Last updated
Copyright © 2023 Arize AI, Inc
Last updated
Object detection model type support is in early release.
Reach out to support@arize.com for access.
Object detection models identify and locate objects within images or videos by assigning them specific bounding boxes.
Applicable Metrics: Accuracy, Euclidian Distance (embeddings)
Click for all valid model types and metric combinations.
For more details on Python Batch API Reference, visit here:
Arize supports logging object detection prediction and actual values using the ObjectDetectionColumnNames
object, which can be assigned to the prediction/actual schema parameters, object_detection_prediction_column_names
and object_detection_actual_column_names
.
Object prediction or actual declaration is required to use the object detection model type in Arize.
The link_to_data_column_name
should be the name of the column where the URL links to the source images are stored.
In addition to object detection prediction and actual values, Arize supports logging the embedding features associated with the images in an object detection model using the object.
The vector_column_name
should be the name of the column where the embedding vectors are stored. The embedding vector is the dense vector representation of the unstructured input. Note: embedding features are not sparse vectors.
See for more information on embeddings and options for generating them.
How to declare your model schema and log data for object detection models
[0.24713118374347687, 0.7061651349067688, 1.12...
[[50.43, 109.49, 538.21...
[[55.39, 107.72, 539.25, 362.9], [554.41, 194....
[bus]
[bus, person, person]
[vehicle, person, person]
[0.9997552]