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@v1
to 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:
Google Gemini
,Keypoint Detection Model
,Detections Stabilizer
,Image Contours
,Path Deviation
,Perspective Correction
,Gaze Detection
,Instance Segmentation Model
,Local File Sink
,PTZ Tracking (ONVIF)
.md),Florence-2 Model
,Keypoint Detection Model
,Clip Comparison
,SIFT Comparison
,Detections Merge
,Time in Zone
,Roboflow Custom Metadata
,Dimension Collapse
,SmolVLM2
,Identify Outliers
,Template Matching
,Instance Segmentation Model
,Dynamic Zone
,Dynamic Crop
,Time in Zone
,Detections Consensus
,VLM as Classifier
,Segment Anything 2 Model
,VLM as Classifier
,Pixel Color Count
,SIFT Comparison
,Bounding Rectangle
,Detection Offset
,Size Measurement
,Object Detection Model
,Twilio SMS Notification
,Detections Filter
,Llama 3.2 Vision
,Clip Comparison
,Email Notification
,LMM
,Roboflow Dataset Upload
,Time in Zone
,Path Deviation
,Detections Transformation
,Byte Tracker
,Byte Tracker
,Qwen2.5-VL
,JSON Parser
,Webhook Sink
,Byte Tracker
,Object Detection Model
,Velocity
,Slack Notification
,Roboflow Dataset Upload
,Detections Classes Replacement
,YOLO-World Model
,OpenAI
,Model Monitoring Inference Aggregator
,VLM as Detector
,OpenAI
,Buffer
,Line Counter
,Moondream2
,Distance Measurement
,Overlap Filter
,Google Vision OCR
,Line Counter
,CogVLM
,Detections Stitch
,Florence-2 Model
,Identify Changes
,VLM as Detector
,OpenAI
,Anthropic Claude
- outputs:
Keypoint Visualization
,Keypoint Detection Model
,Detections Stabilizer
,Circle Visualization
,Path Deviation
,Perspective Correction
,Color Visualization
,Gaze Detection
,Instance Segmentation Model
,Reference Path Visualization
,Stitch Images
,Florence-2 Model
,Blur Visualization
,PTZ Tracking (ONVIF)
.md),Keypoint Detection Model
,Halo Visualization
,Relative Static Crop
,Stability AI Inpainting
,SIFT Comparison
,Detections Merge
,Icon Visualization
,Time in Zone
,Roboflow Custom Metadata
,Polygon Zone Visualization
,Identify Outliers
,Instance Segmentation Model
,Template Matching
,Stability AI Image Generation
,Dynamic Zone
,Dynamic Crop
,Crop Visualization
,Stitch OCR Detections
,Time in Zone
,Detections Consensus
,Single-Label Classification Model
,Segment Anything 2 Model
,Bounding Rectangle
,Size Measurement
,Detection Offset
,Model Comparison Visualization
,Object Detection Model
,Detections Filter
,Twilio SMS Notification
,Triangle Visualization
,Line Counter Visualization
,Multi-Label Classification Model
,Email Notification
,Roboflow Dataset Upload
,Time in Zone
,Image Slicer
,Path Deviation
,Detections Transformation
,Mask Visualization
,Byte Tracker
,Byte Tracker
,Single-Label Classification Model
,Pixelate Visualization
,Webhook Sink
,Byte Tracker
,Object Detection Model
,Velocity
,Slack Notification
,Dot Visualization
,Image Slicer
,Roboflow Dataset Upload
,Detections Classes Replacement
,YOLO-World Model
,Classification Label Visualization
,Model Monitoring Inference Aggregator
,Polygon Visualization
,Line Counter
,Trace Visualization
,Stability AI Outpainting
,Bounding Box Visualization
,Distance Measurement
,Multi-Label Classification Model
,Overlap Filter
,Label Visualization
,Line Counter
,Corner Visualization
,Detections Stitch
,Background Color Visualization
,Florence-2 Model
,Identify Changes
,Ellipse 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
,object_detection_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 toInferenceParameter
, 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 ifobject_detection_prediction
or Prediction with detected bounding boxes and segmentation masks in form of sv.Detections(...) object ifinstance_segmentation_prediction
.object_present
(Union[boolean
,dictionary
]): Boolean flag ifboolean
or Dictionary ifdictionary
.presence_confidence
(Union[float_zero_to_one
,dictionary
]):float
value in range[0.0, 1.0]
iffloat_zero_to_one
or Dictionary ifdictionary
.
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"
}