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• 0.139.0
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• detection.proto

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google.cloud.automl.v1beta1.detection.proto Maven / Gradle / Ivy

// Copyright 2024 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 = "cloud.google.com/go/automl/apiv1beta1/automlpb;automlpb";
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;
}