Kinds¶

In Workflows, some values can’t be set in advance and are only determined during execution. This is similar to writing a function where you don’t know the exact input values upfront — they’re only provided at runtime, either from user inputs or from other function outputs.

To manage this, Workflows use selectors, which act like references, pointing to data without containing it directly.

!!! Example selectors

Selectors might refer to a named input - for example input image - like `$inputs.image` 
or predictions generated by a previous step - like `$steps.my_model.predictions`

In the Workflows ecosystem, users focus on data purpose (e.g., “image”) without worrying about its exact format. Meanwhile, developers building workflow blocks need precise data formats. Kinds serve both needs - they simplify data handling for users while ensuring developers work with the correct data structure.

What are the Kinds?¶

Kinds is Workflows type system with each kind defining:

• name - expressing semantic meaning of the underlying data - like image or point;

• Python data representation - the data type and format that blocks creators should expect when handling the data within blocks;

• optional serialized data representation - defining what is the format of the kind that external systems should use to integrate with Workflows ecosystem - when needed, custom kinds serializers and deserializers are provided to ensure seamless translation;

Using kinds streamlines compatibility: when a step outputs data of a certain kind and another step requires that same kind, the workflow engine assumes they’ll be compatible, reducing the need for compatibility checks and providing compile-time verification of Workflows definitions.

Kinds declared in Roboflow plugins¶

• contours: List of numpy arrays where each array represents contour points
• keypoint_detection_prediction: Prediction with detected bounding boxes and detected keypoints in form of sv.Detections(...) object
• roboflow_project: Roboflow project name
• image: Image in workflows
• image_keypoints: Image keypoints detected by classical Computer Vision method
• integer: Integer value
• image_metadata: Dictionary with image metadata required by supervision
• string: String value
• object_detection_prediction: Prediction with detected bounding boxes in form of sv.Detections(...) object
• detection: Single element of detections-based prediction (like object_detection_prediction)
• inference_id: Inference identifier
• language_model_output: LLM / VLM output
• top_class: String value representing top class predicted by classification model
• float: Float value
• zone: Definition of polygon zone
• bytes: This kind represent bytes
• boolean: Boolean flag
• serialised_payloads: Serialised element that is usually accepted by sink
• instance_segmentation_prediction: Prediction with detected bounding boxes and segmentation masks in form of sv.Detections(...) object
• rgb_color: RGB color
• dictionary: Dictionary
• float_zero_to_one: float value in range [0.0, 1.0]
• bar_code_detection: Prediction with barcode detection
• prediction_type: String value with type of prediction
• roboflow_api_key: Roboflow API key
• qr_code_detection: Prediction with QR code detection
• roboflow_model_id: Roboflow model id
• classification_prediction: Predictions from classifier
• parent_id: Identifier of parent for step output
• list_of_values: List of values of any type
• video_metadata: Video image metadata
• *: Equivalent of any element
• point: Single point in 2D
• numpy_array: Numpy array