Class ImageClassificationModelMetadata (2.3.0) 

public sealed class ImageClassificationModelMetadata : IMessage<ImageClassificationModelMetadata>, IEquatable<ImageClassificationModelMetadata>, IDeepCloneable<ImageClassificationModelMetadata>, IBufferMessage, IMessage

Model metadata for image classification.

Inheritance

Object > ImageClassificationModelMetadata

Namespace

Google.Cloud.AutoML.V1

Assembly

Google.Cloud.AutoML.V1.dll

Constructors

ImageClassificationModelMetadata()

public ImageClassificationModelMetadata()

ImageClassificationModelMetadata(ImageClassificationModelMetadata)

public ImageClassificationModelMetadata(ImageClassificationModelMetadata other)
Parameter
NameDescription
otherImageClassificationModelMetadata

Properties

BaseModelId

public string BaseModelId { get; set; }

Optional. The ID of the base model. If it is specified, the new model will be created based on the base model. Otherwise, the new model will be created from scratch. The base model must be in the same project and location as the new model to create, and have the same model_type.

Property Value
TypeDescription
String

ModelType

public string ModelType { get; set; }

Optional. Type of the model. The available values are:

  • cloud - Model to be used via prediction calls to AutoML API. This is the default value.
  • mobile-low-latency-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device with TensorFlow afterwards. Expected to have low latency, but may have lower prediction quality than other models.
  • mobile-versatile-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device with TensorFlow afterwards.
  • mobile-high-accuracy-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device with TensorFlow afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other models.
  • mobile-core-ml-low-latency-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile device with Core ML afterwards. Expected to have low latency, but may have lower prediction quality than other models.
  • mobile-core-ml-versatile-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile device with Core ML afterwards.
  • mobile-core-ml-high-accuracy-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile device with Core ML afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other models.
Property Value
TypeDescription
String

NodeCount

public long NodeCount { get; set; }

Output only. The number of nodes this model is deployed on. A node is an abstraction of a machine resource, which can handle online prediction QPS as given in the node_qps field.

Property Value
TypeDescription
Int64

NodeQps

public double NodeQps { get; set; }

Output only. An approximate number of online prediction QPS that can be supported by this model per each node on which it is deployed.

Property Value
TypeDescription
Double

StopReason

public string StopReason { get; set; }

Output only. The reason that this create model operation stopped, e.g. BUDGET_REACHED, MODEL_CONVERGED.

Property Value
TypeDescription
String

TrainBudgetMilliNodeHours

public long TrainBudgetMilliNodeHours { get; set; }

The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The actual train_cost will be equal or less than this value. If further model training ceases to provide any improvements, it will stop without using full budget and the stop_reason will be MODEL_CONVERGED. Note, node_hour = actual_hour * number_of_nodes_invovled. For model type cloud(default), the train budget must be between 8,000 and 800,000 milli node hours, inclusive. The default value is 192, 000 which represents one day in wall time. For model type mobile-low-latency-1, mobile-versatile-1, mobile-high-accuracy-1, mobile-core-ml-low-latency-1, mobile-core-ml-versatile-1, mobile-core-ml-high-accuracy-1, the train budget must be between 1,000 and 100,000 milli node hours, inclusive. The default value is 24, 000 which represents one day in wall time.

Property Value
TypeDescription
Int64

TrainCostMilliNodeHours

public long TrainCostMilliNodeHours { get; set; }

Output only. The actual train cost of creating this model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed the train budget.

Property Value
TypeDescription
Int64