Object Detection

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ObjectDetectionLabel

Arize class to define the prediction and actual arguments associated with the Object Detection model type

class ObjectDetectionLabel(
    bounding_boxes_coordinates: List[List[float]]
    categories: List[str]
    scores: Optional[List[float]] = None
)

Argument	Data Type	Definitions
bounding_box_coordinates	List[List[float]]	(Required) Field defining the coordinates of the rectangular outline that locates an object within an image or video. Pascal VOC format required.
categories	List[str]	(Required) Field containing the predefined classes or labels used by the model to classify the detected objects.
scores	List[float]	(Optional*) Field containing the confidence scores that the model assigns to its predictions, indicating how certain the model is that the predicted class is contained within the bounding box. * This parameter is not applicable to actual (ground truth) labels and is only applicable when defining prediction_label

Code Example

from arize.utils.types import Environments, ModelTypes, Schema, ObjectDetectionLabel

object_detection_prediction = ObjectDetectionLabel(
    bounding_boxes_coordinates: [[147.5, 98.0, 169.67, 276.69], [114.19, 108.4...]]
    categories: ['chair', 'chair', 'bed', 'dining table', 'chair']
    scores: [0.90, 0.97, 0.98, 0.96, 0.96, 0.92, ...]
)

object_detection_actual = ObjectDetectionLabel(
    bounding_boxes_coordinates: [[437.51, 366.37, 529.0, 480.97], [0.0, 302.24...]]
    categories: ['dining table', 'bed', 'chair', 'chair']
)

response = arize.log(
    model_id="demo-object-detection-single-log",
    model_version="v1",
    environment=Environments.PRODUCTION,
    model_type=ModelTypes.OBJECT_DETECTION,
    prediction_id="123",
    prediction_label=object_detection_prediction,
    actual_label=object_detection_actual
)