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@v1
to 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 ifobject_detection_prediction
or Prediction with detected bounding boxes and segmentation masks in form of sv.Detections(...) object ifinstance_segmentation_prediction
or 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"
}