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

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
}