Detections Stitch

Class: DetectionsStitchBlockV1

Source: inference.core.workflows.core_steps.fusion.detections_stitch.v1.DetectionsStitchBlockV1

This block merges detections that were inferred for multiple sub-parts of the same input image into single detection.

Block may be helpful in the following scenarios: * to apply Slicing Adaptive Inference (SAHI) technique, as a final step of procedure, which involves Image Slicer block and model block at previous stages. * to merge together detections performed by precise, high-resolution model applied as secondary model after coarse detection is performed in the first stage and Dynamic Crop is applied later.

Type identifier

Use the following identifier in step "type" field: roboflow_core/detections_stitch@v1to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
overlap_filtering_strategy	str	Which strategy to employ when filtering overlapping boxes. None does nothing, NMS discards lower-confidence detections, NMM combines them..	✅
iou_threshold	float	Minimum overlap threshold between boxes. If intersection over union (IoU) is above this ratio, discard or merge the lower confidence 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 Stitch in version v1.

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

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Detections Stitch in version v1 has.

Bindings

• input

  • reference_image (image): Original image that was cropped to produce the predictions..
  • predictions (Union[object_detection_prediction, instance_segmentation_prediction]): Model predictions to be merged into the original image..
  • overlap_filtering_strategy (string): Which strategy to employ when filtering overlapping boxes. None does nothing, NMS discards lower-confidence detections, NMM combines them..
  • iou_threshold (float_zero_to_one): Minimum overlap threshold between boxes. If intersection over union (IoU) is above this ratio, discard or merge the lower confidence box..

• 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.

Example JSON definition of step Detections Stitch in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/detections_stitch@v1",
    "reference_image": "$inputs.image",
    "predictions": "$steps.my_object_detection_model.predictions",
    "overlap_filtering_strategy": "nms",
    "iou_threshold": 0.4
}