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public static final class ImageObjectDetectionModelMetadata.Builder extends GeneratedMessageV3.Builder<ImageObjectDetectionModelMetadata.Builder> implements ImageObjectDetectionModelMetadataOrBuilderModel metadata specific to image object detection.
Protobuf type google.cloud.automl.v1beta1.ImageObjectDetectionModelMetadata
Inheritance
Object > AbstractMessageLite.Builder<MessageType,BuilderType> > AbstractMessage.Builder<BuilderType> > GeneratedMessageV3.Builder > ImageObjectDetectionModelMetadata.BuilderImplements
ImageObjectDetectionModelMetadataOrBuilderStatic Methods
getDescriptor()
public static final Descriptors.Descriptor getDescriptor()| Type | Description |
| Descriptor |
Methods
addRepeatedField(Descriptors.FieldDescriptor field, Object value)
public ImageObjectDetectionModelMetadata.Builder addRepeatedField(Descriptors.FieldDescriptor field, Object value)| Name | Description |
| field | FieldDescriptor |
| value | Object |
| Type | Description |
| ImageObjectDetectionModelMetadata.Builder |
build()
public ImageObjectDetectionModelMetadata build()| Type | Description |
| ImageObjectDetectionModelMetadata |
buildPartial()
public ImageObjectDetectionModelMetadata buildPartial()| Type | Description |
| ImageObjectDetectionModelMetadata |
clear()
public ImageObjectDetectionModelMetadata.Builder clear()| Type | Description |
| ImageObjectDetectionModelMetadata.Builder |
clearField(Descriptors.FieldDescriptor field)
public ImageObjectDetectionModelMetadata.Builder clearField(Descriptors.FieldDescriptor field)| Name | Description |
| field | FieldDescriptor |
| Type | Description |
| ImageObjectDetectionModelMetadata.Builder |
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;
| Type | Description |
| 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;
| Type | Description |
| 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;
| Type | Description |
| ImageObjectDetectionModelMetadata.Builder | This builder for chaining. |
clearOneof(Descriptors.OneofDescriptor oneof)
public ImageObjectDetectionModelMetadata.Builder clearOneof(Descriptors.OneofDescriptor oneof)| Name | Description |
| oneof | OneofDescriptor |
| Type | Description |
| ImageObjectDetectionModelMetadata.Builder |
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;
| Type | Description |
| 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;
| Type | Description |
| 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;
| Type | Description |
| ImageObjectDetectionModelMetadata.Builder | This builder for chaining. |
clone()
public ImageObjectDetectionModelMetadata.Builder clone()| Type | Description |
| ImageObjectDetectionModelMetadata.Builder |
getDefaultInstanceForType()
public ImageObjectDetectionModelMetadata getDefaultInstanceForType()| Type | Description |
| ImageObjectDetectionModelMetadata |
getDescriptorForType()
public Descriptors.Descriptor getDescriptorForType()| Type | Description |
| Descriptor |
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;
| Type | Description |
| 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;
| Type | Description |
| 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;
| Type | Description |
| 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;
| Type | Description |
| 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;
| Type | Description |
| 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;
| Type | Description |
| 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;
| Type | Description |
| 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;
| Type | Description |
| long | The trainCostMilliNodeHours. |
internalGetFieldAccessorTable()
protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()| Type | Description |
| FieldAccessorTable |
isInitialized()
public final boolean isInitialized()| Type | Description |
| boolean |
mergeFrom(ImageObjectDetectionModelMetadata other)
public ImageObjectDetectionModelMetadata.Builder mergeFrom(ImageObjectDetectionModelMetadata other)| Name | Description |
| other | ImageObjectDetectionModelMetadata |
| Type | Description |
| ImageObjectDetectionModelMetadata.Builder |
mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
public ImageObjectDetectionModelMetadata.Builder mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)| Name | Description |
| input | CodedInputStream |
| extensionRegistry | ExtensionRegistryLite |
| Type | Description |
| ImageObjectDetectionModelMetadata.Builder |
| Type | Description |
| IOException |
mergeFrom(Message other)
public ImageObjectDetectionModelMetadata.Builder mergeFrom(Message other)| Name | Description |
| other | Message |
| Type | Description |
| ImageObjectDetectionModelMetadata.Builder |
mergeUnknownFields(UnknownFieldSet unknownFields)
public final ImageObjectDetectionModelMetadata.Builder mergeUnknownFields(UnknownFieldSet unknownFields)| Name | Description |
| unknownFields | UnknownFieldSet |
| Type | Description |
| ImageObjectDetectionModelMetadata.Builder |
setField(Descriptors.FieldDescriptor field, Object value)
public ImageObjectDetectionModelMetadata.Builder setField(Descriptors.FieldDescriptor field, Object value)| Name | Description |
| field | FieldDescriptor |
| value | Object |
| Type | Description |
| ImageObjectDetectionModelMetadata.Builder |
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;
| Name | Description |
| value | StringThe modelType to set. |
| Type | Description |
| 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;
| Name | Description |
| value | ByteStringThe bytes for modelType to set. |
| Type | Description |
| 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;
| Name | Description |
| value | longThe nodeCount to set. |
| Type | Description |
| 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;
| Name | Description |
| value | doubleThe nodeQps to set. |
| Type | Description |
| 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)| Name | Description |
| field | FieldDescriptor |
| index | int |
| value | Object |
| Type | Description |
| ImageObjectDetectionModelMetadata.Builder |
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;
| Name | Description |
| value | StringThe stopReason to set. |
| Type | Description |
| 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;
| Name | Description |
| value | ByteStringThe bytes for stopReason to set. |
| Type | Description |
| 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;
| Name | Description |
| value | longThe trainBudgetMilliNodeHours to set. |
| Type | Description |
| 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;
| Name | Description |
| value | longThe trainCostMilliNodeHours to set. |
| Type | Description |
| ImageObjectDetectionModelMetadata.Builder | This builder for chaining. |
setUnknownFields(UnknownFieldSet unknownFields)
public final ImageObjectDetectionModelMetadata.Builder setUnknownFields(UnknownFieldSet unknownFields)| Name | Description |
| unknownFields | UnknownFieldSet |
| Type | Description |
| ImageObjectDetectionModelMetadata.Builder |