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Image Classification Model Overview

Image classification models take an image as input and return a predicted label for the image. *all classification variant specifications apply to the Image Classification model type, with the addition of embeddings

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Performance Metrics

Accuracy, Recall, Precision, FPR, FNR, F1, Sensitivity, Specificity

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Code Example

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 API reference for more details.
Navigate here for step-by-step instructions to view private AWS S3 image links.
Example Row
image_vectorimage_linkprediction_labelactual_labelprediction_scoreactual_scoreTimestamp
[1.0, 2, 3]"https://link-to-my-image.png"carbus0.311618590882
from arize.pandas.logger import Client, Schema
from arize.utils.types import ModelTypes, Environments, EmbeddingColumnNames

API_KEY = 'ARIZE_API_KEY'
SPACE_ID = 'YOUR SPACE ID'
arize_client = Client(space_id=SPACE_ID, api_key=API_KEY)


# Declare which columns are the feature columns
feature_column_names=[
    "MERCHANT_TYPE", 
    "ENTRY_MODE", 
    "STATE", 
    "MEAN_AMOUNT", 
    "STD_AMOUNT", 
    "TX_AMOUNT",
]

# feature & tag columns can be optionally defined with typing:
tag_columns = TypedColumns(
    inferred=["name"],
    to_int=["zip_code", "age"]
)

# Declare embedding feature columns
embedding_feature_column_names = {
    # Dictionary keys will be the name of the embedding feature in the app
    "embedding_display_name": EmbeddingColumnNames(
        vector_column_name="image_vector",  # column name of the vectors, required
        link_to_data_column_name="image_link", # column name of the link to the images, optional
    )
}

# Defina the Schema, including embedding information
schema = Schema(
    prediction_id_column_name="prediction_id",
    timestamp_column_name="prediction_ts",
    prediction_label_column_name="PREDICTION",
    prediction_score_column_name="PREDICTION_SCORE",
    actual_label_column_name="ACTUAL",
    actual_score_column_name="ACTUAL_SCORE",
    feature_column_names=feature_column_names,
    embedding_feature_column_names=embedding_feature_column_names,
    tag_column_names=tag_columns,
)

# Log the dataframe with the schema mapping 
response = arize_client.log(
    model_id="sample-model-1",
    model_version= "v1",
    model_type=ModelTypes.SCORE_CATEGORICAL,
    environment=Environments.PRODUCTION,
    dataframe=test_dataframe,
    schema=schema,
)
https://storage.googleapis.com/arize-phoenix-assets/assets/images/phoenix-docs-images/gc.ico

Google Colaboratory
Image Classification Embedding FeaturesArize supports logging the embedding features associated with the image the model is acting on and the image itself using the EmbeddingColumnNames 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.
  • The link_to_data_column_name should be the name of the column where the URL links to the source images, that your model classifies, are stored. 
{ 
    "embedding_display_name": EmbeddingColumnNames(
        vector_column_name="image_vector", 
        link_to_data_column_name="image_link" 
    ) 
}
See here for more information on embeddings and options for generating them.