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.
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) unique Model identifierproject_id
- (Optional) The ID of the project for which to list the objects.state
- (Optional) A filter to return only resources their lifecycleState matches 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
- Average precision of the trained modelcompartment_id
- Compartment Identifierconfidence_threshold
- Confidence ratio of the calculationdefined_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 renamedfreeform_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 creationis_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 modelmodel_type
- Type of the Model.model_version
- The version of the modelprecision
- Precision of the trained modelproject_id
- The OCID of the project to associate with the model.recall
- Recall of the trained modelstate
- 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 Imagestesting_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
- 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 stringtime_updated
- The time the Model was updated. An RFC3339 formatted datetime stringtotal_image_count
- Total number of training Imagestrained_duration_in_hours
- Total hours actually used for trainingtraining_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
- 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
- The namespace name of the ObjectStorage bucket that contains the input data file.object
- The object name of the input data file.