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

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, keypoint_detection_prediction, instance_segmentation_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"
}