Oracle Cloud Infrastructure v2.33.0, May 1 25

Oracle Cloud Infrastructure v2.33.0 published on Thursday, May 1, 2025 by Pulumi

oci.AiAnomalyDetection.getDetectionModel

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Oracle Cloud Infrastructure v2.33.0 published on Thursday, May 1, 2025 by Pulumi

Example Usage

import * as pulumi from "@pulumi/pulumi";
import * as oci from "@pulumi/oci";

const testModel = oci.AiAnomalyDetection.getDetectionModel({
    modelId: testModelOciAiAnomalyDetectionModel.id,
});

import pulumi
import pulumi_oci as oci

test_model = oci.AiAnomalyDetection.get_detection_model(model_id=test_model_oci_ai_anomaly_detection_model["id"])

package main

import (
	"github.com/pulumi/pulumi-oci/sdk/v2/go/oci/aianomalydetection"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := aianomalydetection.GetDetectionModel(ctx, &aianomalydetection.GetDetectionModelArgs{
			ModelId: testModelOciAiAnomalyDetectionModel.Id,
		}, nil)
		if err != nil {
			return err
		}
		return nil
	})
}

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Oci = Pulumi.Oci;

return await Deployment.RunAsync(() => 
{
    var testModel = Oci.AiAnomalyDetection.GetDetectionModel.Invoke(new()
    {
        ModelId = testModelOciAiAnomalyDetectionModel.Id,
    });

});

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.oci.AiAnomalyDetection.AiAnomalyDetectionFunctions;
import com.pulumi.oci.AiAnomalyDetection.inputs.GetDetectionModelArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        final var testModel = AiAnomalyDetectionFunctions.getDetectionModel(GetDetectionModelArgs.builder()
            .modelId(testModelOciAiAnomalyDetectionModel.id())
            .build());

    }
}

variables:
  testModel:
    fn::invoke:
      function: oci:AiAnomalyDetection:getDetectionModel
      arguments:
        modelId: ${testModelOciAiAnomalyDetectionModel.id}

Using getDetectionModel

Two invocation forms are available. The direct form accepts plain arguments and either blocks until the result value is available, or returns a Promise-wrapped result. The output form accepts Input-wrapped arguments and returns an Output-wrapped result.

function getDetectionModel(args: GetDetectionModelArgs, opts?: InvokeOptions): Promise<GetDetectionModelResult>
function getDetectionModelOutput(args: GetDetectionModelOutputArgs, opts?: InvokeOptions): Output<GetDetectionModelResult>

def get_detection_model(model_id: Optional[str] = None,
                        opts: Optional[InvokeOptions] = None) -> GetDetectionModelResult
def get_detection_model_output(model_id: Optional[pulumi.Input[str]] = None,
                        opts: Optional[InvokeOptions] = None) -> Output[GetDetectionModelResult]

func GetDetectionModel(ctx *Context, args *GetDetectionModelArgs, opts ...InvokeOption) (*GetDetectionModelResult, error)
func GetDetectionModelOutput(ctx *Context, args *GetDetectionModelOutputArgs, opts ...InvokeOption) GetDetectionModelResultOutput

> Note: This function is named GetDetectionModel in the Go SDK.

public static class GetDetectionModel 
{
    public static Task<GetDetectionModelResult> InvokeAsync(GetDetectionModelArgs args, InvokeOptions? opts = null)
    public static Output<GetDetectionModelResult> Invoke(GetDetectionModelInvokeArgs args, InvokeOptions? opts = null)
}

public static CompletableFuture<GetDetectionModelResult> getDetectionModel(GetDetectionModelArgs args, InvokeOptions options)
public static Output<GetDetectionModelResult> getDetectionModel(GetDetectionModelArgs args, InvokeOptions options)

fn::invoke:
  function: oci:AiAnomalyDetection/getDetectionModel:getDetectionModel
  arguments:
    # arguments dictionary

The following arguments are supported:

ModelId string: The OCID of the Model.

ModelId string: The OCID of the Model.

modelId String: The OCID of the Model.

modelId string: The OCID of the Model.

model_id str: The OCID of the Model.

modelId String: The OCID of the Model.

getDetectionModel Result

The following output properties are available:

CompartmentId string: The OCID for the model's compartment.
DefinedTags Dictionary<string, string>: Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
Description string: A short description of the Model.
DisplayName string: A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
FreeformTags Dictionary<string, string>: Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
Id string: The OCID of the model that is immutable on creation.
LifecycleDetails string: A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
ModelId string
ModelTrainingDetails List<GetDetectionModelModelTrainingDetail>: Specifies the details of the MSET model during the create call.
ModelTrainingResults List<GetDetectionModelModelTrainingResult>: Specifies the details for an Anomaly Detection model trained with MSET.
ProjectId string: The OCID of the project to associate with the model.
State string: The state of the model.
SystemTags Dictionary<string, string>: Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
TimeCreated string: The time the the Model was created. An RFC3339 formatted datetime string.
TimeUpdated string: The time the Model was updated. An RFC3339 formatted datetime string.

CompartmentId string: The OCID for the model's compartment.
DefinedTags map[string]string: Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
Description string: A short description of the Model.
DisplayName string: A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
FreeformTags map[string]string: Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
Id string: The OCID of the model that is immutable on creation.
LifecycleDetails string: A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
ModelId string
ModelTrainingDetails []GetDetectionModelModelTrainingDetail: Specifies the details of the MSET model during the create call.
ModelTrainingResults []GetDetectionModelModelTrainingResult: Specifies the details for an Anomaly Detection model trained with MSET.
ProjectId string: The OCID of the project to associate with the model.
State string: The state of the model.
SystemTags map[string]string: Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
TimeCreated string: The time the the Model was created. An RFC3339 formatted datetime string.
TimeUpdated string: The time the Model was updated. An RFC3339 formatted datetime string.

compartmentId String: The OCID for the model's compartment.
definedTags Map<String,String>: Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
description String: A short description of the Model.
displayName String: A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
freeformTags Map<String,String>: Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
id String: The OCID of the model that is immutable on creation.
lifecycleDetails String: A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
modelId String
modelTrainingDetails List<GetDetectionModelModelTrainingDetail>: Specifies the details of the MSET model during the create call.
modelTrainingResults List<GetDetectionModelModelTrainingResult>: Specifies the details for an Anomaly Detection model trained with MSET.
projectId String: The OCID of the project to associate with the model.
state String: The state of the model.
systemTags Map<String,String>: Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
timeCreated String: The time the the Model was created. An RFC3339 formatted datetime string.
timeUpdated String: The time the Model was updated. An RFC3339 formatted datetime string.

compartmentId string: The OCID for the model's compartment.
definedTags {[key: string]: string}: Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
description string: A short description of the Model.
displayName string: A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
freeformTags {[key: string]: string}: Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
id string: The OCID of the model that is immutable on creation.
lifecycleDetails string: A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
modelId string
modelTrainingDetails GetDetectionModelModelTrainingDetail[]: Specifies the details of the MSET model during the create call.
modelTrainingResults GetDetectionModelModelTrainingResult[]: Specifies the details for an Anomaly Detection model trained with MSET.
projectId string: The OCID of the project to associate with the model.
state string: The state of the model.
systemTags {[key: string]: string}: Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
timeCreated string: The time the the Model was created. An RFC3339 formatted datetime string.
timeUpdated string: The time the Model was updated. An RFC3339 formatted datetime string.

compartment_id str: The OCID for the model's compartment.
defined_tags Mapping[str, str]: Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
description str: A short description of the Model.
display_name str: A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
freeform_tags Mapping[str, str]: Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
id str: The OCID of the model that is immutable on creation.
lifecycle_details str: A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
model_id str
model_training_details Sequence[GetDetectionModelModelTrainingDetail]: Specifies the details of the MSET model during the create call.
model_training_results Sequence[GetDetectionModelModelTrainingResult]: Specifies the details for an Anomaly Detection model trained with MSET.
project_id str: The OCID of the project to associate with the model.
state str: The state of the model.
system_tags Mapping[str, str]: Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
time_created str: The time the the Model was created. An RFC3339 formatted datetime string.
time_updated str: The time the Model was updated. An RFC3339 formatted datetime string.

compartmentId String: The OCID for the model's compartment.
definedTags Map<String>: Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
description String: A short description of the Model.
displayName String: A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
freeformTags Map<String>: Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
id String: The OCID of the model that is immutable on creation.
lifecycleDetails String: A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
modelId String
modelTrainingDetails List<Property Map>: Specifies the details of the MSET model during the create call.
modelTrainingResults List<Property Map>: Specifies the details for an Anomaly Detection model trained with MSET.
projectId String: The OCID of the project to associate with the model.
state String: The state of the model.
systemTags Map<String>: Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
timeCreated String: The time the the Model was created. An RFC3339 formatted datetime string.
timeUpdated String: The time the Model was updated. An RFC3339 formatted datetime string.

Supporting Types

GetDetectionModelModelTrainingDetail

AlgorithmHint string: User can choose specific algorithm for training.
DataAssetIds List<string>: The list of OCIDs of the data assets to train the model. The dataAssets have to be in the same project where the ai model would reside.
TargetFap double: A target model accuracy metric user provides as their requirement
TrainingFraction double: Fraction of total data that is used for training the model. The remaining is used for validation of the model.
WindowSize int: Window size defined during training or deduced by the algorithm.

AlgorithmHint string: User can choose specific algorithm for training.
DataAssetIds []string: The list of OCIDs of the data assets to train the model. The dataAssets have to be in the same project where the ai model would reside.
TargetFap float64: A target model accuracy metric user provides as their requirement
TrainingFraction float64: Fraction of total data that is used for training the model. The remaining is used for validation of the model.
WindowSize int: Window size defined during training or deduced by the algorithm.

algorithmHint String: User can choose specific algorithm for training.
dataAssetIds List<String>: The list of OCIDs of the data assets to train the model. The dataAssets have to be in the same project where the ai model would reside.
targetFap Double: A target model accuracy metric user provides as their requirement
trainingFraction Double: Fraction of total data that is used for training the model. The remaining is used for validation of the model.
windowSize Integer: Window size defined during training or deduced by the algorithm.

algorithmHint string: User can choose specific algorithm for training.
dataAssetIds string[]: The list of OCIDs of the data assets to train the model. The dataAssets have to be in the same project where the ai model would reside.
targetFap number: A target model accuracy metric user provides as their requirement
trainingFraction number: Fraction of total data that is used for training the model. The remaining is used for validation of the model.
windowSize number: Window size defined during training or deduced by the algorithm.

algorithm_hint str: User can choose specific algorithm for training.
data_asset_ids Sequence[str]: The list of OCIDs of the data assets to train the model. The dataAssets have to be in the same project where the ai model would reside.
target_fap float: A target model accuracy metric user provides as their requirement
training_fraction float: Fraction of total data that is used for training the model. The remaining is used for validation of the model.
window_size int: Window size defined during training or deduced by the algorithm.

algorithmHint String: User can choose specific algorithm for training.
dataAssetIds List<String>: The list of OCIDs of the data assets to train the model. The dataAssets have to be in the same project where the ai model would reside.
targetFap Number: A target model accuracy metric user provides as their requirement
trainingFraction Number: Fraction of total data that is used for training the model. The remaining is used for validation of the model.
windowSize Number: Window size defined during training or deduced by the algorithm.

GetDetectionModelModelTrainingResult

Fap double: Accuracy metric for a signal.
IsTrainingGoalAchieved bool: A boolean value to indicate if train goal/targetFap is achieved for trained model
Mae double
MaxInferenceSyncRows int
MultivariateFap double: The model accuracy metric on timestamp level.
Rmse double
RowReductionDetails List<GetDetectionModelModelTrainingResultRowReductionDetail>: Information regarding how/what row reduction methods will be applied. If this property is not present or is null, then it means row reduction is not applied.
SignalDetails List<GetDetectionModelModelTrainingResultSignalDetail>: The list of signal details.
Warning string: A warning message to explain the reason when targetFap cannot be achieved for trained model
WindowSize int: Window size defined during training or deduced by the algorithm.

Fap float64: Accuracy metric for a signal.
IsTrainingGoalAchieved bool: A boolean value to indicate if train goal/targetFap is achieved for trained model
Mae float64
MaxInferenceSyncRows int
MultivariateFap float64: The model accuracy metric on timestamp level.
Rmse float64
RowReductionDetails []GetDetectionModelModelTrainingResultRowReductionDetail: Information regarding how/what row reduction methods will be applied. If this property is not present or is null, then it means row reduction is not applied.
SignalDetails []GetDetectionModelModelTrainingResultSignalDetail: The list of signal details.
Warning string: A warning message to explain the reason when targetFap cannot be achieved for trained model
WindowSize int: Window size defined during training or deduced by the algorithm.

fap Double: Accuracy metric for a signal.
isTrainingGoalAchieved Boolean: A boolean value to indicate if train goal/targetFap is achieved for trained model
mae Double
maxInferenceSyncRows Integer
multivariateFap Double: The model accuracy metric on timestamp level.
rmse Double
rowReductionDetails List<GetDetectionModelModelTrainingResultRowReductionDetail>: Information regarding how/what row reduction methods will be applied. If this property is not present or is null, then it means row reduction is not applied.
signalDetails List<GetDetectionModelModelTrainingResultSignalDetail>: The list of signal details.
warning String: A warning message to explain the reason when targetFap cannot be achieved for trained model
windowSize Integer: Window size defined during training or deduced by the algorithm.

fap number: Accuracy metric for a signal.
isTrainingGoalAchieved boolean: A boolean value to indicate if train goal/targetFap is achieved for trained model
mae number
maxInferenceSyncRows number
multivariateFap number: The model accuracy metric on timestamp level.
rmse number
rowReductionDetails GetDetectionModelModelTrainingResultRowReductionDetail[]: Information regarding how/what row reduction methods will be applied. If this property is not present or is null, then it means row reduction is not applied.
signalDetails GetDetectionModelModelTrainingResultSignalDetail[]: The list of signal details.
warning string: A warning message to explain the reason when targetFap cannot be achieved for trained model
windowSize number: Window size defined during training or deduced by the algorithm.

fap float: Accuracy metric for a signal.
is_training_goal_achieved bool: A boolean value to indicate if train goal/targetFap is achieved for trained model
mae float
max_inference_sync_rows int
multivariate_fap float: The model accuracy metric on timestamp level.
rmse float
row_reduction_details Sequence[GetDetectionModelModelTrainingResultRowReductionDetail]: Information regarding how/what row reduction methods will be applied. If this property is not present or is null, then it means row reduction is not applied.
signal_details Sequence[GetDetectionModelModelTrainingResultSignalDetail]: The list of signal details.
warning str: A warning message to explain the reason when targetFap cannot be achieved for trained model
window_size int: Window size defined during training or deduced by the algorithm.

fap Number: Accuracy metric for a signal.
isTrainingGoalAchieved Boolean: A boolean value to indicate if train goal/targetFap is achieved for trained model
mae Number
maxInferenceSyncRows Number
multivariateFap Number: The model accuracy metric on timestamp level.
rmse Number
rowReductionDetails List<Property Map>: Information regarding how/what row reduction methods will be applied. If this property is not present or is null, then it means row reduction is not applied.
signalDetails List<Property Map>: The list of signal details.
warning String: A warning message to explain the reason when targetFap cannot be achieved for trained model
windowSize Number: Window size defined during training or deduced by the algorithm.

GetDetectionModelModelTrainingResultRowReductionDetail

IsReductionEnabled bool

A boolean value to indicate if row reduction is applied

ReductionMethod string

Method for row reduction:

DELETE_ROW - delete rows with equal intervals
AVERAGE_ROW - average multiple rows to one row

ReductionPercentage double

A percentage to reduce data size down to on top of original data

IsReductionEnabled bool

A boolean value to indicate if row reduction is applied

ReductionMethod string

Method for row reduction:

DELETE_ROW - delete rows with equal intervals
AVERAGE_ROW - average multiple rows to one row

ReductionPercentage float64

A percentage to reduce data size down to on top of original data

isReductionEnabled Boolean

A boolean value to indicate if row reduction is applied

reductionMethod String

Method for row reduction:

DELETE_ROW - delete rows with equal intervals
AVERAGE_ROW - average multiple rows to one row

reductionPercentage Double

A percentage to reduce data size down to on top of original data

isReductionEnabled boolean

A boolean value to indicate if row reduction is applied

reductionMethod string

Method for row reduction:

DELETE_ROW - delete rows with equal intervals
AVERAGE_ROW - average multiple rows to one row

reductionPercentage number

A percentage to reduce data size down to on top of original data

is_reduction_enabled bool

A boolean value to indicate if row reduction is applied

reduction_method str

Method for row reduction:

DELETE_ROW - delete rows with equal intervals
AVERAGE_ROW - average multiple rows to one row

reduction_percentage float

A percentage to reduce data size down to on top of original data

isReductionEnabled Boolean

A boolean value to indicate if row reduction is applied

reductionMethod String

Method for row reduction:

DELETE_ROW - delete rows with equal intervals
AVERAGE_ROW - average multiple rows to one row

reductionPercentage Number

A percentage to reduce data size down to on top of original data

GetDetectionModelModelTrainingResultSignalDetail

Details string

detailed information for a signal.

Fap double

Accuracy metric for a signal.

IsQuantized bool

A boolean value to indicate if a signal is quantized or not.

Max double

Max value within a signal.

Min double

Min value within a signal.

MviRatio double

The ratio of missing values in a signal filled/imputed by the IDP algorithm.

SignalName string

The name of a signal.

Status string

Status of the signal:

ACCEPTED - the signal is used for training the model
DROPPED - the signal does not meet requirement, and is dropped before training the model.
OTHER - placeholder for other status

Std double

Standard deviation of values within a signal.

Details string

detailed information for a signal.

Fap float64

Accuracy metric for a signal.

IsQuantized bool

A boolean value to indicate if a signal is quantized or not.

Max float64

Max value within a signal.

Min float64

Min value within a signal.

MviRatio float64

The ratio of missing values in a signal filled/imputed by the IDP algorithm.

SignalName string

The name of a signal.

Status string

Status of the signal:

ACCEPTED - the signal is used for training the model
DROPPED - the signal does not meet requirement, and is dropped before training the model.
OTHER - placeholder for other status

Std float64

Standard deviation of values within a signal.

details String

detailed information for a signal.

fap Double

Accuracy metric for a signal.

isQuantized Boolean

A boolean value to indicate if a signal is quantized or not.

max Double

Max value within a signal.

min Double

Min value within a signal.

mviRatio Double

The ratio of missing values in a signal filled/imputed by the IDP algorithm.

signalName String

The name of a signal.

status String

Status of the signal:

ACCEPTED - the signal is used for training the model
DROPPED - the signal does not meet requirement, and is dropped before training the model.
OTHER - placeholder for other status

std Double

Standard deviation of values within a signal.

details string

detailed information for a signal.

fap number

Accuracy metric for a signal.

isQuantized boolean

A boolean value to indicate if a signal is quantized or not.

max number

Max value within a signal.

min number

Min value within a signal.

mviRatio number

The ratio of missing values in a signal filled/imputed by the IDP algorithm.

signalName string

The name of a signal.

status string

Status of the signal:

ACCEPTED - the signal is used for training the model
DROPPED - the signal does not meet requirement, and is dropped before training the model.
OTHER - placeholder for other status

std number

Standard deviation of values within a signal.

details str

detailed information for a signal.

fap float

Accuracy metric for a signal.

is_quantized bool

A boolean value to indicate if a signal is quantized or not.

max float

Max value within a signal.

min float

Min value within a signal.

mvi_ratio float

The ratio of missing values in a signal filled/imputed by the IDP algorithm.

signal_name str

The name of a signal.

status str

Status of the signal:

ACCEPTED - the signal is used for training the model
DROPPED - the signal does not meet requirement, and is dropped before training the model.
OTHER - placeholder for other status

std float

Standard deviation of values within a signal.

details String

detailed information for a signal.

fap Number

Accuracy metric for a signal.

isQuantized Boolean

A boolean value to indicate if a signal is quantized or not.

max Number

Max value within a signal.

min Number

Min value within a signal.

mviRatio Number

The ratio of missing values in a signal filled/imputed by the IDP algorithm.

signalName String

The name of a signal.

status String

Status of the signal:

ACCEPTED - the signal is used for training the model
DROPPED - the signal does not meet requirement, and is dropped before training the model.
OTHER - placeholder for other status

std Number

Standard deviation of values within a signal.

Package Details

Repository: oci pulumi/pulumi-oci
License: Apache-2.0
Notes: This Pulumi package is based on the oci Terraform Provider.

Oracle Cloud Infrastructure v2.33.0 published on Thursday, May 1, 2025 by Pulumi

pulumi/pulumi-oci

oci.AiAnomalyDetection.getDetectionModel

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Example Usage

Using getDetectionModel

getDetectionModel Result

Supporting Types

GetDetectionModelModelTrainingDetail

GetDetectionModelModelTrainingResult

GetDetectionModelModelTrainingResultRowReductionDetail

GetDetectionModelModelTrainingResultSignalDetail

Package Details

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On this page