Data Source: oci_ai_vision_models

This data source provides the list of Models in Oracle Cloud Infrastructure Ai Vision service.

Returns a list of models in a compartment.

Example Usage

data "oci_ai_vision_models" "test_models" {

	#Optional
	compartment_id = var.compartment_id
	display_name = var.model_display_name
	id = var.model_id
	project_id = oci_ai_vision_project.test_project.id
	state = var.model_state
}

Argument Reference

The following arguments are supported:

• compartment_id - (Optional) The ID of the compartment in which to list resources.
• display_name - (Optional) A filter to return only resources that match the entire display name given.
• id - (Optional) The filter to find the model with the given identifier.
• project_id - (Optional) The ID of the project for which to list the objects.
• state - (Optional) The filter to match models with the given lifecycleState.

Attributes Reference

The following attributes are exported:

• model_collection - The list of model_collection.

Model Reference

The following attributes are exported:

• average_precision - The mean average precision of the trained model.
• compartment_id - The compartment identifier.
• confidence_threshold - The intersection over the union threshold used for calculating precision and recall.
• defined_tags - Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
• description - An optional description of the model.
• display_name - A human-friendly name for the model, which can be changed.
• freeform_tags - A simple key-value pair that is applied without any predefined name, type, or scope. It exists for cross-compatibility only. For example: {"bar-key": "value"}
• id - A unique identifier that is immutable after creation.
• is_quick_mode - Set to true when experimenting with a new model type or dataset, so model training is quick, with a predefined low number of passes through the training data.
• lifecycle_details - A message describing the current state in more detail, that can provide actionable information if training failed.
• max_training_duration_in_hours - The maximum model training duration in hours, expressed as a decimal fraction.
• metrics - The complete set of per-label metrics for successfully trained models.
• model_type - What type of Vision model this is.
• model_version - The version of the model.
• precision - The precision of the trained model.
• project_id - The OCID of the project that contains 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. For example: {"orcl-cloud": {"free-tier-retained": "true"}}
• test_image_count - The number of images set aside for evaluating model performance metrics after training.
• testing_dataset - The base entity which is the input for creating and training a model.
  • bucket - The name of the Object Storage bucket that contains the input data file.
  • dataset_id - OCID of the Data Labeling dataset.
  • dataset_type - The dataset type, based on where it is stored.
  • namespace - The namespace name of the Object Storage bucket that contains the input data file.
  • object - The object name of the input data file.
• time_created - When the model was created, as an RFC3339 datetime string.
• time_updated - When the model was updated, as an RFC3339 datetime string.
• total_image_count - The number of images in the dataset used to train, validate, and test the model.
• trained_duration_in_hours - The total hours actually used for model training.
• training_dataset - The base entity which is the input for creating and training a model.
  • bucket - The name of the Object Storage bucket that contains the input data file.
  • dataset_id - OCID of the Data Labeling dataset.
  • dataset_type - The dataset type, based on where it is stored.
  • namespace - The namespace name of the Object Storage bucket that contains the input data file.
  • object - The object name of the input data file.
• validation_dataset - The base entity which is the input for creating and training a model.
  • bucket - The name of the Object Storage bucket that contains the input data file.
  • dataset_id - OCID of the Data Labeling dataset.
  • dataset_type - The dataset type, based on where it is stored.
  • namespace - The namespace name of the Object Storage bucket that contains the input data file.
  • object - The object name of the input data file.