Reference documentation and code samples for the Cloud AutoML V1beta1 Client class ImageObjectDetectionModelMetadata.
Model metadata specific to image object detection.
Generated from protobuf message google.cloud.automl.v1beta1.ImageObjectDetectionModelMetadata
Namespace
Google \ Cloud \ AutoMl \ V1beta1Methods
__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: * |
↳ 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. |
↳ train_budget_milli_node_hours |
int|string
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_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
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
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.
Guaranteed to not exceed the train budget.
Parameter | |
---|---|
Name | Description |
var |
int|string
|
Returns | |
---|---|
Type | Description |
$this |