Reference documentation and code samples for the Cloud AutoML V1beta1 API class Google::Cloud::AutoML::V1beta1::ImageObjectDetectionModelMetadata.
Model metadata specific to image object detection.
Inherits
- Object
Extended By
- Google::Protobuf::MessageExts::ClassMethods
Includes
- Google::Protobuf::MessageExts
Methods
#model_type
def model_type() -> ::String
Returns
-
(::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.
-
#model_type=
def model_type=(value) -> ::String
Parameter
-
value (::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.
-
Returns
-
(::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
def node_count() -> ::Integer
Returns
- (::Integer) — 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_count=
def node_count=(value) -> ::Integer
Parameter
- value (::Integer) — 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
- (::Integer) — 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
def node_qps() -> ::Float
Returns
- (::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.
#node_qps=
def node_qps=(value) -> ::Float
Parameter
- value (::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.
Returns
- (::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
def stop_reason() -> ::String
Returns
-
(::String) — Output only. The reason that this create model operation stopped,
e.g.
BUDGET_REACHED
,MODEL_CONVERGED
.
#stop_reason=
def stop_reason=(value) -> ::String
Parameter
-
value (::String) — Output only. The reason that this create model operation stopped,
e.g.
BUDGET_REACHED
,MODEL_CONVERGED
.
Returns
-
(::String) — Output only. The reason that this create model operation stopped,
e.g.
BUDGET_REACHED
,MODEL_CONVERGED
.
#train_budget_milli_node_hours
def train_budget_milli_node_hours() -> ::Integer
Returns
-
(::Integer) — 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 beMODEL_CONVERGED
. Note, node_hour = actual_hour * number_of_nodes_invovled. For model typecloud-high-accuracy-1
(default) andcloud-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 typemobile-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_budget_milli_node_hours=
def train_budget_milli_node_hours=(value) -> ::Integer
Parameter
-
value (::Integer) — 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 beMODEL_CONVERGED
. Note, node_hour = actual_hour * number_of_nodes_invovled. For model typecloud-high-accuracy-1
(default) andcloud-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 typemobile-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
-
(::Integer) — 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 beMODEL_CONVERGED
. Note, node_hour = actual_hour * number_of_nodes_invovled. For model typecloud-high-accuracy-1
(default) andcloud-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 typemobile-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
def train_cost_milli_node_hours() -> ::Integer
Returns
- (::Integer) — 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.
#train_cost_milli_node_hours=
def train_cost_milli_node_hours=(value) -> ::Integer
Parameter
- value (::Integer) — 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
- (::Integer) — 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.