Package com.google.cloud.automl.v1beta1

Interface ImageObjectDetectionModelMetadataOrBuilder

    • Method Detail

      • getModelType

        String getModelType()
         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][google.cloud.automl.v1beta1.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.v1beta1.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.v1beta1.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.
        string model_type = 1;
        Returns:
        The modelType.

      • getModelTypeBytes

        com.google.protobuf.ByteString getModelTypeBytes()
         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][google.cloud.automl.v1beta1.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.v1beta1.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.v1beta1.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.
        string model_type = 1;
        Returns:
        The bytes for modelType.

      • getNodeCount

        long getNodeCount()
         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.
        int64 node_count = 3;
        Returns:
        The nodeCount.

      • getNodeQps

        double getNodeQps()
         Output only. An approximate number of online prediction QPS that can
 be supported by this model per each node on which it is deployed.
        double node_qps = 4;
        Returns:
        The nodeQps.

      • getStopReason

        String getStopReason()
         Output only. The reason that this create model operation stopped,
 e.g. `BUDGET_REACHED`, `MODEL_CONVERGED`.
        string stop_reason = 5;
        Returns:
        The stopReason.

      • getStopReasonBytes

        com.google.protobuf.ByteString getStopReasonBytes()
         Output only. The reason that this create model operation stopped,
 e.g. `BUDGET_REACHED`, `MODEL_CONVERGED`.
        string stop_reason = 5;
        Returns:
        The bytes for stopReason.

      • getTrainBudgetMilliNodeHours

        long getTrainBudgetMilliNodeHours()
         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.
        int64 train_budget_milli_node_hours = 6;
        Returns:
        The trainBudgetMilliNodeHours.

      • getTrainCostMilliNodeHours

        long getTrainCostMilliNodeHours()
         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.
        int64 train_cost_milli_node_hours = 7;
        Returns:
        The trainCostMilliNodeHours.