google.cloud.automl.v1.image.proto Maven / Gradle / Ivy
Go to download
Show more of this group Show more artifacts with this name
Show all versions of proto-google-cloud-automl-v1 Show documentation
Show all versions of proto-google-cloud-automl-v1 Show documentation
PROTO library for proto-google-cloud-automl-v1
// Copyright 2024 Google LLC
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
syntax = "proto3";
package google.cloud.automl.v1;
import "google/api/field_behavior.proto";
import "google/cloud/automl/v1/classification.proto";
option csharp_namespace = "Google.Cloud.AutoML.V1";
option go_package = "cloud.google.com/go/automl/apiv1/automlpb;automlpb";
option java_multiple_files = true;
option java_outer_classname = "ImageProto";
option java_package = "com.google.cloud.automl.v1";
option php_namespace = "Google\\Cloud\\AutoMl\\V1";
option ruby_package = "Google::Cloud::AutoML::V1";
// Dataset metadata that is specific to image classification.
message ImageClassificationDatasetMetadata {
// Required. Type of the classification problem.
ClassificationType classification_type = 1 [(google.api.field_behavior) = REQUIRED];
}
// Dataset metadata specific to image object detection.
message ImageObjectDetectionDatasetMetadata {
}
// Model metadata for image classification.
message ImageClassificationModelMetadata {
// Optional. The ID of the `base` model. If it is specified, the new model
// will be created based on the `base` model. Otherwise, the new model will be
// created from scratch. The `base` model must be in the same
// `project` and `location` as the new model to create, and have the same
// `model_type`.
string base_model_id = 1 [(google.api.field_behavior) = OPTIONAL];
// 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`(default), the train budget must be between 8,000
// and 800,000 milli node hours, inclusive. The default value is 192, 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 = 16 [(google.api.field_behavior) = OPTIONAL];
// 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 = 17 [(google.api.field_behavior) = OUTPUT_ONLY];
// Output only. The reason that this create model operation stopped,
// e.g. `BUDGET_REACHED`, `MODEL_CONVERGED`.
string stop_reason = 5 [(google.api.field_behavior) = OUTPUT_ONLY];
// Optional. Type of the model. The available values are:
// * `cloud` - Model to be used via prediction calls to AutoML API.
// This is the default value.
// * `mobile-low-latency-1` - A model that, in addition to providing
// prediction via AutoML API, can also be exported (see
// [AutoMl.ExportModel][google.cloud.automl.v1.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][google.cloud.automl.v1.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][google.cloud.automl.v1.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.
// * `mobile-core-ml-low-latency-1` - A model that, in addition to providing
// prediction via AutoML API, can also be exported (see
// [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile device with Core
// ML afterwards. Expected to have low latency, but may have
// lower prediction quality than other models.
// * `mobile-core-ml-versatile-1` - A model that, in addition to providing
// prediction via AutoML API, can also be exported (see
// [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile device with Core
// ML afterwards.
// * `mobile-core-ml-high-accuracy-1` - A model that, in addition to
// providing prediction via AutoML API, can also be exported
// (see [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile device with
// Core ML afterwards. Expected to have a higher latency, but
// should also have a higher prediction quality than other
// models.
string model_type = 7 [(google.api.field_behavior) = OPTIONAL];
// 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 = 13 [(google.api.field_behavior) = OUTPUT_ONLY];
// 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 node_qps field.
int64 node_count = 14 [(google.api.field_behavior) = OUTPUT_ONLY];
}
// Model metadata specific to image object detection.
message ImageObjectDetectionModelMetadata {
// 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][google.cloud.automl.v1.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][google.cloud.automl.v1.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][google.cloud.automl.v1.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 [(google.api.field_behavior) = OPTIONAL];
// 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 [(google.api.field_behavior) = OUTPUT_ONLY];
// 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 [(google.api.field_behavior) = OUTPUT_ONLY];
// Output only. The reason that this create model operation stopped,
// e.g. `BUDGET_REACHED`, `MODEL_CONVERGED`.
string stop_reason = 5 [(google.api.field_behavior) = OUTPUT_ONLY];
// 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.
int64 train_budget_milli_node_hours = 6 [(google.api.field_behavior) = OPTIONAL];
// 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 [(google.api.field_behavior) = OUTPUT_ONLY];
}
// Model deployment metadata specific to Image Classification.
message ImageClassificationModelDeploymentMetadata {
// Input only. The number of nodes to deploy the model on. A node is an
// abstraction of a machine resource, which can handle online prediction QPS
// as given in the model's
// [node_qps][google.cloud.automl.v1.ImageClassificationModelMetadata.node_qps].
// Must be between 1 and 100, inclusive on both ends.
int64 node_count = 1 [(google.api.field_behavior) = INPUT_ONLY];
}
// Model deployment metadata specific to Image Object Detection.
message ImageObjectDetectionModelDeploymentMetadata {
// Input only. The number of nodes to deploy the model on. A node is an
// abstraction of a machine resource, which can handle online prediction QPS
// as given in the model's
// [qps_per_node][google.cloud.automl.v1.ImageObjectDetectionModelMetadata.qps_per_node].
// Must be between 1 and 100, inclusive on both ends.
int64 node_count = 1 [(google.api.field_behavior) = INPUT_ONLY];
}