Cloud AutoML V1 Client - Class ImageObjectDetectionModelMetadata (1.5.4)

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

Constructor.

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.

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.

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.

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.

Output only. An approximate number of online prediction QPS that can be supported by this model per each node on which it is deployed.

Output only. An approximate number of online prediction QPS that can be supported by this model per each node on which it is deployed.

Output only. The reason that this create model operation stopped, e.g. BUDGET_REACHED , MODEL_CONVERGED .

Output only. The reason that this create model operation stopped, e.g. BUDGET_REACHED , MODEL_CONVERGED .

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.

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.

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.

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.