Class ImageObjectDetectionModelMetadata.Builder (2.3.1)

public static final class ImageObjectDetectionModelMetadata.Builder extends GeneratedMessageV3.Builder<ImageObjectDetectionModelMetadata.Builder> implements ImageObjectDetectionModelMetadataOrBuilder

Model metadata specific to image object detection.

Protobuf type google.cloud.automl.v1beta1.ImageObjectDetectionModelMetadata

Static Methods

getDescriptor()

public static final Descriptors.Descriptor getDescriptor()
Returns
TypeDescription
Descriptor

Methods

addRepeatedField(Descriptors.FieldDescriptor field, Object value)

public ImageObjectDetectionModelMetadata.Builder addRepeatedField(Descriptors.FieldDescriptor field, Object value)
Parameters
NameDescription
fieldFieldDescriptor
valueObject
Returns
TypeDescription
ImageObjectDetectionModelMetadata.Builder
Overrides

build()

public ImageObjectDetectionModelMetadata build()
Returns
TypeDescription
ImageObjectDetectionModelMetadata

buildPartial()

public ImageObjectDetectionModelMetadata buildPartial()
Returns
TypeDescription
ImageObjectDetectionModelMetadata

clear()

public ImageObjectDetectionModelMetadata.Builder clear()
Returns
TypeDescription
ImageObjectDetectionModelMetadata.Builder
Overrides

clearField(Descriptors.FieldDescriptor field)

public ImageObjectDetectionModelMetadata.Builder clearField(Descriptors.FieldDescriptor field)
Parameter
NameDescription
fieldFieldDescriptor
Returns
TypeDescription
ImageObjectDetectionModelMetadata.Builder
Overrides

clearModelType()

public ImageObjectDetectionModelMetadata.Builder clearModelType()

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.

string model_type = 1;

Returns
TypeDescription
ImageObjectDetectionModelMetadata.Builder

This builder for chaining.

clearNodeCount()

public ImageObjectDetectionModelMetadata.Builder clearNodeCount()

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
TypeDescription
ImageObjectDetectionModelMetadata.Builder

This builder for chaining.

clearNodeQps()

public ImageObjectDetectionModelMetadata.Builder clearNodeQps()

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
TypeDescription
ImageObjectDetectionModelMetadata.Builder

This builder for chaining.

clearOneof(Descriptors.OneofDescriptor oneof)

public ImageObjectDetectionModelMetadata.Builder clearOneof(Descriptors.OneofDescriptor oneof)
Parameter
NameDescription
oneofOneofDescriptor
Returns
TypeDescription
ImageObjectDetectionModelMetadata.Builder
Overrides

clearStopReason()

public ImageObjectDetectionModelMetadata.Builder clearStopReason()

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

string stop_reason = 5;

Returns
TypeDescription
ImageObjectDetectionModelMetadata.Builder

This builder for chaining.

clearTrainBudgetMilliNodeHours()

public ImageObjectDetectionModelMetadata.Builder clearTrainBudgetMilliNodeHours()

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
TypeDescription
ImageObjectDetectionModelMetadata.Builder

This builder for chaining.

clearTrainCostMilliNodeHours()

public ImageObjectDetectionModelMetadata.Builder clearTrainCostMilliNodeHours()

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
TypeDescription
ImageObjectDetectionModelMetadata.Builder

This builder for chaining.

clone()

public ImageObjectDetectionModelMetadata.Builder clone()
Returns
TypeDescription
ImageObjectDetectionModelMetadata.Builder
Overrides

getDefaultInstanceForType()

public ImageObjectDetectionModelMetadata getDefaultInstanceForType()
Returns
TypeDescription
ImageObjectDetectionModelMetadata

getDescriptorForType()

public Descriptors.Descriptor getDescriptorForType()
Returns
TypeDescription
Descriptor
Overrides

getModelType()

public 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) 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.

string model_type = 1;

Returns
TypeDescription
String

The modelType.

getModelTypeBytes()

public 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) 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.

string model_type = 1;

Returns
TypeDescription
ByteString

The bytes for modelType.

getNodeCount()

public 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
TypeDescription
long

The nodeCount.

getNodeQps()

public 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
TypeDescription
double

The nodeQps.

getStopReason()

public String getStopReason()

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

string stop_reason = 5;

Returns
TypeDescription
String

The stopReason.

getStopReasonBytes()

public ByteString getStopReasonBytes()

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

string stop_reason = 5;

Returns
TypeDescription
ByteString

The bytes for stopReason.

getTrainBudgetMilliNodeHours()

public 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
TypeDescription
long

The trainBudgetMilliNodeHours.

getTrainCostMilliNodeHours()

public 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
TypeDescription
long

The trainCostMilliNodeHours.

internalGetFieldAccessorTable()

protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
Returns
TypeDescription
FieldAccessorTable
Overrides

isInitialized()

public final boolean isInitialized()
Returns
TypeDescription
boolean
Overrides

mergeFrom(ImageObjectDetectionModelMetadata other)

public ImageObjectDetectionModelMetadata.Builder mergeFrom(ImageObjectDetectionModelMetadata other)
Parameter
NameDescription
otherImageObjectDetectionModelMetadata
Returns
TypeDescription
ImageObjectDetectionModelMetadata.Builder

mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

public ImageObjectDetectionModelMetadata.Builder mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
inputCodedInputStream
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
ImageObjectDetectionModelMetadata.Builder
Overrides Exceptions
TypeDescription
IOException

mergeFrom(Message other)

public ImageObjectDetectionModelMetadata.Builder mergeFrom(Message other)
Parameter
NameDescription
otherMessage
Returns
TypeDescription
ImageObjectDetectionModelMetadata.Builder
Overrides

mergeUnknownFields(UnknownFieldSet unknownFields)

public final ImageObjectDetectionModelMetadata.Builder mergeUnknownFields(UnknownFieldSet unknownFields)
Parameter
NameDescription
unknownFieldsUnknownFieldSet
Returns
TypeDescription
ImageObjectDetectionModelMetadata.Builder
Overrides

setField(Descriptors.FieldDescriptor field, Object value)

public ImageObjectDetectionModelMetadata.Builder setField(Descriptors.FieldDescriptor field, Object value)
Parameters
NameDescription
fieldFieldDescriptor
valueObject
Returns
TypeDescription
ImageObjectDetectionModelMetadata.Builder
Overrides

setModelType(String value)

public ImageObjectDetectionModelMetadata.Builder setModelType(String value)

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.

string model_type = 1;

Parameter
NameDescription
valueString

The modelType to set.

Returns
TypeDescription
ImageObjectDetectionModelMetadata.Builder

This builder for chaining.

setModelTypeBytes(ByteString value)

public ImageObjectDetectionModelMetadata.Builder setModelTypeBytes(ByteString value)

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.

string model_type = 1;

Parameter
NameDescription
valueByteString

The bytes for modelType to set.

Returns
TypeDescription
ImageObjectDetectionModelMetadata.Builder

This builder for chaining.

setNodeCount(long value)

public ImageObjectDetectionModelMetadata.Builder setNodeCount(long value)

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;

Parameter
NameDescription
valuelong

The nodeCount to set.

Returns
TypeDescription
ImageObjectDetectionModelMetadata.Builder

This builder for chaining.

setNodeQps(double value)

public ImageObjectDetectionModelMetadata.Builder setNodeQps(double value)

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;

Parameter
NameDescription
valuedouble

The nodeQps to set.

Returns
TypeDescription
ImageObjectDetectionModelMetadata.Builder

This builder for chaining.

setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

public ImageObjectDetectionModelMetadata.Builder setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)
Parameters
NameDescription
fieldFieldDescriptor
indexint
valueObject
Returns
TypeDescription
ImageObjectDetectionModelMetadata.Builder
Overrides

setStopReason(String value)

public ImageObjectDetectionModelMetadata.Builder setStopReason(String value)

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

string stop_reason = 5;

Parameter
NameDescription
valueString

The stopReason to set.

Returns
TypeDescription
ImageObjectDetectionModelMetadata.Builder

This builder for chaining.

setStopReasonBytes(ByteString value)

public ImageObjectDetectionModelMetadata.Builder setStopReasonBytes(ByteString value)

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

string stop_reason = 5;

Parameter
NameDescription
valueByteString

The bytes for stopReason to set.

Returns
TypeDescription
ImageObjectDetectionModelMetadata.Builder

This builder for chaining.

setTrainBudgetMilliNodeHours(long value)

public ImageObjectDetectionModelMetadata.Builder setTrainBudgetMilliNodeHours(long value)

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;

Parameter
NameDescription
valuelong

The trainBudgetMilliNodeHours to set.

Returns
TypeDescription
ImageObjectDetectionModelMetadata.Builder

This builder for chaining.

setTrainCostMilliNodeHours(long value)

public ImageObjectDetectionModelMetadata.Builder setTrainCostMilliNodeHours(long value)

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;

Parameter
NameDescription
valuelong

The trainCostMilliNodeHours to set.

Returns
TypeDescription
ImageObjectDetectionModelMetadata.Builder

This builder for chaining.

setUnknownFields(UnknownFieldSet unknownFields)

public final ImageObjectDetectionModelMetadata.Builder setUnknownFields(UnknownFieldSet unknownFields)
Parameter
NameDescription
unknownFieldsUnknownFieldSet
Returns
TypeDescription
ImageObjectDetectionModelMetadata.Builder
Overrides