Class: Google::Cloud::AutoML::V1::ImageObjectDetectionModelMetadata

Inherits:
Object
  • Object
show all
Extended by:
Protobuf::MessageExts::ClassMethods
Includes:
Protobuf::MessageExts
Defined in:
proto_docs/google/cloud/automl/v1/image.rb

Overview

Model metadata specific to image object detection.

Instance Attribute Summary collapse

Instance Attribute Details

#model_type ⇒ ::String

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

  • cloud-high-accuracy-1 - (default) A model to be used via prediction calls to AutoML API. Expected to have a higher latency, but should also have a higher prediction quality than other models.
  • cloud-low-latency-1 - A model to be used via prediction calls to AutoML API. Expected to have low latency, but may have lower prediction quality than other models.
  • mobile-low-latency-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see 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) 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) 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.

Returns:

  • (::String) —

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

    • cloud-high-accuracy-1 - (default) A model to be used via prediction calls to AutoML API. Expected to have a higher latency, but should also have a higher prediction quality than other models.
    • cloud-low-latency-1 - A model to be used via prediction calls to AutoML API. Expected to have low latency, but may have lower prediction quality than other models.
    • mobile-low-latency-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see 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) 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) 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.


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# File 'proto_docs/google/cloud/automl/v1/image.rb', line 182

class ImageObjectDetectionModelMetadata
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods
end

#node_count ⇒ ::Integer (readonly)

Returns 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 qps_per_node field.

Returns:

  • (::Integer) —

    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 qps_per_node field.



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# File 'proto_docs/google/cloud/automl/v1/image.rb', line 182

class ImageObjectDetectionModelMetadata
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods
end

#node_qps ⇒ ::Float (readonly)

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

Returns:

  • (::Float) —

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



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# File 'proto_docs/google/cloud/automl/v1/image.rb', line 182

class ImageObjectDetectionModelMetadata
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods
end

#stop_reason ⇒ ::String (readonly)

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

Returns:

  • (::String) —

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



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# File 'proto_docs/google/cloud/automl/v1/image.rb', line 182

class ImageObjectDetectionModelMetadata
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods
end

#train_budget_milli_node_hours ⇒ ::Integer

Returns Optional. 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-high-accuracy-1(default) and cloud-low-latency-1, the train budget must be between 20,000 and 900,000 milli node hours, inclusive. The default value is 216, 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.

Returns:

  • (::Integer) —

    Optional. 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-high-accuracy-1(default) and cloud-low-latency-1, the train budget must be between 20,000 and 900,000 milli node hours, inclusive. The default value is 216, 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.



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# File 'proto_docs/google/cloud/automl/v1/image.rb', line 182

class ImageObjectDetectionModelMetadata
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods
end

#train_cost_milli_node_hours ⇒ ::Integer (readonly)

Returns 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.

Returns:

  • (::Integer) —

    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.



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# File 'proto_docs/google/cloud/automl/v1/image.rb', line 182

class ImageObjectDetectionModelMetadata
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods
end