Detection Offset

Class: DetectionOffsetBlockV1

Source: inference.core.workflows.core_steps.transformations.detection_offset.v1.DetectionOffsetBlockV1

Apply a fixed offset to the width and height of a detection.

You can use this block to add padding around the result of a detection. This is useful to ensure that you can analyze bounding boxes that may be within the region of an object instead of being around an object.

Type identifier

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

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
offset_width	int	Offset for box width..	✅
offset_height	int	Offset for box height..	✅
units	str	Units for offset dimensions..	❌

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 Detection Offset in version v1.

• inputs: Segment Anything 2 Model, Line Counter, Keypoint Detection Model, Line Counter, PTZ Tracking (ONVIF).md), Detections Stabilizer, Image Contours, Byte Tracker, Path Deviation, SIFT Comparison, Overlap Filter, Gaze Detection, Pixel Color Count, VLM as Detector, Object Detection Model, Time in Zone, Instance Segmentation Model, Dynamic Zone, Keypoint Detection Model, Google Vision OCR, Byte Tracker, Detections Consensus, Byte Tracker, Detection Offset, Detections Filter, Time in Zone, Object Detection Model, Bounding Rectangle, Detections Transformation, Detections Classes Replacement, YOLO-World Model, Detections Merge, Perspective Correction, Moondream2, Path Deviation, Template Matching, Instance Segmentation Model, SIFT Comparison, Velocity, Detections Stitch, Distance Measurement, VLM as Detector, Dynamic Crop
• outputs: Crop Visualization, Stitch OCR Detections, Line Counter, Segment Anything 2 Model, Line Counter, Triangle Visualization, Blur Visualization, PTZ Tracking (ONVIF).md), Background Color Visualization, Color Visualization, Detections Stabilizer, Corner Visualization, Byte Tracker, Path Deviation, Mask Visualization, Icon Visualization, Overlap Filter, Circle Visualization, Pixelate Visualization, Model Comparison Visualization, Time in Zone, Dynamic Zone, Byte Tracker, Detections Consensus, Roboflow Dataset Upload, Byte Tracker, Roboflow Dataset Upload, Detection Offset, Detections Filter, Trace Visualization, Roboflow Custom Metadata, Polygon Visualization, Keypoint Visualization, Dot Visualization, Time in Zone, Bounding Rectangle, Detections Transformation, Bounding Box Visualization, Detections Classes Replacement, Ellipse Visualization, Size Measurement, Halo Visualization, Detections Merge, Perspective Correction, Florence-2 Model, Stability AI Inpainting, Florence-2 Model, Path Deviation, Model Monitoring Inference Aggregator, Velocity, Detections Stitch, Label Visualization, Distance Measurement, Dynamic Crop

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Detection Offset in version v1 has.

Bindings

• input

  • predictions (Union[object_detection_prediction, instance_segmentation_prediction, keypoint_detection_prediction]): Model predictions to offset dimensions for..
  • offset_width (integer): Offset for box width..
  • offset_height (integer): Offset for box height..

• output

  • predictions (Union[object_detection_prediction, instance_segmentation_prediction, keypoint_detection_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 or Prediction with detected bounding boxes and detected keypoints in form of sv.Detections(...) object if keypoint_detection_prediction.

Example JSON definition of step Detection Offset in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/detection_offset@v1",
    "predictions": "$steps.object_detection_model.predictions",
    "offset_width": 10,
    "offset_height": 10,
    "units": "Pixels"
}