Class ModelType (1.29.0)

  ModelType 
 ( 
 value 
 ) 
 

Values: MODEL_TYPE_UNSPECIFIED (0): Should not be set. CLOUD (1): A Model best tailored to be used within Google Cloud, and which cannot be exported. Default. MOBILE_TF_LOW_LATENCY_1 (2): A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow or Core ML model and used on a mobile or edge device afterwards. Expected to have low latency, but may have lower prediction quality than other mobile models. MOBILE_TF_VERSATILE_1 (3): A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow or Core ML model and used on a mobile or edge device with afterwards. MOBILE_TF_HIGH_ACCURACY_1 (4): A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow or Core ML model and used on a mobile or edge device afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other mobile models.

Methods

ModelType

  ModelType 
 ( 
 value 
 ) 
 

Values: MODEL_TYPE_UNSPECIFIED (0): Should not be set. CLOUD (1): A Model best tailored to be used within Google Cloud, and which cannot be exported. Default. MOBILE_TF_LOW_LATENCY_1 (2): A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow or Core ML model and used on a mobile or edge device afterwards. Expected to have low latency, but may have lower prediction quality than other mobile models. MOBILE_TF_VERSATILE_1 (3): A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow or Core ML model and used on a mobile or edge device with afterwards. MOBILE_TF_HIGH_ACCURACY_1 (4): A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow or Core ML model and used on a mobile or edge device afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other mobile models.