Roboflow Custom Metadata

Class: RoboflowCustomMetadataBlockV1

Source: inference.core.workflows.core_steps.sinks.roboflow.custom_metadata.v1.RoboflowCustomMetadataBlockV1

Block allows users to add custom metadata to each inference result in Roboflow Model Monitoring dashboard.

This is useful for adding information specific to your use case. For example, if you want to be able to filter inferences by a specific label such as location, you can attach those labels to each inference with this block.

For more information on Model Monitoring at Roboflow, see https://docs.roboflow.com/deploy/model-monitoring.

Type identifier

Use the following identifier in step "type" field: roboflow_core/roboflow_custom_metadata@v1to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
field_value	str	This is the name of the metadata field you are creating.	✅
field_name	str	Name of the field to be updated..	❌
fire_and_forget	bool	Boolean flag to run the block asynchronously (True) for faster workflows or synchronously (False) for debugging and error handling..	✅

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 Roboflow Custom Metadata in version v1.

• inputs: Identify Changes, Detection Offset, Line Counter, Single-Label Classification Model, Detections Filter, Slack Notification, Time in Zone, Local File Sink, YOLO-World Model, Instance Segmentation Model, Roboflow Custom Metadata, Perspective Correction, OpenAI, Clip Comparison, OpenAI, Gaze Detection, Byte Tracker, Email Notification, Detections Classes Replacement, Object Detection Model, Template Matching, LMM, Detections Consensus, Roboflow Dataset Upload, Overlap Filter, Dynamic Crop, VLM as Classifier, Dynamic Zone, Velocity, Model Monitoring Inference Aggregator, Segment Anything 2 Model, Object Detection Model, Llama 3.2 Vision, Anthropic Claude, Keypoint Detection Model, SIFT Comparison, CogVLM, VLM as Detector, Path Deviation, Moondream2, Twilio SMS Notification, Multi-Label Classification Model, OCR Model, Google Vision OCR, Stitch OCR Detections, Time in Zone, Single-Label Classification Model, Webhook Sink, Google Gemini, Roboflow Dataset Upload, JSON Parser, Identify Outliers, Byte Tracker, Multi-Label Classification Model, Detections Transformation, Florence-2 Model, SIFT Comparison, Detections Stabilizer, LMM For Classification, Instance Segmentation Model, CSV Formatter, Florence-2 Model, Detections Merge, Keypoint Detection Model, Detections Stitch, Bounding Rectangle, Byte Tracker, VLM as Detector, Path Deviation, VLM as Classifier
• outputs: Line Counter, Single-Label Classification Model, Polygon Zone Visualization, Slack Notification, Time in Zone, Local File Sink, YOLO-World Model, Instance Segmentation Model, Trace Visualization, Roboflow Custom Metadata, Perspective Correction, OpenAI, Distance Measurement, Circle Visualization, Clip Comparison, OpenAI, Triangle Visualization, Halo Visualization, Gaze Detection, Line Counter, Size Measurement, Corner Visualization, Email Notification, Object Detection Model, Template Matching, LMM, Detections Consensus, Roboflow Dataset Upload, Dynamic Crop, Cache Set, Model Monitoring Inference Aggregator, Cache Get, Segment Anything 2 Model, Object Detection Model, Llama 3.2 Vision, Model Comparison Visualization, Anthropic Claude, Keypoint Detection Model, Crop Visualization, Blur Visualization, CLIP Embedding Model, CogVLM, Image Threshold, Stability AI Inpainting, Image Preprocessing, Keypoint Visualization, Background Color Visualization, Path Deviation, Pixel Color Count, Color Visualization, Twilio SMS Notification, Multi-Label Classification Model, Classification Label Visualization, Google Vision OCR, Pixelate Visualization, Label Visualization, Time in Zone, Reference Path Visualization, Webhook Sink, Single-Label Classification Model, Google Gemini, Roboflow Dataset Upload, Line Counter Visualization, Multi-Label Classification Model, Image Blur, Florence-2 Model, SIFT Comparison, LMM For Classification, Instance Segmentation Model, Polygon Visualization, Florence-2 Model, Ellipse Visualization, Mask Visualization, Keypoint Detection Model, Detections Stitch, Bounding Box Visualization, Dot Visualization, Stability AI Image Generation, Path Deviation

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Roboflow Custom Metadata in version v1 has.

Bindings

• input

  • predictions (Union[object_detection_prediction, instance_segmentation_prediction, keypoint_detection_prediction, classification_prediction]): Model predictions to attach custom metadata to..
  • field_value (string): This is the name of the metadata field you are creating.
  • fire_and_forget (boolean): Boolean flag to run the block asynchronously (True) for faster workflows or synchronously (False) for debugging and error handling..

• output

  • error_status (boolean): Boolean flag.
  • message (string): String value.

Example JSON definition of step Roboflow Custom Metadata in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/roboflow_custom_metadata@v1",
    "predictions": "$steps.my_step.predictions",
    "field_value": "toronto",
    "field_name": "The name of the value of the field",
    "fire_and_forget": true
}