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: Detection Offset, SIFT Comparison, Line Counter, Detections Filter, VLM as Detector, Time in Zone, Pixel Color Count, Path Deviation, YOLO-World Model, Instance Segmentation Model, Perspective Correction, Moondream2, Distance Measurement, Google Vision OCR, Time in Zone, Gaze Detection, Byte Tracker, Line Counter, Detections Transformation, Byte Tracker, SIFT Comparison, Detections Stabilizer, Detections Classes Replacement, Object Detection Model, Template Matching, Detections Consensus, Image Contours, Overlap Filter, Dynamic Crop, Instance Segmentation Model, Detections Merge, Dynamic Zone, Velocity, Keypoint Detection Model, Detections Stitch, Bounding Rectangle, Byte Tracker, Segment Anything 2 Model, Object Detection Model, VLM as Detector, Path Deviation, Keypoint Detection Model
• outputs: Detection Offset, Blur Visualization, Line Counter, Stability AI Inpainting, Detections Filter, Time in Zone, Dot Visualization, Keypoint 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 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"
}