Object Detection
How to declare your model schema and log data for object detection models
Object detection model type support is in
early release. Reach out to [email protected] 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)
Python Batch
image_vector | image_link | prediction_bboxes | actual_bboxes | prediction_categories | actual_categories | actual_super_categories | prediction_scores | Timestamp |
|---|---|---|---|---|---|---|---|---|
[0.24713118374347687, 0.7061651349067688, 1.12... | "https://link-to-my-image.png" | [[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] | 1618590882 |
embedding_feature_column_names={
"image_embedding": EmbeddingColumnNames(
vector_column_name="image_vector",
link_to_data_column_name="url"
)
}
object_detection_prediction_column_names=ObjectDetectionColumnNames(
bounding_boxes_coordinates_column_name="prediction_bboxes",
categories_column_name="prediction_categories",
scores_column_name="prediction_scores"
)
object_detection_actual_column_names=ObjectDetectionColumnNames(
bounding_boxes_coordinates_column_name="actual_bboxes",
categories_column_name="actual_categories",
)
# Defina the Schema, including embedding information
schema = Schema(
prediction_id_column_name="prediction_id",
timestamp_column_name="prediction_ts",
tag_column_names=tags,
embedding_feature_column_names=embedding_feature_column_names,
object_detection_prediction_column_names=object_detection_prediction_column_names,
object_detection_actual_column_names=object_detection_actual_column_names,
)
response = arize_client.log(
model_id= "CV-object-detection",
model_version= "v1",
model_type=ModelTypes.OBJECT_DETECTION,
environment=Environments.PRODUCTION,
dataframe=df,
schema=schema
)
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.
class ObjectDetectionColumnNames(
bounding_boxes_coordinates_column_name: str
categories_column_name: str
scores_column_name: Optional[str] = None # actual ground truth labels wont have scores
)
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
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 are stored.
{
"embedding_display_name": EmbeddingColumnNames(
vector_column_name="image_vector",
link_to_data_column_name="image_link"
)
}
Last modified 2mo ago