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

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, classification_prediction, keypoint_detection_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
}