Index
AutoMl
(interface)PredictionService
(interface)AnnotationPayload
(message)AnnotationSpec
(message)BatchPredictInputConfig
(message)BatchPredictOperationMetadata
(message)BatchPredictOperationMetadata.BatchPredictOutputInfo
(message)BatchPredictOutputConfig
(message)BatchPredictRequest
(message)BatchPredictResult
(message)BigQueryDestination
(message)BoundingBoxMetricsEntry
(message)BoundingBoxMetricsEntry.ConfidenceMetricsEntry
(message)BoundingPoly
(message)ClassificationAnnotation
(message)ClassificationEvaluationMetrics
(message)ClassificationEvaluationMetrics.ConfidenceMetricsEntry
(message)ClassificationEvaluationMetrics.ConfusionMatrix
(message)ClassificationEvaluationMetrics.ConfusionMatrix.Row
(message)CreateDatasetRequest
(message)CreateModelOperationMetadata
(message)CreateModelRequest
(message)Dataset
(message)DeleteDatasetRequest
(message)DeleteModelRequest
(message)DeleteOperationMetadata
(message)DeployModelOperationMetadata
(message)DeployModelRequest
(message)ExamplePayload
(message)ExportDataOperationMetadata
(message)ExportDataOperationMetadata.ExportDataOutputInfo
(message)ExportDataRequest
(message)GcsDestination
(message)GcsSource
(message)GetAnnotationSpecRequest
(message)GetDatasetRequest
(message)GetModelEvaluationRequest
(message)GetModelRequest
(message)ImportDataOperationMetadata
(message)ImportDataRequest
(message)InputConfig
(message)ListDatasetsRequest
(message)ListDatasetsResponse
(message)ListModelEvaluationsRequest
(message)ListModelEvaluationsResponse
(message)ListModelsRequest
(message)ListModelsResponse
(message)Model
(message)Model.DeploymentState
(enum)ModelEvaluation
(message)NormalizedVertex
(message)OperationMetadata
(message)OutputConfig
(message)PredictRequest
(message)PredictResponse
(message)UndeployModelOperationMetadata
(message)UndeployModelRequest
(message)VideoObjectTrackingAnnotation
(message)VideoObjectTrackingDatasetMetadata
(message)VideoObjectTrackingEvaluationMetrics
(message)VideoObjectTrackingModelMetadata
(message)
AutoMl
AutoML Server API.
The resource names are assigned by the server. The server never reuses names that it has created after the resources with those names are deleted.
An ID of a resource is the last element of the item's resource name. For projects/{project_id}/locations/{location_id}/datasets/{dataset_id}
, then the id for the item is {dataset_id}
.
Currently the only supported location_id
is "us-central1".
On any input that is documented to expect a string parameter in snake_case or dash-case, either of those cases is accepted.
CreateDataset | |
---|---|
Creates a dataset.
|
CreateModel | |
---|---|
Creates a model. Returns a Model in the
|
DeleteDataset | |
---|---|
Deletes a dataset and all of its contents. Returns empty response in the
|
DeleteModel | |
---|---|
Deletes a model. Returns
|
DeployModel | |
---|---|
Deploys a model. Not applicable for this product. This product automatically deploys models when they are successfully trained. Returns an empty response in the
|
ExportData | |
---|---|
Exports dataset's data to the provided output location. Returns an empty response in the
|
GetAnnotationSpec | |
---|---|
Gets an annotation spec.
|
GetDataset | |
---|---|
Gets a dataset.
|
GetModel | |
---|---|
Gets a model.
|
GetModelEvaluation | |
---|---|
Gets a model evaluation.
|
ImportData | |
---|---|
Imports data into a dataset. You can only call this method for an empty Dataset.
|
ListDatasets | |
---|---|
Lists datasets in a project.
|
ListModelEvaluations | |
---|---|
Lists model evaluations.
|
ListModels | |
---|---|
Lists models.
|
UndeployModel | |
---|---|
Removes a deployed model. Not applicable for this product. This product automatically removes deployed models that are deleted. Returns an empty response in the
|
PredictionService
AutoML Prediction API.
On any input that is documented to expect a string parameter in snake_case or dash-case, either of those cases is accepted.
BatchPredict | |
---|---|
Perform a batch prediction and return the id of a long-running operation. You can request the operation result by using the
|
Predict | |
---|---|
Not used for AutoML Video Intelligence Object Tracking.
|
AnnotationPayload
Contains annotation information that is relevant to AutoML.
Fields | ||
---|---|---|
annotation_spec_id |
Output only . The resource ID of the annotation spec that this annotation pertains to. The annotation spec comes from either an ancestor dataset, or the dataset that was used to train the model in use. |
|
display_name |
Output only. The value of |
|
Union field detail . Output only . Additional information about the annotation specific to the AutoML domain. detail can be only one of the following: |
||
classification |
Not used for AutoML Video Intelligence Object Tracking. |
|
video_object_tracking |
Annotation details for object tracking predictions. |
AnnotationSpec
A definition of an annotation.
Fields | |
---|---|
name |
Output only. Resource name of the annotation spec. Form: 'projects/{project_id}/locations/{location_id}/datasets/{dataset_id}/annotationSpecs/{annotation_spec_id}' |
display_name |
Required. The name of the annotation spec to show in the interface. The name can be up to 32 characters long and can consist only of ASCII Latin letters A-Z and a-z, underscores (_), and ASCII digits 0-9. |
example_count |
Output only. The number of examples in the parent dataset labeled by the annotation spec. |
BatchPredictInputConfig
Input configuration for BatchPredict
action. The input is one or more CSV files stored in Google Cloud Storage where the CSV files are in the following format:
GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END
GCS_FILE_PATH
identifies the Google Cloud Storage path to a video up to 50GB in size and up to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI.TIME_SEGMENT_START
andTIME_SEGMENT_END
must be within the length of the video, and end has to be after the start. Both are measured in seconds from the beginning of the video.
Three sample rows:
gs://folder/video1.mp4,10,40
gs://folder/video1.mp4,20,60
gs://folder/vid2.mov,0,inf
See Annotating videos for more information.
Fields | |
---|---|
gcs_source |
The Google Cloud Storage location for the input content. |
BatchPredictOperationMetadata
Details of BatchPredict operation.
Fields | |
---|---|
input_config |
Output only. The input config that was given upon starting this batch predict operation. |
output_info |
Output only. Information further describing this batch predict's output. |
BatchPredictOutputInfo
Further describes this batch predict's output. Supplements
Fields | ||
---|---|---|
Union field output_location . The output location into which prediction output is written. output_location can be only one of the following: |
||
gcs_output_directory |
The full path of the Google Cloud Storage directory created, into which the prediction output is written. |
|
bigquery_output_dataset |
The path of the BigQuery dataset created, in bq://projectId.bqDatasetId format, into which the prediction output is written. |
BatchPredictOutputConfig
Output configuration for BatchPredict
Action.
AutoML Video Intelligence creates a directory specified in the
. The name of the directory is "prediction-<model-display-name>-<timestamp-of-prediction-call>", where timestamp is in gcsDestination
YYYY-MM-DDThh:mm:ss.sssZ
ISO-8601 format.
AutoML Video Intelligence creates a file named video_object_tracking.csv in the new directory, and also a JSON file for each object tracking request in it, that is, each row in the input CSV file.
The format of the video_object_tracking.csv file is as follows:
GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS
The
GCS_FILE_PATH
,TIME_SEGMENT_START
,TIME_SEGMENT_END
match the same fields from the input CSV file.JSON_FILE_NAME
is the name of the JSON file in the output directory that contains prediction responses for each video time segment. The JSON files are named video_object_tracking_1.json, video_object_tracking_2.json, and so on up to the number of object tracking requests. These files include theAnnotationPayload
in JSON format.STATUS
contains "OK" if the prediction completed successfully; otherwise contains error information. IfSTATUS
is not "OK" then the JSON file for that prediction might be empty or the file might not exist.
Each JSON file where STATUS
is "OK", contains a list of AnnotationPayload protos in JSON format, which are the predictions for the video time segment the file is assigned to in the video_classification.csv. All AnnotationPayload protos have a video_object_tracking
field, and are sorted by the video_object_tracking.type
field. The types returned are determined by the object_tracking_types
parameter of BatchPredictRequest.params
.
Fields | ||
---|---|---|
Union field destination . Required. The destination of the output. destination can be only one of the following: |
||
gcs_destination |
The Google Cloud Storage location of the directory where the output is to be written to. |
|
bigquery_destination |
The BigQuery location where the output is to be written to. |
BatchPredictRequest
Request message for PredictionService.BatchPredict
.
Fields | |||||||
---|---|---|---|---|---|---|---|
name |
Name of the model requested to serve the batch prediction. Authorization requires the following Google IAM permission on the specified resource
|
||||||
input_config |
Required. The input configuration for batch prediction. |
||||||
output_config |
Required. The Configuration specifying where output predictions should be written. |
||||||
params |
Can be one of the following:
See Annotating videos for more details. |
BatchPredictResult
Result of the Batch Predict. This message is returned in response
of the operation returned by the PredictionService.BatchPredict
.
Fields | |
---|---|
metadata |
Additional domain-specific prediction response metadata. Includes the following fields:
|
BigQueryDestination
The BigQuery location for the output content.
Fields | |
---|---|
output_uri |
Required. BigQuery URI to a project, up to 2000 characters long. For example: |
BoundingBoxMetricsEntry
Bounding box matching model metrics for a single intersection-over-union threshold and multiple label match confidence thresholds.
Fields | |
---|---|
iou_threshold |
Output only. The intersection-over-union threshold value used to compute this metrics entry. |
mean_average_precision |
Output only. The mean average precision, most often close to au_prc. |
confidence_metrics_entries[] |
Output only. Metrics for each label-match confidence_threshold from 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99. Precision-recall curve is derived from them. |
ConfidenceMetricsEntry
Metrics for a single confidence threshold.
Fields | |
---|---|
confidence_threshold |
Output only. The confidence threshold value used to compute the metrics. |
recall |
Output only. Recall under the given confidence threshold. |
precision |
Output only. Precision under the given confidence threshold. |
f1_score |
Output only. The harmonic mean of recall and precision. |
BoundingPoly
A bounding polygon of a detected object on a plane. On output both vertices and normalized_vertices are provided. The polygon is formed by connecting vertices in the order they are listed.
Fields | |
---|---|
normalized_vertices[] |
Output only . The bounding polygon normalized vertices. |
ClassificationAnnotation
Contains annotation details specific to classification.
Fields | |
---|---|
score |
Output only. A confidence estimate between 0.0 and 1.0. A higher value means greater confidence that the annotation is positive. If a user approves an annotation as negative or positive, the score value remains unchanged. If a user creates an annotation, the score is 0 for negative or 1 for positive. |
ClassificationEvaluationMetrics
Model evaluation metrics for classification problems. Note: For Video Classification this metrics only describe quality of the Video Classification predictions of "segment_classification" type.
Fields | |
---|---|
au_prc |
Output only. The Area Under Precision-Recall Curve metric. Micro-averaged for the overall evaluation. |
base_au_prc |
Output only. The Area Under Precision-Recall Curve metric based on priors. Micro-averaged for the overall evaluation. Deprecated. |
au_roc |
Output only. The Area Under Receiver Operating Characteristic curve metric. Micro-averaged for the overall evaluation. |
log_loss |
Output only. The Log Loss metric. |
confidence_metrics_entry[] |
Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed. |
confusion_matrix |
Output only. Confusion matrix of the evaluation. Only set for MULTICLASS classification problems where number of labels is no more than 10. Only set for model level evaluation, not for evaluation per label. |
annotation_spec_id[] |
Output only. The annotation spec ids used for this evaluation. |
ConfidenceMetricsEntry
Metrics for a single confidence threshold.
Fields | |
---|---|
confidence_threshold |
Output only. Metrics are computed with an assumption that the model never returns predictions with score lower than this value. |
position_threshold |
Output only. Metrics are computed with an assumption that the model always returns at most this many predictions (ordered by their score, descendingly), but they all still need to meet the confidence_threshold. |
recall |
Output only. Recall (True Positive Rate) for the given confidence threshold. |
precision |
Output only. Precision for the given confidence threshold. |
false_positive_rate |
Output only. False Positive Rate for the given confidence threshold. |
f1_score |
Output only. The harmonic mean of recall and precision. |
recall_at1 |
Output only. The Recall (True Positive Rate) when only considering the label that has the highest prediction score and not below the confidence threshold for each example. |
precision_at1 |
Output only. The precision when only considering the label that has the highest prediction score and not below the confidence threshold for each example. |
false_positive_rate_at1 |
Output only. The False Positive Rate when only considering the label that has the highest prediction score and not below the confidence threshold for each example. |
f1_score_at1 |
Output only. The harmonic mean of |
true_positive_count |
Output only. The number of model created labels that match a ground truth label. |
false_positive_count |
Output only. The number of model created labels that do not match a ground truth label. |
false_negative_count |
Output only. The number of ground truth labels that are not matched by a model created label. |
true_negative_count |
Output only. The number of labels that were not created by the model, but if they would, they would not match a ground truth label. |
ConfusionMatrix
Confusion matrix of the model running the classification.
Fields | |
---|---|
annotation_spec_id[] |
Output only. IDs of the annotation specs used in the confusion matrix. |
row[] |
Output only. Rows in the confusion matrix. The number of rows is equal to the size of |
Row
Output only. A row in the confusion matrix.
Fields | |
---|---|
example_count[] |
Output only. Value of the specific cell in the confusion matrix. The number of values each row has (i.e. the length of the row) is equal to the length of the |
CreateDatasetRequest
Request message for AutoMl.CreateDataset
.
Fields | |
---|---|
parent |
The resource name of the project to create the dataset for. Authorization requires the following Google IAM permission on the specified resource
|
dataset |
The dataset to create. |
CreateModelOperationMetadata
Details of CreateModel operation.
CreateModelRequest
Request message for AutoMl.CreateModel
.
Fields | |
---|---|
parent |
Resource name of the parent project where the model is being created. Authorization requires the following Google IAM permission on the specified resource
|
model |
The model to create. |
Dataset
A workspace for solving a single, particular machine learning (ML) problem. A workspace contains examples that may be annotated.
Fields | |
---|---|
name |
Output only. The resource name of the dataset. Form: |
display_name |
Required. The name of the dataset to show in the interface. The name can be up to 32 characters long and can consist only of ASCII Latin letters A-Z and a-z, underscores (_), and ASCII digits 0-9. |
example_count |
Output only. The number of examples in the dataset. |
create_time |
Output only. Timestamp when this dataset was created. |
video_object_tracking_dataset_metadata |
Metadata for a dataset used for video object tracking. |
DeleteDatasetRequest
Request message for AutoMl.DeleteDataset
.
Fields | |
---|---|
name |
The resource name of the dataset to delete. Authorization requires the following Google IAM permission on the specified resource
|
DeleteModelRequest
Request message for AutoMl.DeleteModel
.
Fields | |
---|---|
name |
Resource name of the model being deleted. Authorization requires the following Google IAM permission on the specified resource
|
DeleteOperationMetadata
Details of operations that perform deletes of any entities.
DeployModelOperationMetadata
Details of DeployModel operation.
DeployModelRequest
Request message for AutoMl.DeployModel
.
Fields | |
---|---|
name |
Resource name of the model to deploy. Authorization requires the following Google IAM permission on the specified resource
|
ExamplePayload
Example data used for training or prediction.
ExportDataOperationMetadata
Details of ExportData operation.
Fields | |
---|---|
output_info |
Output only. Information further describing this export data's output. |
ExportDataOutputInfo
Further describes this export data's output. Supplements OutputConfig
.
Fields | ||
---|---|---|
Union field output_location . The output location to which the exported data is written. output_location can be only one of the following: |
||
gcs_output_directory |
The full path of the Google Cloud Storage directory created, into which the exported data is written. |
|
bigquery_output_dataset |
The path of the BigQuery dataset created, in bq://projectId.bqDatasetId format, into which the exported data is written. |
ExportDataRequest
Request message for AutoMl.ExportData
.
Fields | |
---|---|
name |
Required. The resource name of the dataset. Authorization requires the following Google IAM permission on the specified resource
|
output_config |
Required. The desired output location. |
GcsDestination
The Google Cloud Storage location where the output is to be written to.
Fields | |
---|---|
output_uri_prefix |
Required. Google Cloud Storage URI to output directory, up to 2000 characters long. Accepted forms: * Prefix path: gs://bucket/directory The requesting user must have write permission to the bucket. The directory is created if it doesn't exist. |
GcsSource
The Google Cloud Storage location for the input content.
Fields | |
---|---|
input_uris[] |
Required. Google Cloud Storage URIs to input files, up to 2000 characters long. Accepted forms: * Full object path, e.g. gs://bucket/directory/object.csv |
GetAnnotationSpecRequest
Request message for AutoMl.GetAnnotationSpec
.
Fields | |
---|---|
name |
The resource name of the annotation spec to retrieve. Authorization requires the following Google IAM permission on the specified resource
|
GetDatasetRequest
Request message for AutoMl.GetDataset
.
Fields | |
---|---|
name |
The resource name of the dataset to retrieve. Authorization requires the following Google IAM permission on the specified resource
|
GetModelEvaluationRequest
Request message for AutoMl.GetModelEvaluation
.
Fields | |
---|---|
name |
Resource name for the model evaluation. Authorization requires the following Google IAM permission on the specified resource
|
GetModelRequest
Request message for AutoMl.GetModel
.
Fields | |
---|---|
name |
Resource name of the model. Authorization requires the following Google IAM permission on the specified resource
|
ImportDataOperationMetadata
Details of ImportData operation.
ImportDataRequest
Request message for AutoMl.ImportData
.
Fields | |
---|---|
name |
Required. Dataset name. Dataset must already exist. All imported annotations and examples will be added. Authorization requires the following Google IAM permission on the specified resource
|
input_config |
Required. The desired input location and its domain specific semantics, if any. |
InputConfig
Input configuration for ImportData
action.
The format of input depends on dataset_metadata the Dataset into which the import is happening has. As input source the gcs_source
is expected, unless specified otherwise. If a file with identical content (even if it had different GCS_FILE_PATH
) is mentioned multiple times , then its label, bounding boxes etc. are appended. The same file should be always provided with the same ML_USE
and GCS_FILE_PATH
, if it is not then these values are nondeterministically selected from the given ones.
The formats are represented in EBNF with commas being literal and with non-terminal symbols defined near the end of this comment. The formats are:
See Preparing your training data for more information.
A CSV file(s) with each line in format:
ML_USE,GCS_FILE_PATH
ML_USE
- Identifies the data set that the current row (file) applies to. This value can be one of the following:TRAIN
- Rows in this file are used to train the model.TEST
- Rows in this file are used to test the model during training.UNASSIGNED
- Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing.
GCS_FILE_PATH
- Identifies a file stored in Google Cloud Storage that contains the model training information.
For example file:
TRAIN,gs:folder/train_videos.csv
TEST,gs:folder/test_videos.csv
UNASSIGNED,gs:folder/other_videos.csv
After the training data set has been determined from the TRAIN
and UNASSIGNED
CSV files, the training data is divided into train and validation data sets. 70% for training and 30% for validation.
Each CSV file specified using the GCS_FILE_PATH
field has the following format:
GCS_FILE_PATH,LABEL,[INSTANCE_ID],TIMESTAMP,BOUNDING_BOX
GCS_FILE_PATH
- The path to a video stored in Google Cloud Storage. The video can be up to 1h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI.LABEL
- A label that identifies the object of the video segment.[INSTANCE_ID]
- You can provide an instance id or leave this field blank. Providing instance ids can help to obtain a better model. That is, you can identify a specific labeled entity in a video frame with an instance id. If that entity leaves the video frame, and shows up at a later timestamp, you can identify the identity with the same instance id to help train a more accurate model.TIMESTAMP
- The time, in seconds that identifies the frame of video with the labeled object.TIMESTAMP
must be greater than zero and less than or equal to the length of the video. AutoML Video Intelligence uses the video frame that is closest to theTIMESTAMP
to train the model.BOUNDING_BOX
- The coordinates of the labeled object in the video frame. You can specify up to 500 bounding boxes per video frame. A bounding box consists of four pairs of horizontal and vertical (x,y) coordinates that form a square region of the video that contains an object to be tracked. For example:0.8,0.8,0.9,0.8,0.9,0.9,0.8,0.9
. An empty field is equivalent to a value of 0.
Here is an example of the format of one of the CSV files identified by the gcsSource
"top level" file.
gs://folder/video1.avi,car,1,12.10,0.8,0.8,0.9,0.8,0.9,0.9,0.8,0.9
gs://folder/video1.avi,car,1,12.90,0.4,0.8,0.5,0.8,0.5,0.9,0.4,0.9
gs://folder/video1.avi,car,2,12.10,.4,.2,.5,.2,.5,.3,.4,.3
gs://folder/video1.avi,car,2,12.90,.8,.2,,,.9,.3,,
gs://folder/video1.avi,bike,,12,50,.45,.45,,,.55,.55
gs://folder/video2.avi,car,1,0,.1,.9,,,.9,.1
Errors:
If any of the provided CSV files can't be parsed or if more than certain percent of CSV rows cannot be processed then the operation fails and nothing is imported. Regardless of overall success or failure the per-row failures, up to a certain count cap, will be listed in Operation.metadata.partial_failures.
Fields | |
---|---|
gcs_source |
The Google Cloud Storage location for the input content. |
ListDatasetsRequest
Request message for AutoMl.ListDatasets
.
Fields | |
---|---|
parent |
The resource name of the project from which to list datasets. Authorization requires the following Google IAM permission on the specified resource
|
filter |
An expression for filtering the results of the request.
An example of using the filter is:
|
page_size |
Requested page size. Server may return fewer results than requested. If unspecified, server will pick a default size. |
page_token |
A token identifying a page of results for the server to return Typically obtained via |
ListDatasetsResponse
Response message for AutoMl.ListDatasets
.
Fields | |
---|---|
datasets[] |
The datasets read. |
next_page_token |
A token to retrieve next page of results. Pass to |
ListModelEvaluationsRequest
Request message for AutoMl.ListModelEvaluations
.
Fields | |
---|---|
parent |
Resource name of the model to list the model evaluations for. If modelId is set as "-", this will list model evaluations from across all models of the parent location. Authorization requires the following Google IAM permission on the specified resource
|
filter |
An expression for filtering the results of the request.
Some examples of using the filter are:
|
page_size |
Requested page size. |
page_token |
A token identifying a page of results for the server to return. Typically obtained via |
ListModelEvaluationsResponse
Response message for AutoMl.ListModelEvaluations
.
Fields | |
---|---|
model_evaluation[] |
List of model evaluations in the requested page. |
next_page_token |
A token to retrieve next page of results. Pass to the |
ListModelsRequest
Request message for AutoMl.ListModels
.
Fields | |
---|---|
parent |
Resource name of the project, from which to list the models. Authorization requires the following Google IAM permission on the specified resource
|
filter |
An expression for filtering the results of the request.
Some examples of using the filter are:
|
page_size |
Requested page size. |
page_token |
A token identifying a page of results for the server to return Typically obtained via |
ListModelsResponse
Response message for AutoMl.ListModels
.
Fields | |
---|---|
model[] |
List of models in the requested page. |
next_page_token |
A token to retrieve next page of results. Pass to |
Model
API proto representing a trained machine learning model.
Fields | |
---|---|
name |
Output only. Resource name of the model. Format: |
display_name |
Required. The name of the model to show in the interface. The name can be up to 32 characters long and can consist only of ASCII Latin letters A-Z and a-z, underscores (_), and ASCII digits 0-9. It must start with a letter. |
dataset_id |
Required. The resource ID of the dataset used to create the model. The dataset must come from the same ancestor project and location. |
create_time |
Output only. Timestamp when the model training finished and can be used for prediction. |
update_time |
Output only. Timestamp when this model was last updated. |
deployment_state |
Output only. Deployment state of the model. A model can only serve prediction requests after it gets deployed. |
video_object_tracking_model_metadata |
Metadata for video object tracking models. |
DeploymentState
Deployment state of the model.
Enums | |
---|---|
DEPLOYMENT_STATE_UNSPECIFIED |
Should not be used, an un-set enum has this value by default. |
DEPLOYED |
Model is deployed. |
UNDEPLOYED |
Model is not deployed. |
ModelEvaluation
Evaluation results of a model.
Fields | ||
---|---|---|
name |
Output only. Resource name of the model evaluation. Format:
|
|
annotation_spec_id |
Output only. The ID of the annotation spec that the model evaluation applies to. The The ID is empty for the overall model evaluation. |
|
display_name |
Output only. The value of The display_name is empty for the overall model evaluation. |
|
create_time |
Output only. Timestamp when this model evaluation was created. |
|
evaluated_example_count |
Output only. The number of examples used for model evaluation, i.e. for which ground truth from time of model creation is compared against the predicted annotations created by the model. For overall ModelEvaluation (i.e. with annotation_spec_id not set) this is the total number of all examples used for evaluation. Otherwise, this is the count of examples that according to the ground truth were annotated by the |
|
Union field metrics . Output only. Problem type specific evaluation metrics. metrics can be only one of the following: |
||
classification_evaluation_metrics |
Model evaluation metrics. |
|
video_object_tracking_evaluation_metrics |
Evaluation metrics for object tracking models. |
NormalizedVertex
A vertex represents a 2D point in the image. The normalized vertex coordinates are between 0 to 1 fractions relative to the original plane (image, video). E.g. if the plane (e.g. whole image) would have size 10 x 20 then a point with normalized coordinates (0.1, 0.3) would be at the position (1, 6) on that plane.
Fields | |
---|---|
x |
Required. Horizontal coordinate. |
y |
Required. Vertical coordinate. |
OperationMetadata
Metadata used across all long running operations returned by AutoML API.
Fields | ||
---|---|---|
progress_percent |
Output only. Progress of operation. Range: [0, 100]. Not used currently. |
|
partial_failures[] |
Output only. Partial failures encountered. E.g. single files that couldn't be read. This field should never exceed 20 entries. Status details field will contain standard GCP error details. |
|
create_time |
Output only. Time when the operation was created. |
|
update_time |
Output only. Time when the operation was updated for the last time. |
|
Union field details . Ouptut only. Details of specific operation. Even if this field is empty, the presence allows to distinguish different types of operations. details can be only one of the following: |
||
delete_details |
Details of a Delete operation. |
|
deploy_model_details |
Details of a DeployModel operation. |
|
undeploy_model_details |
Details of an UndeployModel operation. |
|
create_model_details |
Details of CreateModel operation. |
|
import_data_details |
Details of ImportData operation. |
|
batch_predict_details |
Details of BatchPredict operation. |
|
export_data_details |
Details of ExportData operation. |
OutputConfig
Output configuration for ExportData.
As destination the gcs_destination
must be set. Only ground truth annotations are exported (not approved annotations are not exported).
The outputs correspond to how the data was imported, and may be used as input to import data. The output formats are represented as EBNF with literal commas and same non-terminal symbols definitions as the InputConfig
for import data.
The outputs are contained within a CSV file named video_object_tracking.csv
, with each line in the following format and may have multiple lines per a single ML_USE
:
ML_USE,GCS_FILE_PATH
Each GCS_FILE_PATH
leads to another CSV file that describes examples that have given ML_USE
, using the following row format:
GCS_FILE_PATH,LABEL,INSTANCE_ID,TIMESTAMP,BOUNDING_BOX
Here the data in the GCS_FILE_PATH
column point at the original, source locations of the imported videos.
Fields | |
---|---|
gcs_destination |
The Google Cloud Storage location where the output is to be written to. In the given directory a new directory is created similar to the following where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format:
All export output will be written into that directory. |
PredictRequest
Request message for PredictionService.Predict
.
Fields | |
---|---|
name |
Name of the model requested to serve the prediction. Authorization requires the following Google IAM permission on the specified resource
|
payload |
Required. Payload to perform a prediction on. The payload must match the problem type that the model was trained to solve. |
params |
Additional domain-specific parameters, any string must be up to 25000 characters long. |
PredictResponse
Response message for PredictionService.Predict
.
Fields | |
---|---|
payload[] |
Prediction result. |
metadata |
Additional domain-specific prediction response metadata. |
UndeployModelOperationMetadata
Details of UndeployModel operation.
UndeployModelRequest
Request message for AutoMl.UndeployModel
.
Fields | |
---|---|
name |
Resource name of the model to undeploy. Authorization requires the following Google IAM permission on the specified resource
|
VideoObjectTrackingAnnotation
Annotation details for video object tracking.
Fields | |
---|---|
instance_id |
Optional. The instance of the object, expressed as a positive integer. Used to tell apart objects of the same type (i.e. AnnotationSpec) when multiple are present on a single example. NOTE: Instance ID prediction quality is not a part of model evaluation and is done as best effort. Especially in cases when an entity goes off-screen for a longer time (minutes), when it comes back it may be given a new instance ID. |
time_offset |
Required. A time (frame) of a video to which this annotation pertains. Represented as the duration since the video's start. |
bounding_box |
Required. The rectangle representing the object location on the frame (i.e. at the time_offset of the video). |
score |
Output only. The confidence that this annotation is positive for the video at the time_offset, value in [0, 1], higher means higher positivity confidence. For annotations created by the user the score is 1. When user approves an annotation, the original float score is kept (and not changed to 1). |
VideoObjectTrackingDatasetMetadata
Dataset metadata specific to video object tracking.
VideoObjectTrackingEvaluationMetrics
Model evaluation metrics for video object tracking problems. Evaluates prediction quality of both labeled bounding boxes and labeled tracks (i.e. series of bounding boxes sharing same label and instance ID).
Fields | |
---|---|
evaluated_frame_count |
Output only. The number of video frames used to create this evaluation. |
evaluated_bounding_box_count |
Output only. The total number of bounding boxes (i.e. summed over all frames) the ground truth used to create this evaluation had. |
evaluated_track_count |
Output only. The total number of tracks (i.e. as seen across all frames) the ground truth used to create this evaluation had. |
bounding_box_metrics_entries[] |
Output only. The bounding boxes match metrics for each Intersection-over-union threshold 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and each label confidence threshold 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 pair. |
bounding_box_mean_average_precision |
Output only. The single metric for bounding boxes evaluation: the mean_average_precision averaged over all bounding_box_metrics_entries. |
VideoObjectTrackingModelMetadata
Model metadata specific to video object tracking.