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

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[instance_segmentation_prediction, object_detection_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
}