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

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"
}