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.

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

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"
}