Reference documentation and code samples for the Cloud AutoML V1 Client class ImageObjectDetectionModelMetadata.
Model metadata specific to image object detection.
Generated from protobuf message google.cloud.automl.v1.ImageObjectDetectionModelMetadata
Namespace
Google \ Cloud \ AutoMl \ V1
Methods
__construct
Constructor.
Parameters
Name
Description
data
array
Optional. Data for populating the Message object.
↳ model_type
string
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.
↳ node_count
int|string
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.
↳ node_qps
float
Output only. An approximate number of online prediction QPS that can be supported by this model per each node on which it is deployed.
↳ stop_reason
string
Output only. The reason that this create model operation stopped, e.g. BUDGET_REACHED, MODEL_CONVERGED.
↳ train_budget_milli_node_hours
int|string
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.
↳ train_cost_milli_node_hours
int|string
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.
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.
Returns
Type
Description
string
setModelType
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.
Parameter
Name
Description
var
string
Returns
Type
Description
$this
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.
Returns
Type
Description
int|string
setNodeCount
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.
Parameter
Name
Description
var
int|string
Returns
Type
Description
$this
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.
Returns
Type
Description
float
setNodeQps
Output only. An approximate number of online prediction QPS that can
be supported by this model per each node on which it is deployed.
Parameter
Name
Description
var
float
Returns
Type
Description
$this
getStopReason
Output only. The reason that this create model operation stopped,
e.g. BUDGET_REACHED, MODEL_CONVERGED.
Returns
Type
Description
string
setStopReason
Output only. The reason that this create model operation stopped,
e.g. BUDGET_REACHED, MODEL_CONVERGED.
Parameter
Name
Description
var
string
Returns
Type
Description
$this
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.
Returns
Type
Description
int|string
setTrainBudgetMilliNodeHours
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.
Parameter
Name
Description
var
int|string
Returns
Type
Description
$this
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.
Returns
Type
Description
int|string
setTrainCostMilliNodeHours
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.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-09-09 UTC."],[],[],null,["# Cloud AutoML V1 Client - Class ImageObjectDetectionModelMetadata (2.0.5)\n\nVersion latestkeyboard_arrow_down\n\n- [2.0.5 (latest)](/php/docs/reference/cloud-automl/latest/V1.ImageObjectDetectionModelMetadata)\n- [2.0.4](/php/docs/reference/cloud-automl/2.0.4/V1.ImageObjectDetectionModelMetadata)\n- [1.6.5](/php/docs/reference/cloud-automl/1.6.5/V1.ImageObjectDetectionModelMetadata)\n- [1.5.4](/php/docs/reference/cloud-automl/1.5.4/V1.ImageObjectDetectionModelMetadata)\n- [1.4.17](/php/docs/reference/cloud-automl/1.4.17/V1.ImageObjectDetectionModelMetadata) \nReference documentation and code samples for the Cloud AutoML V1 Client class ImageObjectDetectionModelMetadata.\n\nModel metadata specific to image object detection.\n\nGenerated from protobuf message `google.cloud.automl.v1.ImageObjectDetectionModelMetadata`\n\nNamespace\n---------\n\nGoogle \\\\ Cloud \\\\ AutoMl \\\\ V1\n\nMethods\n-------\n\n### __construct\n\nConstructor.\n\n### getModelType\n\nOptional. Type of the model. The available values are:\n\n- `cloud-high-accuracy-1` - (default) A model to be used via prediction\n calls to AutoML API. Expected to have a higher latency, but\n should also have a higher prediction quality than other\n models.\n\n- `cloud-low-latency-1` - A model to be used via prediction\n calls to AutoML API. Expected to have low latency, but may\n have lower prediction quality than other models.\n\n- `mobile-low-latency-1` - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel](/php/docs/reference/cloud-automl/latest/V1.Client.AutoMlClient#_Google_Cloud_AutoMl_V1_Client_AutoMlClient__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.\n- `mobile-versatile-1` - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel](/php/docs/reference/cloud-automl/latest/V1.Client.AutoMlClient#_Google_Cloud_AutoMl_V1_Client_AutoMlClient__exportModel__)) and used on a mobile or edge device with TensorFlow afterwards.\n- `mobile-high-accuracy-1` - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel](/php/docs/reference/cloud-automl/latest/V1.Client.AutoMlClient#_Google_Cloud_AutoMl_V1_Client_AutoMlClient__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.\n\n### setModelType\n\nOptional. Type of the model. The available values are:\n\n- `cloud-high-accuracy-1` - (default) A model to be used via prediction\n calls to AutoML API. Expected to have a higher latency, but\n should also have a higher prediction quality than other\n models.\n\n- `cloud-low-latency-1` - A model to be used via prediction\n calls to AutoML API. Expected to have low latency, but may\n have lower prediction quality than other models.\n\n- `mobile-low-latency-1` - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel](/php/docs/reference/cloud-automl/latest/V1.Client.AutoMlClient#_Google_Cloud_AutoMl_V1_Client_AutoMlClient__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.\n- `mobile-versatile-1` - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel](/php/docs/reference/cloud-automl/latest/V1.Client.AutoMlClient#_Google_Cloud_AutoMl_V1_Client_AutoMlClient__exportModel__)) and used on a mobile or edge device with TensorFlow afterwards.\n- `mobile-high-accuracy-1` - A model that, in addition to providing prediction via AutoML API, can also be exported (see [AutoMl.ExportModel](/php/docs/reference/cloud-automl/latest/V1.Client.AutoMlClient#_Google_Cloud_AutoMl_V1_Client_AutoMlClient__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.\n\n### getNodeCount\n\nOutput only. The number of nodes this model is deployed on. A node is an\nabstraction of a machine resource, which can handle online prediction QPS\nas given in the qps_per_node field.\n\n### setNodeCount\n\nOutput only. The number of nodes this model is deployed on. A node is an\nabstraction of a machine resource, which can handle online prediction QPS\nas given in the qps_per_node field.\n\n### getNodeQps\n\nOutput only. An approximate number of online prediction QPS that can\nbe supported by this model per each node on which it is deployed.\n\n### setNodeQps\n\nOutput only. An approximate number of online prediction QPS that can\nbe supported by this model per each node on which it is deployed.\n\n### getStopReason\n\nOutput only. The reason that this create model operation stopped,\ne.g. `BUDGET_REACHED`, `MODEL_CONVERGED`.\n\n### setStopReason\n\nOutput only. The reason that this create model operation stopped,\ne.g. `BUDGET_REACHED`, `MODEL_CONVERGED`.\n\n### getTrainBudgetMilliNodeHours\n\nOptional. The train budget of creating this model, expressed in milli node\nhours i.e. 1,000 value in this field means 1 node hour. The actual\n`train_cost` will be equal or less than this value. If further model\ntraining ceases to provide any improvements, it will stop without using\nfull budget and the stop_reason will be `MODEL_CONVERGED`.\n\nNote, node_hour = actual_hour \\* number_of_nodes_invovled.\nFor model type `cloud-high-accuracy-1`(default) and `cloud-low-latency-1`,\nthe train budget must be between 20,000 and 900,000 milli node hours,\ninclusive. The default value is 216, 000 which represents one day in\nwall time.\nFor model type `mobile-low-latency-1`, `mobile-versatile-1`,\n`mobile-high-accuracy-1`, `mobile-core-ml-low-latency-1`,\n`mobile-core-ml-versatile-1`, `mobile-core-ml-high-accuracy-1`, the train\nbudget must be between 1,000 and 100,000 milli node hours, inclusive.\nThe default value is 24, 000 which represents one day in wall time.\n\n### setTrainBudgetMilliNodeHours\n\nOptional. The train budget of creating this model, expressed in milli node\nhours i.e. 1,000 value in this field means 1 node hour. The actual\n`train_cost` will be equal or less than this value. If further model\ntraining ceases to provide any improvements, it will stop without using\nfull budget and the stop_reason will be `MODEL_CONVERGED`.\n\nNote, node_hour = actual_hour \\* number_of_nodes_invovled.\nFor model type `cloud-high-accuracy-1`(default) and `cloud-low-latency-1`,\nthe train budget must be between 20,000 and 900,000 milli node hours,\ninclusive. The default value is 216, 000 which represents one day in\nwall time.\nFor model type `mobile-low-latency-1`, `mobile-versatile-1`,\n`mobile-high-accuracy-1`, `mobile-core-ml-low-latency-1`,\n`mobile-core-ml-versatile-1`, `mobile-core-ml-high-accuracy-1`, the train\nbudget must be between 1,000 and 100,000 milli node hours, inclusive.\nThe default value is 24, 000 which represents one day in wall time.\n\n### getTrainCostMilliNodeHours\n\nOutput only. The actual train cost of creating this model, expressed in\nmilli node hours, i.e. 1,000 value in this field means 1 node hour.\n\nGuaranteed to not exceed the train budget.\n\n### setTrainCostMilliNodeHours\n\nOutput only. The actual train cost of creating this model, expressed in\nmilli node hours, i.e. 1,000 value in this field means 1 node hour.\n\nGuaranteed to not exceed the train budget."]]
