class google.cloud.aiplatform.v1beta1.schema.predict.prediction_v1beta1.types.ClassificationPredictionResult(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
Prediction output format for Image and Text Classification.
ids()
The resource IDs of the AnnotationSpecs that had been identified.
Type
MutableSequence[int]
display_names()
The display names of the AnnotationSpecs that had been identified, order matches the IDs.
Type
MutableSequence[str]
confidences()
The Model’s confidences in correctness of the predicted IDs, higher value means higher confidence. Order matches the Ids.
Type
MutableSequence[float]
confidences(: MutableSequence[float )
display_names(: MutableSequence[str )
ids(: MutableSequence[int )
class google.cloud.aiplatform.v1beta1.schema.predict.prediction_v1beta1.types.ImageObjectDetectionPredictionResult(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
Prediction output format for Image Object Detection.
ids()
The resource IDs of the AnnotationSpecs that had been identified, ordered by the confidence score descendingly.
Type
MutableSequence[int]
display_names()
The display names of the AnnotationSpecs that had been identified, order matches the IDs.
Type
MutableSequence[str]
confidences()
The Model’s confidences in correctness of the predicted IDs, higher value means higher confidence. Order matches the Ids.
Type
MutableSequence[float]
bboxes()
Bounding boxes, i.e. the rectangles over the image, that
pinpoint the found AnnotationSpecs. Given in order that
matches the IDs. Each bounding box is an array of 4 numbers
xMin, xMax, yMin, and yMax, which represent
the extremal coordinates of the box. They are relative to
the image size, and the point 0,0 is in the top left of the
image.
Type
MutableSequence[google.protobuf.struct_pb2.ListValue]
bboxes(: MutableSequence[google.protobuf.struct_pb2.ListValue )
confidences(: MutableSequence[float )
display_names(: MutableSequence[str )
ids(: MutableSequence[int )
class google.cloud.aiplatform.v1beta1.schema.predict.prediction_v1beta1.types.ImageSegmentationPredictionResult(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
Prediction output format for Image Segmentation.
category_mask()
A PNG image where each pixel in the mask represents the category in which the pixel in the original image was predicted to belong to. The size of this image will be the same as the original image. The mapping between the AnntoationSpec and the color can be found in model’s metadata. The model will choose the most likely category and if none of the categories reach the confidence threshold, the pixel will be marked as background.
Type
str
confidence_mask()
A one channel image which is encoded as an 8bit lossless PNG. The size of the image will be the same as the original image. For a specific pixel, darker color means less confidence in correctness of the cateogry in the categoryMask for the corresponding pixel. Black means no confidence and white means complete confidence.
Type
str
category_mask(: st )
confidence_mask(: st )
class google.cloud.aiplatform.v1beta1.schema.predict.prediction_v1beta1.types.TabularClassificationPredictionResult(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
Prediction output format for Tabular Classification.
classes()
The name of the classes being classified, contains all possible values of the target column.
Type
MutableSequence[str]
scores()
The model’s confidence in each class being correct, higher value means higher confidence. The N-th score corresponds to the N-th class in classes.
Type
MutableSequence[float]
classes(: MutableSequence[str )
scores(: MutableSequence[float )
class google.cloud.aiplatform.v1beta1.schema.predict.prediction_v1beta1.types.TabularRegressionPredictionResult(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
Prediction output format for Tabular Regression.
value()
The regression value.
Type
float
lower_bound()
The lower bound of the prediction interval.
Type
float
upper_bound()
The upper bound of the prediction interval.
Type
float
lower_bound(: floa )
upper_bound(: floa )
value(: floa )
class google.cloud.aiplatform.v1beta1.schema.predict.prediction_v1beta1.types.TextExtractionPredictionResult(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
Prediction output format for Text Extraction.
ids()
The resource IDs of the AnnotationSpecs that had been identified, ordered by the confidence score descendingly.
Type
MutableSequence[int]
display_names()
The display names of the AnnotationSpecs that had been identified, order matches the IDs.
Type
MutableSequence[str]
text_segment_start_offsets()
The start offsets, inclusive, of the text segment in which the AnnotationSpec has been identified. Expressed as a zero-based number of characters as measured from the start of the text snippet.
Type
MutableSequence[int]
text_segment_end_offsets()
The end offsets, inclusive, of the text segment in which the AnnotationSpec has been identified. Expressed as a zero-based number of characters as measured from the start of the text snippet.
Type
MutableSequence[int]
confidences()
The Model’s confidences in correctness of the predicted IDs, higher value means higher confidence. Order matches the Ids.
Type
MutableSequence[float]
confidences(: MutableSequence[float )
display_names(: MutableSequence[str )
ids(: MutableSequence[int )
text_segment_end_offsets(: MutableSequence[int )
text_segment_start_offsets(: MutableSequence[int )
class google.cloud.aiplatform.v1beta1.schema.predict.prediction_v1beta1.types.TextSentimentPredictionResult(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
Prediction output format for Text Sentiment
sentiment()
The integer sentiment labels between 0 (inclusive) and sentimentMax label (inclusive), while 0 maps to the least positive sentiment and sentimentMax maps to the most positive one. The higher the score is, the more positive the sentiment in the text snippet is. Note: sentimentMax is an integer value between 1 (inclusive) and 10 (inclusive).
Type
int
sentiment(: in )
class google.cloud.aiplatform.v1beta1.schema.predict.prediction_v1beta1.types.TimeSeriesForecastingPredictionResult(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
Prediction output format for Time Series Forecasting.
value()
The regression value.
Type
float
value(: floa )
class google.cloud.aiplatform.v1beta1.schema.predict.prediction_v1beta1.types.VideoActionRecognitionPredictionResult(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
Prediction output format for Video Action Recognition.
id()
The resource ID of the AnnotationSpec that had been identified.
Type
str
display_name()
The display name of the AnnotationSpec that had been identified.
Type
str
time_segment_start()
The beginning, inclusive, of the video’s time segment in which the AnnotationSpec has been identified. Expressed as a number of seconds as measured from the start of the video, with fractions up to a microsecond precision, and with “s” appended at the end.
Type
google.protobuf.duration_pb2.Duration
time_segment_end()
The end, exclusive, of the video’s time segment in which the AnnotationSpec has been identified. Expressed as a number of seconds as measured from the start of the video, with fractions up to a microsecond precision, and with “s” appended at the end.
Type
google.protobuf.duration_pb2.Duration
confidence()
The Model’s confidence in correction of this prediction, higher value means higher confidence.
Type
google.protobuf.wrappers_pb2.FloatValue
confidence(: google.protobuf.wrappers_pb2.FloatValu )
display_name(: st )
id(: st )
time_segment_end(: google.protobuf.duration_pb2.Duratio )
time_segment_start(: google.protobuf.duration_pb2.Duratio )
class google.cloud.aiplatform.v1beta1.schema.predict.prediction_v1beta1.types.VideoClassificationPredictionResult(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
Prediction output format for Video Classification.
id()
The resource ID of the AnnotationSpec that had been identified.
Type
str
display_name()
The display name of the AnnotationSpec that had been identified.
Type
str
type_()
The type of the prediction. The requested types can be configured via parameters. This will be one of
- segment-classification
- shot-classification
one-sec-interval-classification
Type
str
time_segment_start()
The beginning, inclusive, of the video’s time segment in which the AnnotationSpec has been identified. Expressed as a number of seconds as measured from the start of the video, with fractions up to a microsecond precision, and with “s” appended at the end. Note that for ‘segment-classification’ prediction type, this equals the original ‘timeSegmentStart’ from the input instance, for other types it is the start of a shot or a 1 second interval respectively.
Type
google.protobuf.duration_pb2.Duration
time_segment_end()
The end, exclusive, of the video’s time segment in which the AnnotationSpec has been identified. Expressed as a number of seconds as measured from the start of the video, with fractions up to a microsecond precision, and with “s” appended at the end. Note that for ‘segment-classification’ prediction type, this equals the original ‘timeSegmentEnd’ from the input instance, for other types it is the end of a shot or a 1 second interval respectively.
Type
google.protobuf.duration_pb2.Duration
confidence()
The Model’s confidence in correction of this prediction, higher value means higher confidence.
Type
google.protobuf.wrappers_pb2.FloatValue
confidence(: google.protobuf.wrappers_pb2.FloatValu )
display_name(: st )
id(: st )
time_segment_end(: google.protobuf.duration_pb2.Duratio )
time_segment_start(: google.protobuf.duration_pb2.Duratio )
type_(: st )
class google.cloud.aiplatform.v1beta1.schema.predict.prediction_v1beta1.types.VideoObjectTrackingPredictionResult(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
Prediction output format for Video Object Tracking.
id()
The resource ID of the AnnotationSpec that had been identified.
Type
str
display_name()
The display name of the AnnotationSpec that had been identified.
Type
str
time_segment_start()
The beginning, inclusive, of the video’s time segment in which the object instance has been detected. Expressed as a number of seconds as measured from the start of the video, with fractions up to a microsecond precision, and with “s” appended at the end.
Type
google.protobuf.duration_pb2.Duration
time_segment_end()
The end, inclusive, of the video’s time segment in which the object instance has been detected. Expressed as a number of seconds as measured from the start of the video, with fractions up to a microsecond precision, and with “s” appended at the end.
Type
google.protobuf.duration_pb2.Duration
confidence()
The Model’s confidence in correction of this prediction, higher value means higher confidence.
Type
google.protobuf.wrappers_pb2.FloatValue
frames()
All of the frames of the video in which a single object instance has been detected. The bounding boxes in the frames identify the same object.
Type
MutableSequence[google.cloud.aiplatform.v1beta1.schema.predict.prediction_v1beta1.types.VideoObjectTrackingPredictionResult.Frame]
class Frame(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Bases: proto.message.Message
The fields xMin, xMax, yMin, and yMax refer to a
bounding box, i.e. the rectangle over the video frame pinpointing
the found AnnotationSpec. The coordinates are relative to the frame
size, and the point 0,0 is in the top left of the frame.
time_offset()
A time (frame) of a video in which the object has been detected. Expressed as a number of seconds as measured from the start of the video, with fractions up to a microsecond precision, and with “s” appended at the end.
Type
google.protobuf.duration_pb2.Duration
x_min()
The leftmost coordinate of the bounding box.
Type
google.protobuf.wrappers_pb2.FloatValue
x_max()
The rightmost coordinate of the bounding box.
Type
google.protobuf.wrappers_pb2.FloatValue
y_min()
The topmost coordinate of the bounding box.
Type
google.protobuf.wrappers_pb2.FloatValue
y_max()
The bottommost coordinate of the bounding box.
Type
google.protobuf.wrappers_pb2.FloatValue