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

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[classification_prediction, object_detection_prediction, instance_segmentation_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
}