Visualization, Halo Visualization, Gaze Detection, Line Counter, Byte Tracker, Size Measurement, Byte Tracker, Corner Visualization, Email Notification, Detections Classes Replacement, Object Detection Model, Template Matching, Detections Consensus, Roboflow Dataset Upload, Overlap Filter, Dynamic Crop, Velocity, Model Monitoring Inference Aggregator, Stitch Images, Segment Anything 2 Model, Object Detection Model, Model Comparison Visualization, Keypoint Detection Model, Crop Visualization, Blur Visualization, Relative Static Crop, Keypoint Visualization, Background Color Visualization, Path Deviation, Color Visualization, Twilio SMS Notification, Multi-Label Classification Model, Classification Label Visualization, Pixelate Visualization, Stitch OCR Detections, Label Visualization, Image Slicer, Time in Zone, Reference Path Visualization, Webhook Sink, Single-Label Classification Model, Roboflow Dataset Upload, Line Counter Visualization, Byte Tracker, Multi-Label Classification Model, Detections Transformation, Florence-2 Model, SIFT Comparison, Detections Stabilizer, Instance Segmentation Model, Polygon Visualization, Florence-2 Model, Detections Merge, Ellipse Visualization, Mask Visualization, Keypoint Detection Model, Detections Stitch, Bounding Box Visualization, Dot Visualization, Stability AI Image Generation, Identify Outliers, Path Deviation

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, instance_segmentation_prediction, keypoint_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

  • predictions (object_detection_prediction): Prediction with detected bounding boxes in form of sv.Detections(...) object.
  • 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"
}