public final class ImageObjectDetectionModelMetadata extends GeneratedMessageV3 implements ImageObjectDetectionModelMetadataOrBuilder
Model metadata specific to image object detection.
Protobuf type google.cloud.automl.v1beta1.ImageObjectDetectionModelMetadata
Static Fields
public static final int MODEL_TYPE_FIELD_NUMBER
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public static final int NODE_COUNT_FIELD_NUMBER
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public static final int NODE_QPS_FIELD_NUMBER
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public static final int STOP_REASON_FIELD_NUMBER
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public static final int TRAIN_BUDGET_MILLI_NODE_HOURS_FIELD_NUMBER
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public static final int TRAIN_COST_MILLI_NODE_HOURS_FIELD_NUMBER
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Static Methods
public static ImageObjectDetectionModelMetadata getDefaultInstance()
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public static final Descriptors.Descriptor getDescriptor()
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public static ImageObjectDetectionModelMetadata.Builder newBuilder()
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public static ImageObjectDetectionModelMetadata.Builder newBuilder(ImageObjectDetectionModelMetadata prototype)
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public static ImageObjectDetectionModelMetadata parseDelimitedFrom(InputStream input)
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public static ImageObjectDetectionModelMetadata parseDelimitedFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
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public static ImageObjectDetectionModelMetadata parseFrom(byte[] data)
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| Name | Description |
| data | byte[]
|
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public static ImageObjectDetectionModelMetadata parseFrom(byte[] data, ExtensionRegistryLite extensionRegistry)
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public static ImageObjectDetectionModelMetadata parseFrom(ByteString data)
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public static ImageObjectDetectionModelMetadata parseFrom(ByteString data, ExtensionRegistryLite extensionRegistry)
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public static ImageObjectDetectionModelMetadata parseFrom(CodedInputStream input)
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public static ImageObjectDetectionModelMetadata parseFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
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public static ImageObjectDetectionModelMetadata parseFrom(InputStream input)
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public static ImageObjectDetectionModelMetadata parseFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
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public static ImageObjectDetectionModelMetadata parseFrom(ByteBuffer data)
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public static ImageObjectDetectionModelMetadata parseFrom(ByteBuffer data, ExtensionRegistryLite extensionRegistry)
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public static Parser<ImageObjectDetectionModelMetadata> parser()
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Methods
public boolean equals(Object obj)
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Overrides
public ImageObjectDetectionModelMetadata getDefaultInstanceForType()
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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.
|
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;
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.
|
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.
|
public Parser<ImageObjectDetectionModelMetadata> getParserForType()
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Overrides
public int getSerializedSize()
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Overrides
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.
|
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.
|
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.
|
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.
|
public final UnknownFieldSet getUnknownFields()
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protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
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public final boolean isInitialized()
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public ImageObjectDetectionModelMetadata.Builder newBuilderForType()
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protected ImageObjectDetectionModelMetadata.Builder newBuilderForType(GeneratedMessageV3.BuilderParent parent)
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protected Object newInstance(GeneratedMessageV3.UnusedPrivateParameter unused)
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public ImageObjectDetectionModelMetadata.Builder toBuilder()
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public void writeTo(CodedOutputStream output)
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Exceptions
