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

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, keypoint_detection_prediction, object_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
}