aws_lookoutvision
This is the Amazon Lookout for Vision API Reference.
It provides descriptions of actions, data types, common parameters, and common errors.
Amazon Lookout for Vision enables you to find visual defects in industrial products, accurately and at scale. It uses computer vision to identify missing components in an industrial product, damage to vehicles or structures, irregularities in production lines, and even minuscule defects in silicon wafers — or any other physical item where quality is important such as a missing capacitor on printed circuit boards.Summary
Functions
-
create_dataset(Client, ProjectName, Input)
Creates a new dataset in an Amazon Lookout for Vision project.
- create_dataset(Client, ProjectName, Input0, Options0)
-
create_model(Client, ProjectName, Input)
Creates a new version of a model within an an Amazon Lookout for Vision project.
- create_model(Client, ProjectName, Input0, Options0)
-
create_project(Client, Input)
Creates an empty Amazon Lookout for Vision project.
- create_project(Client, Input0, Options0)
-
delete_dataset(Client, DatasetType, ProjectName, Input)
Deletes an existing Amazon Lookout for Vision
dataset
. - delete_dataset(Client, DatasetType, ProjectName, Input0, Options0)
-
delete_model(Client, ModelVersion, ProjectName, Input)
Deletes an Amazon Lookout for Vision model.
- delete_model(Client, ModelVersion, ProjectName, Input0, Options0)
-
delete_project(Client, ProjectName, Input)
Deletes an Amazon Lookout for Vision project.
- delete_project(Client, ProjectName, Input0, Options0)
-
describe_dataset(Client, DatasetType, ProjectName)
Describe an Amazon Lookout for Vision dataset.
- describe_dataset(Client, DatasetType, ProjectName, QueryMap, HeadersMap)
- describe_dataset(Client, DatasetType, ProjectName, QueryMap, HeadersMap, Options0)
-
describe_model(Client, ModelVersion, ProjectName)
Describes a version of an Amazon Lookout for Vision model.
- describe_model(Client, ModelVersion, ProjectName, QueryMap, HeadersMap)
- describe_model(Client, ModelVersion, ProjectName, QueryMap, HeadersMap, Options0)
-
describe_project(Client, ProjectName)
Describes an Amazon Lookout for Vision project.
- describe_project(Client, ProjectName, QueryMap, HeadersMap)
- describe_project(Client, ProjectName, QueryMap, HeadersMap, Options0)
-
detect_anomalies(Client, ModelVersion, ProjectName, Input)
Detects anomalies in an image that you supply.
- detect_anomalies(Client, ModelVersion, ProjectName, Input0, Options0)
-
list_dataset_entries(Client, DatasetType, ProjectName)
Lists the JSON Lines within a dataset.
- list_dataset_entries(Client, DatasetType, ProjectName, QueryMap, HeadersMap)
- list_dataset_entries(Client, DatasetType, ProjectName, QueryMap, HeadersMap, Options0)
-
list_models(Client, ProjectName)
Lists the versions of a model in an Amazon Lookout for Vision project.
- list_models(Client, ProjectName, QueryMap, HeadersMap)
- list_models(Client, ProjectName, QueryMap, HeadersMap, Options0)
-
list_projects(Client)
Lists the Amazon Lookout for Vision projects in your AWS account.
- list_projects(Client, QueryMap, HeadersMap)
- list_projects(Client, QueryMap, HeadersMap, Options0)
-
list_tags_for_resource(Client, ResourceArn)
Returns a list of tags attached to the specified Amazon Lookout for Vision model.
- list_tags_for_resource(Client, ResourceArn, QueryMap, HeadersMap)
- list_tags_for_resource(Client, ResourceArn, QueryMap, HeadersMap, Options0)
-
start_model(Client, ModelVersion, ProjectName, Input)
Starts the running of the version of an Amazon Lookout for Vision model.
- start_model(Client, ModelVersion, ProjectName, Input0, Options0)
-
stop_model(Client, ModelVersion, ProjectName, Input)
Stops the hosting of a running model.
- stop_model(Client, ModelVersion, ProjectName, Input0, Options0)
-
tag_resource(Client, ResourceArn, Input)
Adds one or more key-value tags to an Amazon Lookout for Vision model.
- tag_resource(Client, ResourceArn, Input0, Options0)
-
untag_resource(Client, ResourceArn, Input)
Removes one or more tags from an Amazon Lookout for Vision model.
- untag_resource(Client, ResourceArn, Input0, Options0)
-
update_dataset_entries(Client, DatasetType, ProjectName, Input)
Adds one or more JSON Line entries to a dataset.
- update_dataset_entries(Client, DatasetType, ProjectName, Input0, Options0)
Functions
create_dataset(Client, ProjectName, Input)
Creates a new dataset in an Amazon Lookout for Vision project.
CreateDataset
can create a training or a test dataset from a valid
dataset source (DatasetSource
).
If you want a single dataset project, specify train
for the value of
DatasetType
.
To have a project with separate training and test datasets, call
CreateDataset
twice. On the first call, specify train
for the value of
DatasetType
. On the second call, specify test
for the value of
DatasetType
.
lookoutvision:CreateDataset
operation.
create_dataset(Client, ProjectName, Input0, Options0)
create_model(Client, ProjectName, Input)
Creates a new version of a model within an an Amazon Lookout for Vision project.
CreateModel
is an asynchronous operation in which Amazon Lookout for
Vision trains, tests, and evaluates a new version of a model.
To get the current status, check the Status
field returned in the
response from DescribeModel
.
If the project has a single dataset, Amazon Lookout for Vision internally splits the dataset to create a training and a test dataset. If the project has a training and a test dataset, Lookout for Vision uses the respective datasets to train and test the model.
After training completes, the evaluation metrics are stored at the
location specified in OutputConfig
.
lookoutvision:CreateModel
operation. If you want to tag your model, you
also require permission to the lookoutvision:TagResource
operation.
create_model(Client, ProjectName, Input0, Options0)
create_project(Client, Input)
Creates an empty Amazon Lookout for Vision project.
After you create the project, add a dataset by calling CreateDataset
.
lookoutvision:CreateProject
operation.
create_project(Client, Input0, Options0)
delete_dataset(Client, DatasetType, ProjectName, Input)
Deletes an existing Amazon Lookout for Vision dataset
.
If your the project has a single dataset, you must create a new dataset before you can create a model.
If you project has a training dataset and a test dataset consider the following.
If you delete the test dataset, your project reverts to a single dataset project. If you then train the model, Amazon Lookout for Vision internally splits the remaining dataset into a training and test dataset.
If you delete the training dataset, you must create a training dataset before you can create a model.
lookoutvision:DeleteDataset
operation.
delete_dataset(Client, DatasetType, ProjectName, Input0, Options0)
delete_model(Client, ModelVersion, ProjectName, Input)
Deletes an Amazon Lookout for Vision model.
You can't delete a running model. To stop a running model, use the
StopModel
operation.
It might take a few seconds to delete a model. To determine if a model has
been deleted, call ListProjects
and check if the version of the model
(ModelVersion
) is in the Models
array.
lookoutvision:DeleteModel
operation.
delete_model(Client, ModelVersion, ProjectName, Input0, Options0)
delete_project(Client, ProjectName, Input)
Deletes an Amazon Lookout for Vision project.
To delete a project, you must first delete each version of the model
associated with the project. To delete a model use the DeleteModel
operation.
You also have to delete the dataset(s) associated with the model. For more
information, see DeleteDataset
. The images referenced by the training
and test datasets aren't deleted.
lookoutvision:DeleteProject
operation.
delete_project(Client, ProjectName, Input0, Options0)
describe_dataset(Client, DatasetType, ProjectName)
Describe an Amazon Lookout for Vision dataset.
This operation requires permissions to perform thelookoutvision:DescribeDataset
operation.
describe_dataset(Client, DatasetType, ProjectName, QueryMap, HeadersMap)
describe_dataset(Client, DatasetType, ProjectName, QueryMap, HeadersMap, Options0)
describe_model(Client, ModelVersion, ProjectName)
Describes a version of an Amazon Lookout for Vision model.
This operation requires permissions to perform thelookoutvision:DescribeModel
operation.
describe_model(Client, ModelVersion, ProjectName, QueryMap, HeadersMap)
describe_model(Client, ModelVersion, ProjectName, QueryMap, HeadersMap, Options0)
describe_project(Client, ProjectName)
Describes an Amazon Lookout for Vision project.
This operation requires permissions to perform thelookoutvision:DescribeProject
operation.
describe_project(Client, ProjectName, QueryMap, HeadersMap)
describe_project(Client, ProjectName, QueryMap, HeadersMap, Options0)
detect_anomalies(Client, ModelVersion, ProjectName, Input)
Detects anomalies in an image that you supply.
The response from DetectAnomalies
includes a boolean prediction that the
image contains one or more anomalies and a confidence value for the
prediction.
Before calling DetectAnomalies
, you must first start your model with the
StartModel
operation. You are charged for the amount of time, in
minutes, that a model runs and for the number of anomaly detection units
that your model uses. If you are not using a model, use the StopModel
operation to stop your model.
lookoutvision:DetectAnomalies
operation.
detect_anomalies(Client, ModelVersion, ProjectName, Input0, Options0)
list_dataset_entries(Client, DatasetType, ProjectName)
Lists the JSON Lines within a dataset.
An Amazon Lookout for Vision JSON Line contains the anomaly information for a single image, including the image location and the assigned label.
This operation requires permissions to perform thelookoutvision:ListDatasetEntries
operation.
list_dataset_entries(Client, DatasetType, ProjectName, QueryMap, HeadersMap)
list_dataset_entries(Client, DatasetType, ProjectName, QueryMap, HeadersMap, Options0)
list_models(Client, ProjectName)
Lists the versions of a model in an Amazon Lookout for Vision project.
This operation requires permissions to perform thelookoutvision:ListModels
operation.
list_models(Client, ProjectName, QueryMap, HeadersMap)
list_models(Client, ProjectName, QueryMap, HeadersMap, Options0)
list_projects(Client)
Lists the Amazon Lookout for Vision projects in your AWS account.
This operation requires permissions to perform thelookoutvision:ListProjects
operation.
list_projects(Client, QueryMap, HeadersMap)
list_projects(Client, QueryMap, HeadersMap, Options0)
list_tags_for_resource(Client, ResourceArn)
Returns a list of tags attached to the specified Amazon Lookout for Vision model.
This operation requires permissions to perform thelookoutvision:ListTagsForResource
operation.
list_tags_for_resource(Client, ResourceArn, QueryMap, HeadersMap)
list_tags_for_resource(Client, ResourceArn, QueryMap, HeadersMap, Options0)
start_model(Client, ModelVersion, ProjectName, Input)
Starts the running of the version of an Amazon Lookout for Vision model.
Starting a model takes a while to complete. To check the current state of
the model, use DescribeModel
.
A model is ready to use when its status is HOSTED
.
Once the model is running, you can detect custom labels in new images by
calling DetectAnomalies
.
You are charged for the amount of time that the model is running. To stop
a running model, call StopModel
.
lookoutvision:StartModel
operation.
start_model(Client, ModelVersion, ProjectName, Input0, Options0)
stop_model(Client, ModelVersion, ProjectName, Input)
Stops the hosting of a running model.
The operation might take a while to complete. To check the current status,
call DescribeModel
.
After the model hosting stops, the Status
of the model is TRAINED
.
lookoutvision:StopModel
operation.
stop_model(Client, ModelVersion, ProjectName, Input0, Options0)
tag_resource(Client, ResourceArn, Input)
Adds one or more key-value tags to an Amazon Lookout for Vision model.
For more information, see Tagging a model in the Amazon Lookout for Vision Developer Guide.
This operation requires permissions to perform thelookoutvision:TagResource
operation.
tag_resource(Client, ResourceArn, Input0, Options0)
untag_resource(Client, ResourceArn, Input)
Removes one or more tags from an Amazon Lookout for Vision model.
For more information, see Tagging a model in the Amazon Lookout for Vision Developer Guide.
This operation requires permissions to perform thelookoutvision:UntagResource
operation.
untag_resource(Client, ResourceArn, Input0, Options0)
update_dataset_entries(Client, DatasetType, ProjectName, Input)
Adds one or more JSON Line entries to a dataset.
A JSON Line includes information about an image used for training or testing an Amazon Lookout for Vision model. The following is an example JSON Line.
Updating a dataset might take a while to complete. To check the current
status, call DescribeDataset
and check the Status
field in the
response.
lookoutvision:UpdateDatasetEntries
operation.