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