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:
Dynamic Crop,Instance Segmentation Model,Byte Tracker,Byte Tracker,Detections Transformation,Segment Anything 2 Model,Path Deviation,Detections Consensus,Image Contours,Gaze Detection,Keypoint Detection Model,Detections Stabilizer,Line Counter,Detections Merge,Keypoint Detection Model,SIFT Comparison,SIFT Comparison,Instance Segmentation Model,Time in Zone,Detections Filter,VLM as Detector,Detections Classes Replacement,Dynamic Zone,Byte Tracker,Object Detection Model,Bounding Rectangle,Path Deviation,Perspective Correction,Pixel Color Count,Detection Offset,Time in Zone,Google Vision OCR,YOLO-World Model,Template Matching,Line Counter,Distance Measurement,Detections Stitch,VLM as Detector,Object Detection Model,Velocity - outputs:
Color Visualization,Dynamic Crop,Byte Tracker,Detections Transformation,Background Color Visualization,Blur Visualization,Byte Tracker,Segment Anything 2 Model,Path Deviation,Detections Consensus,Line Counter,Dot Visualization,Model Monitoring Inference Aggregator,Detections Stabilizer,Stitch OCR Detections,Triangle Visualization,Detections Merge,Stability AI Inpainting,Bounding Box Visualization,Size Measurement,Pixelate Visualization,Mask Visualization,Florence-2 Model,Time in Zone,Detections Filter,Model Comparison Visualization,Corner Visualization,Detections Classes Replacement,Dynamic Zone,Roboflow Custom Metadata,Byte Tracker,Bounding Rectangle,Path Deviation,Perspective Correction,Halo Visualization,Florence-2 Model,Detection Offset,Time in Zone,Trace Visualization,Roboflow Dataset Upload,Line Counter,Roboflow Dataset Upload,Keypoint Visualization,Distance Measurement,Ellipse Visualization,Label Visualization,Detections Stitch,Circle Visualization,Polygon Visualization,Velocity,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[keypoint_detection_prediction,object_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 ifobject_detection_predictionor Prediction with detected bounding boxes and segmentation masks in form of sv.Detections(...) object ifinstance_segmentation_predictionor Prediction with detected bounding boxes and detected keypoints in form of sv.Detections(...) object ifkeypoint_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"
}