Image Classification
How to log your model schema for image classification models
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
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 API reference for more details.Python Pandas
Python Single Record
UI Import JSON Input
Input for API
image_vector | image_link | prediction_label | actual_label | prediction_score | actual_score | Timestamp |
|---|---|---|---|---|---|---|
[1.0, 2, 3] | "https://link-to-my-image.png" | car | bus | 0.3 | 1 | 1618590882 |
from arize.pandas.logger import Client, Schema
from arize.utils.types import ModelTypes, Environments, EmbeddingColumnNames
API_KEY = 'ARIZE_API_KEY'
SPACE_KEY = 'YOUR SPACE KEY'
arize_client = Client(space_key=SPACE_KEY, 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",
]
# 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,
)
# 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,
)
Arize 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_nameshould 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_nameshould 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"
)
}
from arize.api import Client
from arize.utils.types import ModelTypes, Environments, Embedding
API_KEY = 'ARIZE_API_KEY'
SPACE_KEY = 'YOUR SPACE KEY'
arize_client = Client(space_key=SPACE_KEY, api_key=API_KEY)
# Example features
features = {
'state': 'ca',
'city': 'berkeley',
'merchant_name': 'Peets Coffee',
'pos_approved': True,
'item_count': 10,
'merchant_type': 'coffee shop',
'charge_amount': 20.11,
}
# Example embedding features
embedding_features = {
"image_embedding": Embedding(
vector=np.array([1.0, 2, 3]),
link_to_data="https://link-to-my-image.png",
),
}
# Log data into the Arize platform
response = arize.log(
model_id='sample-model-1',
model_version='v1",
model_type=ModelTypes.SCORE_CATEGORICAL,
environment=Environments.PRODUCTION,
features=features
prediction_label="not fraud",
prediction_score = 0.3
actual_label="fraud",
actual_score = 1
features=features,
embedding_features=embedding_features
)
Arize supports logging the embedding features associated with the image the model is acting on and the image itself using the
Embedding object.- The embedding
vectoris the dense vector representation of the unstructured input.Note: embedding features are not sparse vectors.⚠ - The embedding
link_to_datais used to pass URL links to the source image your model is classifying.
{
"embedding_display_name": Embedding(
vector=np.array([1.0, 2, 3]),
link_to_data="https://link-to-my-image.png",
)
}
When configuring an embedding in the UI using File Import
"embedding_features": [{
"my_feature": // #required, my_feature is the name of the feature
{
vector: "vector_col", // #required, vector_col is the column name of the vector
raw_data: "raw_data_col", // #optional
link_to_data: "link_to_data_col" // #optional
}
}]
Example file schema with embedding features
{
"prediction_id": "prediction_id",
"timestamp": "timestamp",
"tags": "tag/",
"prediction_score": "prediction_score",
"prediction_label": "prediction_label",
"actual_label": "actual_label",
"actual_score": "actual_score",
"shap_values": "shap/",
"version": "version", // lookup the column "version" in the file
"batch_id": "batch_id",
"exclude": [
"<column1 name>",
"<column2 name>"
],
"embedding_features": [
{
"embedding_1": {
"vector": "vector_column_1"
"raw_data": "raw_data_column_1",
"link_to_data": "link_to_data_column"
}
}
]
}
When configuring an embedding in the UI using the API
"embeddingFeatures": [{
"featureName": "my_feature",
"vectorCol": "vector_col",
"rawDataCol": "raw_data_col",
"linkToDataCol": "link_to_data_col"
}]
Example file schema with embedding features
prediction_id: prediction_id
timestamp: timestamp
features: feature/
tags: tag/
prediction_score: prediction_score
prediction_label: prediction_label
actual_label: actual_label
actual_score: actual_score
shap_values: shap/
version: version // lookup the column "version" in the file
batch_id: batch_id
exclude: // leave empty to omit column exclusions
embedding_features: // leave empty to omit embeddings