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