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// Copyright 2020 Google LLC
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
syntax = "proto3";
package google.cloud.automl.v1beta1;
import "google/cloud/automl/v1beta1/geometry.proto";
import "google/protobuf/duration.proto";
option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1beta1;automl";
option java_multiple_files = true;
option java_package = "com.google.cloud.automl.v1beta1";
option php_namespace = "Google\\Cloud\\AutoMl\\V1beta1";
option ruby_package = "Google::Cloud::AutoML::V1beta1";
// Annotation details for image object detection.
message ImageObjectDetectionAnnotation {
// Output only. The rectangle representing the object location.
BoundingPoly bounding_box = 1;
// Output only. The confidence that this annotation is positive for the parent example,
// value in [0, 1], higher means higher positivity confidence.
float score = 2;
}
// Annotation details for video object tracking.
message VideoObjectTrackingAnnotation {
// 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.
string instance_id = 1;
// Required. A time (frame) of a video to which this annotation pertains.
// Represented as the duration since the video's start.
google.protobuf.Duration time_offset = 2;
// Required. The rectangle representing the object location on the frame (i.e.
// at the time_offset of the video).
BoundingPoly bounding_box = 3;
// 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).
float score = 4;
}
// Bounding box matching model metrics for a single intersection-over-union
// threshold and multiple label match confidence thresholds.
message BoundingBoxMetricsEntry {
// Metrics for a single confidence threshold.
message ConfidenceMetricsEntry {
// Output only. The confidence threshold value used to compute the metrics.
float confidence_threshold = 1;
// Output only. Recall under the given confidence threshold.
float recall = 2;
// Output only. Precision under the given confidence threshold.
float precision = 3;
// Output only. The harmonic mean of recall and precision.
float f1_score = 4;
}
// Output only. The intersection-over-union threshold value used to compute
// this metrics entry.
float iou_threshold = 1;
// Output only. The mean average precision, most often close to au_prc.
float mean_average_precision = 2;
// 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.
repeated ConfidenceMetricsEntry confidence_metrics_entries = 3;
}
// Model evaluation metrics for image object detection problems.
// Evaluates prediction quality of labeled bounding boxes.
message ImageObjectDetectionEvaluationMetrics {
// Output only. The total number of bounding boxes (i.e. summed over all
// images) the ground truth used to create this evaluation had.
int32 evaluated_bounding_box_count = 1;
// 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.
repeated BoundingBoxMetricsEntry bounding_box_metrics_entries = 2;
// Output only. The single metric for bounding boxes evaluation:
// the mean_average_precision averaged over all bounding_box_metrics_entries.
float bounding_box_mean_average_precision = 3;
}
// 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).
message VideoObjectTrackingEvaluationMetrics {
// Output only. The number of video frames used to create this evaluation.
int32 evaluated_frame_count = 1;
// Output only. The total number of bounding boxes (i.e. summed over all
// frames) the ground truth used to create this evaluation had.
int32 evaluated_bounding_box_count = 2;
// 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.
repeated BoundingBoxMetricsEntry bounding_box_metrics_entries = 4;
// Output only. The single metric for bounding boxes evaluation:
// the mean_average_precision averaged over all bounding_box_metrics_entries.
float bounding_box_mean_average_precision = 6;
}
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