dlpy.image_captioning.create_embeddings_from_object_detection

dlpy.image_captioning.create_embeddings_from_object_detection(conn, image_table, detection_model, word_embeddings_file, n_threads=None, gpu=None, max_objects=5, word_delimiter='\t')

Builds CASTable with objects detected in images as numeric data

Parameters:

conn : CAS

Specifies the CAS connection object.

image_table: imageTable

Specifies name of CASTable that contains images to be used for training

detection_model : CASTable or string

Specifies CASTable containing model parameters for the object detection model

word_embeddings_file : string

Specifies full path to file containing pre-trained word vectors to be used for text generation This file should be accessible from the client.

n_threads : int, optional

Specifies the number of threads to use when scoring the table. All cores available used when nothing is set. Default : None

gpu : Gpu, optional

When specified, specifies which gpu to use when scoring the table. GPU=1 uses all available GPU devices and default parameters. Default : None

max_objects : int, optional

Specifies max number of objects detected if less than five Default : 5

word_delimiter : string, optional

Specifies delimiter used in word_embeddings file Default : ‘ ‘

Returns

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CASTable