Data Source: oci_ai_vision_model

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

Get 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:

Attributes Reference

The following attributes are exported:

<<<<<<< ours * 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.