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Copyright © 2023 Arize AI, Inc
Last updated
Image classification models take an image as input and return a predicted label for the image.
*all variant specifications apply to the Image Classification model type, with the addition of embeddings
Accuracy, Recall, Precision, FPR, FNR, F1, Sensitivity, Specificity
The EmbeddingColumnNames class constructs your embedding objects. You can log them into the platform using a dictionary that maps the embedding feature names to the embedding objects. See our for more details.
Navigate for step-by-step instructions to view private AWS S3 image links.
Arize supports logging the embedding features associated with the image the model is acting on and the image itself 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.
Arize supports logging the embedding features associated with the image the model is acting on and the image itself using the object.
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 log your model schema for image classification models
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