public static final class ImageObjectDetectionModelMetadata.Builder extends com.google.protobuf.GeneratedMessageV3.Builder<ImageObjectDetectionModelMetadata.Builder> implements ImageObjectDetectionModelMetadataOrBuilder
Model metadata specific to image object detection.Protobuf type
google.cloud.automl.v1.ImageObjectDetectionModelMetadatagetAllFields, getField, getFieldBuilder, getOneofFieldDescriptor, getParentForChildren, getRepeatedField, getRepeatedFieldBuilder, getRepeatedFieldCount, getUnknownFields, getUnknownFieldSetBuilder, hasField, hasOneof, internalGetMapField, internalGetMapFieldReflection, internalGetMutableMapField, internalGetMutableMapFieldReflection, isClean, markClean, mergeUnknownLengthDelimitedField, mergeUnknownVarintField, newBuilderForField, onBuilt, onChanged, parseUnknownField, setUnknownFieldSetBuilder, setUnknownFieldsProto3findInitializationErrors, getInitializationErrorString, internalMergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, newUninitializedMessageException, toStringaddAll, addAll, mergeDelimitedFrom, mergeDelimitedFrom, newUninitializedMessageExceptionequals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, waitpublic static final com.google.protobuf.Descriptors.Descriptor getDescriptor()
protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
internalGetFieldAccessorTable in class com.google.protobuf.GeneratedMessageV3.Builder<ImageObjectDetectionModelMetadata.Builder>public ImageObjectDetectionModelMetadata.Builder clear()
clear in interface com.google.protobuf.Message.Builderclear in interface com.google.protobuf.MessageLite.Builderclear in class com.google.protobuf.GeneratedMessageV3.Builder<ImageObjectDetectionModelMetadata.Builder>public com.google.protobuf.Descriptors.Descriptor getDescriptorForType()
getDescriptorForType in interface com.google.protobuf.Message.BuildergetDescriptorForType in interface com.google.protobuf.MessageOrBuildergetDescriptorForType in class com.google.protobuf.GeneratedMessageV3.Builder<ImageObjectDetectionModelMetadata.Builder>public ImageObjectDetectionModelMetadata getDefaultInstanceForType()
getDefaultInstanceForType in interface com.google.protobuf.MessageLiteOrBuildergetDefaultInstanceForType in interface com.google.protobuf.MessageOrBuilderpublic ImageObjectDetectionModelMetadata build()
build in interface com.google.protobuf.Message.Builderbuild in interface com.google.protobuf.MessageLite.Builderpublic ImageObjectDetectionModelMetadata buildPartial()
buildPartial in interface com.google.protobuf.Message.BuilderbuildPartial in interface com.google.protobuf.MessageLite.Builderpublic ImageObjectDetectionModelMetadata.Builder clone()
clone in interface com.google.protobuf.Message.Builderclone in interface com.google.protobuf.MessageLite.Builderclone in class com.google.protobuf.GeneratedMessageV3.Builder<ImageObjectDetectionModelMetadata.Builder>public ImageObjectDetectionModelMetadata.Builder setField(com.google.protobuf.Descriptors.FieldDescriptor field, Object value)
setField in interface com.google.protobuf.Message.BuildersetField in class com.google.protobuf.GeneratedMessageV3.Builder<ImageObjectDetectionModelMetadata.Builder>public ImageObjectDetectionModelMetadata.Builder clearField(com.google.protobuf.Descriptors.FieldDescriptor field)
clearField in interface com.google.protobuf.Message.BuilderclearField in class com.google.protobuf.GeneratedMessageV3.Builder<ImageObjectDetectionModelMetadata.Builder>public ImageObjectDetectionModelMetadata.Builder clearOneof(com.google.protobuf.Descriptors.OneofDescriptor oneof)
clearOneof in interface com.google.protobuf.Message.BuilderclearOneof in class com.google.protobuf.GeneratedMessageV3.Builder<ImageObjectDetectionModelMetadata.Builder>public ImageObjectDetectionModelMetadata.Builder setRepeatedField(com.google.protobuf.Descriptors.FieldDescriptor field, int index, Object value)
setRepeatedField in interface com.google.protobuf.Message.BuildersetRepeatedField in class com.google.protobuf.GeneratedMessageV3.Builder<ImageObjectDetectionModelMetadata.Builder>public ImageObjectDetectionModelMetadata.Builder addRepeatedField(com.google.protobuf.Descriptors.FieldDescriptor field, Object value)
addRepeatedField in interface com.google.protobuf.Message.BuilderaddRepeatedField in class com.google.protobuf.GeneratedMessageV3.Builder<ImageObjectDetectionModelMetadata.Builder>public ImageObjectDetectionModelMetadata.Builder mergeFrom(com.google.protobuf.Message other)
mergeFrom in interface com.google.protobuf.Message.BuildermergeFrom in class com.google.protobuf.AbstractMessage.Builder<ImageObjectDetectionModelMetadata.Builder>public ImageObjectDetectionModelMetadata.Builder mergeFrom(ImageObjectDetectionModelMetadata other)
public final boolean isInitialized()
isInitialized in interface com.google.protobuf.MessageLiteOrBuilderisInitialized in class com.google.protobuf.GeneratedMessageV3.Builder<ImageObjectDetectionModelMetadata.Builder>public ImageObjectDetectionModelMetadata.Builder mergeFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws IOException
mergeFrom in interface com.google.protobuf.Message.BuildermergeFrom in interface com.google.protobuf.MessageLite.BuildermergeFrom in class com.google.protobuf.AbstractMessage.Builder<ImageObjectDetectionModelMetadata.Builder>IOExceptionpublic 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.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.
string model_type = 1 [(.google.api.field_behavior) = OPTIONAL];getModelType in interface ImageObjectDetectionModelMetadataOrBuilderpublic 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.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.
string model_type = 1 [(.google.api.field_behavior) = OPTIONAL];getModelTypeBytes in interface ImageObjectDetectionModelMetadataOrBuilderpublic 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][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.
string model_type = 1 [(.google.api.field_behavior) = OPTIONAL];value - The modelType to set.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][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.
string model_type = 1 [(.google.api.field_behavior) = OPTIONAL];public ImageObjectDetectionModelMetadata.Builder setModelTypeBytes(com.google.protobuf.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][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.
string model_type = 1 [(.google.api.field_behavior) = OPTIONAL];value - The bytes for modelType to set.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 [(.google.api.field_behavior) = OUTPUT_ONLY];getNodeCount in interface ImageObjectDetectionModelMetadataOrBuilderpublic 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 [(.google.api.field_behavior) = OUTPUT_ONLY];value - The nodeCount to set.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 [(.google.api.field_behavior) = OUTPUT_ONLY];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 [(.google.api.field_behavior) = OUTPUT_ONLY];getNodeQps in interface ImageObjectDetectionModelMetadataOrBuilderpublic 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 [(.google.api.field_behavior) = OUTPUT_ONLY];value - The nodeQps to set.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 [(.google.api.field_behavior) = OUTPUT_ONLY];public String getStopReason()
Output only. The reason that this create model operation stopped, e.g. `BUDGET_REACHED`, `MODEL_CONVERGED`.
string stop_reason = 5 [(.google.api.field_behavior) = OUTPUT_ONLY];getStopReason in interface ImageObjectDetectionModelMetadataOrBuilderpublic 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 [(.google.api.field_behavior) = OUTPUT_ONLY];getStopReasonBytes in interface ImageObjectDetectionModelMetadataOrBuilderpublic 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 [(.google.api.field_behavior) = OUTPUT_ONLY];value - The stopReason to set.public ImageObjectDetectionModelMetadata.Builder clearStopReason()
Output only. The reason that this create model operation stopped, e.g. `BUDGET_REACHED`, `MODEL_CONVERGED`.
string stop_reason = 5 [(.google.api.field_behavior) = OUTPUT_ONLY];public ImageObjectDetectionModelMetadata.Builder setStopReasonBytes(com.google.protobuf.ByteString value)
Output only. The reason that this create model operation stopped, e.g. `BUDGET_REACHED`, `MODEL_CONVERGED`.
string stop_reason = 5 [(.google.api.field_behavior) = OUTPUT_ONLY];value - The bytes for stopReason to set.public long getTrainBudgetMilliNodeHours()
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.
int64 train_budget_milli_node_hours = 6 [(.google.api.field_behavior) = OPTIONAL];
getTrainBudgetMilliNodeHours in interface ImageObjectDetectionModelMetadataOrBuilderpublic ImageObjectDetectionModelMetadata.Builder setTrainBudgetMilliNodeHours(long value)
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.
int64 train_budget_milli_node_hours = 6 [(.google.api.field_behavior) = OPTIONAL];
value - The trainBudgetMilliNodeHours to set.public ImageObjectDetectionModelMetadata.Builder clearTrainBudgetMilliNodeHours()
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.
int64 train_budget_milli_node_hours = 6 [(.google.api.field_behavior) = OPTIONAL];
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 [(.google.api.field_behavior) = OUTPUT_ONLY];
getTrainCostMilliNodeHours in interface ImageObjectDetectionModelMetadataOrBuilderpublic 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 [(.google.api.field_behavior) = OUTPUT_ONLY];
value - The trainCostMilliNodeHours to set.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 [(.google.api.field_behavior) = OUTPUT_ONLY];
public final ImageObjectDetectionModelMetadata.Builder setUnknownFields(com.google.protobuf.UnknownFieldSet unknownFields)
setUnknownFields in interface com.google.protobuf.Message.BuildersetUnknownFields in class com.google.protobuf.GeneratedMessageV3.Builder<ImageObjectDetectionModelMetadata.Builder>public final ImageObjectDetectionModelMetadata.Builder mergeUnknownFields(com.google.protobuf.UnknownFieldSet unknownFields)
mergeUnknownFields in interface com.google.protobuf.Message.BuildermergeUnknownFields in class com.google.protobuf.GeneratedMessageV3.Builder<ImageObjectDetectionModelMetadata.Builder>Copyright © 2025 Google LLC. All rights reserved.