Central instance to access all DataLabeling related resource activities
AnnotatedDataset is a set holding annotations for data in a Dataset. Each labeling task will generate an AnnotatedDataset under the Dataset that the task is requested for.
Metadata on AnnotatedDataset.
Annotation for Example. Each example may have one or more annotations. For example in image classification problem, each image might have one or more labels. We call labels binded with this image an Annotation.
Additional information associated with the annotation.
Container of information related to one possible annotation that can be used in a labeling task. For example, an image classification task where images are labeled as dog
or cat
must reference an AnnotationSpec for dog
and an AnnotationSpec for cat
.
An AnnotationSpecSet is a collection of label definitions. For example, in image classification tasks, you define a set of possible labels for images as an AnnotationSpecSet. An AnnotationSpecSet is immutable upon creation.
Annotation spec set with the setting of allowing multi labels or not.
Annotation value for an example.
Records a failed evaluation job run.
The BigQuery location for input data. If used in an EvaluationJob, this is where the service saves the prediction input and output sampled from the model version.
Options regarding evaluation between bounding boxes.
A bounding polygon in the image.
Config for image bounding poly (and bounding box) human labeling task.
Metadata for classification annotations.
Metrics calculated for a classification model.
There is no detailed description.
Confusion matrix of the model running the classification. Only applicable when the metrics entry aggregates multiple labels. Not applicable when the entry is for a single label.
There is no detailed description.
Request message for CreateAnnotationSpecSet.
Request message for CreateDataset.
Request message for CreateEvaluationJob.
Request message for CreateInstruction.
Deprecated: this instruction format is not supported any more. Instruction from a CSV file.
DataItem is a piece of data, without annotation. For example, an image.
Dataset is the resource to hold your data. You can request multiple labeling tasks for a dataset while each one will generate an AnnotatedDataset.
Describes an evaluation between a machine learning model’s predictions and ground truth labels. Created when an EvaluationJob runs successfully.
Configuration details used for calculating evaluation metrics and creating an Evaluation.
Defines an evaluation job that runs periodically to generate Evaluations.
Creating an evaluation job is the starting point for using continuous evaluation.
Provides details for how an evaluation job sends email alerts based on the results of a run.
Configures specific details of how a continuous evaluation job works. Provide this configuration when you create an EvaluationJob.
There is no detailed description.
Config for video event human labeling task.
An Example is a piece of data and its annotation. For example, an image with label “house”.
Example comparisons comparing ground truth output and predictions for a specific input.
Request message for ExportData API.
A feedback message inside a feedback thread.
A feedback thread of a certain labeling task on a certain annotated dataset.
There is no detailed description.
Export destination of the data.Only gcs path is allowed in output_uri.
Export folder destination of the data.
Source of the Cloud Storage file to be imported.
Configuration for how human labeling task should be done.
Image bounding poly annotation. It represents a polygon including bounding box in the image.
Image classification annotation definition.
Config for image classification human labeling task.
Container of information about an image.
A polyline for the image annotation.
Image segmentation annotation.
Request message for ImportData API.
The configuration of input data, including data type, location, etc.
Instruction of how to perform the labeling task for human operators. Currently only PDF instruction is supported.
Request message for starting an image labeling task.
Statistics about annotation specs.
Request message for LabelText.
Request message for LabelVideo.
Results of listing annotated datasets for a dataset.
Results of listing annotation spec set under a project.
Results of listing data items in a dataset.
Results of listing datasets within a project.
Results for listing evaluation jobs.
Results of listing Examples in and annotated dataset.
Results for listing FeedbackMessages.
Results for listing FeedbackThreads.
Results of listing instructions under a project.
Normalized bounding polygon.
Normalized polyline.
A vertex represents a 2D point in the image. NOTE: the normalized vertex coordinates are relative to the original image and range from 0 to 1.
Config for video object detection human labeling task. Object detection will be conducted on the images extracted from the video, and those objects will be labeled with bounding boxes. User need to specify the number of images to be extracted per second as the extraction frame rate.
Metrics calculated for an image object detection (bounding box) model.
Config for video object tracking human labeling task.
Video frame level annotation for object detection and tracking.
Metadata describing the feedback from the operator.
General information useful for labels coming from contributors.
The configuration of output data.
Request message for PauseEvaluationJob.
Instruction from a PDF file.
A line with multiple line segments.
Config for image polyline human labeling task.
There is no detailed description.
Metadata describing the feedback from the labeling task requester.
Request message ResumeEvaluationJob.
A row in the confusion matrix. Each entry in this row has the same ground truth label.
Results of searching evaluations.
Request message of SearchExampleComparisons.
Results of searching example comparisons.
Config for image segmentation
Config for setting up sentiments.
Start and end position in a sequence (e.g. text segment).
Text classification annotation.
Config for text classification human labeling task.
Text entity extraction annotation.
Config for text entity extraction human labeling task.
Metadata for the text.
Container of information about a piece of text.
A time period inside of an example that has a time dimension (e.g. video).
A vertex represents a 2D point in the image. NOTE: the vertex coordinates are in the same scale as the original image.
Video classification annotation.
Config for video classification human labeling task. Currently two types of video classification are supported: 1. Assign labels on the entire video. 2. Split the video into multiple video clips based on camera shot, and assign labels on each video clip.
Video event annotation.
Video object tracking annotation.
Container of information of a video.
Container of information of a video thumbnail.
The response message for Operations.ListOperations.
This resource represents a long-running operation that is the result of a network API call.
A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); }
The
Status
type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by
gRPC. Each
Status
message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the
API Design Guide.
Creates an annotation spec set by providing a set of labels.
Deletes an annotation spec set by resource name.
Gets an annotation spec set by resource name.
Lists annotation spec sets for a project. Pagination is supported.
Gets a data item in a dataset by resource name. This API can be called after data are imported into dataset.
Lists data items in a dataset. This API can be called after data are imported into dataset. Pagination is supported.
Deletes an annotated dataset by resource name.
Gets an example by resource name, including both data and annotation.
Lists examples in an annotated dataset. Pagination is supported.
Delete a FeedbackThread.
Create a FeedbackMessage object.
Delete a FeedbackMessage.
Get a FeedbackMessage object.
List FeedbackMessages with pagination.
Get a FeedbackThread object.
List FeedbackThreads with pagination.
Gets an annotated dataset by resource name.
Lists annotated datasets for a dataset. Pagination is supported.
Creates dataset. If success return a Dataset resource.
Gets a data item in a dataset by resource name. This API can be called after data are imported into dataset.
Lists data items in a dataset. This API can be called after data are imported into dataset. Pagination is supported.
Deletes a dataset by resource name.
Searches example comparisons from an evaluation. The return format is a list of example comparisons that show ground truth and prediction(s) for a single input. Search by providing an evaluation ID.
Gets an evaluation by resource name (to search, use projects.evaluations.search).
Exports data and annotations from dataset.
Gets dataset by resource name.
Starts a labeling task for image. The type of image labeling task is configured by feature in the request.
Imports data into dataset based on source locations defined in request. It can be called multiple times for the same dataset. Each dataset can only have one long running operation running on it. For example, no labeling task (also long running operation) can be started while importing is still ongoing. Vice versa.
Lists datasets under a project. Pagination is supported.
Starts a labeling task for text. The type of text labeling task is configured by feature in the request.
Starts a labeling task for video. The type of video labeling task is configured by feature in the request.
Creates an evaluation job.
Stops and deletes an evaluation job.
Gets an evaluation job by resource name.
Lists all evaluation jobs within a project with possible filters. Pagination is supported.
Updates an evaluation job. You can only update certain fields of the job’s EvaluationJobConfig: humanAnnotationConfig.instruction
, exampleCount
, and exampleSamplePercentage
. If you want to change any other aspect of the evaluation job, you must delete the job and create a new one.
Pauses an evaluation job. Pausing an evaluation job that is already in a PAUSED
state is a no-op.
Resumes a paused evaluation job. A deleted evaluation job can’t be resumed. Resuming a running or scheduled evaluation job is a no-op.
Searches evaluations within a project.
Creates an instruction for how data should be labeled.
Deletes an instruction object by resource name.
Gets an instruction by resource name.
Lists instructions for a project. Pagination is supported.
A builder providing access to all methods supported on
project resources.
It is not used directly, but through the
DataLabeling
hub.
Starts asynchronous cancellation on a long-running operation. The server makes a best effort to cancel the operation, but success is not guaranteed. If the server doesn’t support this method, it returns google.rpc.Code.UNIMPLEMENTED
. Clients can use Operations.GetOperation or other methods to check whether the cancellation succeeded or whether the operation completed despite cancellation. On successful cancellation, the operation is not deleted; instead, it becomes an operation with an Operation.error value with a google.rpc.Status.code of 1, corresponding to Code.CANCELLED
.
Deletes a long-running operation. This method indicates that the client is no longer interested in the operation result. It does not cancel the operation. If the server doesn’t support this method, it returns google.rpc.Code.UNIMPLEMENTED
.
Gets the latest state of a long-running operation. Clients can use this method to poll the operation result at intervals as recommended by the API service.
Lists operations that match the specified filter in the request. If the server doesn’t support this method, it returns UNIMPLEMENTED
.