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