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

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[integer, dictionary]): 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"
}