Data Source: oci_ai_vision_model

This data source provides details about a specific Model resource in Oracle Cloud Infrastructure Ai Vision service.

Gets a Model by identifier

Example Usage

data "oci_ai_vision_model" "test_model" {
	#Required
	model_id = oci_ai_vision_model.test_model.id
}

Argument Reference

The following arguments are supported:

• model_id - (Required) unique Model identifier

Attributes Reference

The following attributes are exported:

• average_precision - Average precision of the trained model
• compartment_id - Compartment Identifier
• confidence_threshold - Confidence ratio of the calculation
• defined_tags - Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
• description - A short description of the model.
• display_name - Model Identifier, can be renamed
• freeform_tags - Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
• id - Unique identifier that is immutable on creation
• is_quick_mode - If It’s true, Training is set for recommended epochs needed for quick training.
• lifecycle_details - A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
• max_training_duration_in_hours - The maximum duration in hours for which the training will run.
• metrics - Complete Training Metrics for successful trained model
• model_type - Type of the Model.
• model_version - The version of the model
• precision - Precision of the trained model
• project_id - The OCID of the project to associate with the model.
• recall - Recall of the trained model
• state - The current state of the Model.
• system_tags - Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
• test_image_count - Total number of testing Images
• testing_dataset - The base entity for a Dataset, which is the input for Model creation.
  • bucket - The name of the ObjectStorage bucket that contains the input data file.
  • dataset_id - The OCID of the Data Science Labeling Dataset.
  • dataset_type - Type of the Dataset.
  • namespace_name - The namespace name of the ObjectStorage bucket that contains the input data file.
  • object - The object name of the input data file.
• time_created - The time the Model was created. An RFC3339 formatted datetime string
• time_updated - The time the Model was updated. An RFC3339 formatted datetime string
• total_image_count - Total number of training Images
• trained_duration_in_hours - Total hours actually used for training
• training_dataset - The base entity for a Dataset, which is the input for Model creation.
  • bucket - The name of the ObjectStorage bucket that contains the input data file.
  • dataset_id - The OCID of the Data Science Labeling Dataset.
  • dataset_type - Type of the Dataset.
  • namespace_name - The namespace name of the ObjectStorage bucket that contains the input data file.
  • object - The object name of the input data file.
• validation_dataset - The base entity for a Dataset, which is the input for Model creation.
  • bucket - The name of the ObjectStorage bucket that contains the input data file.
  • dataset_id - The OCID of the Data Science Labeling Dataset.
  • dataset_type - Type of the Dataset.
  • namespace_name - The namespace name of the ObjectStorage bucket that contains the input data file.
  • object - The object name of the input data file.