syntax = "proto2";

package object_detection.protos;

// Message for configuring DetectionModel evaluation jobs (eval.py).
message EvalConfig {
  optional uint32 batch_size = 25 [default=1];
  // Number of visualization images to generate.
  optional uint32 num_visualizations = 1 [default=10];

  // Number of examples to process of evaluation.
  optional uint32 num_examples = 2 [default=5000, deprecated=true];

  // How often to run evaluation.
  optional uint32 eval_interval_secs = 3 [default=300];

  // Maximum number of times to run evaluation. If set to 0, will run forever.
  optional uint32 max_evals = 4 [default=0, deprecated=true];

  // Whether the TensorFlow graph used for evaluation should be saved to disk.
  optional bool save_graph = 5 [default=false];

  // Path to directory to store visualizations in. If empty, visualization
  // images are not exported (only shown on Tensorboard).
  optional string visualization_export_dir = 6 [default=""];

  // BNS name of the TensorFlow master.
  optional string eval_master = 7 [default=""];

  // Type of metrics to use for evaluation.
  repeated string metrics_set = 8;

  // Path to export detections to COCO compatible JSON format.
  optional string export_path = 9 [default=''];

  // Option to not read groundtruth labels and only export detections to
  // COCO-compatible JSON file.
  optional bool ignore_groundtruth = 10 [default=false];

  // Use exponential moving averages of variables for evaluation.
  // TODO(rathodv): When this is false make sure the model is constructed
  // without moving averages in restore_fn.
  optional bool use_moving_averages = 11 [default=false];

  // Whether to evaluate instance masks.
  // Note that since there is no evaluation code currently for instance
  // segmenation this option is unused.
  optional bool eval_instance_masks = 12 [default=false];

  // Minimum score threshold for a detected object box to be visualized
  optional float min_score_threshold = 13 [default=0.5];

  // Maximum number of detections to visualize
  optional int32 max_num_boxes_to_visualize = 14 [default=20];

  // When drawing a single detection, each label is by default visualized as
  // <label name> : <label score>. One can skip the name or/and score using the
  // following fields:
  optional bool skip_scores = 15 [default=false];
  optional bool skip_labels = 16 [default=false];

  // Whether to show groundtruth boxes in addition to detected boxes in
  // visualizations.
  optional bool visualize_groundtruth_boxes = 17 [default=false];

  // Box color for visualizing groundtruth boxes.
  optional string groundtruth_box_visualization_color = 18 [default="black"];

  // Whether to keep image identifier in filename when exported to
  // visualization_export_dir.
  optional bool keep_image_id_for_visualization_export = 19 [default=false];

  // Whether to retain original images (i.e. not pre-processed) in the tensor
  // dictionary, so that they can be displayed in Tensorboard.
  optional bool retain_original_images = 23 [default=true];

  // If True, additionally include per-category metrics.
  optional bool include_metrics_per_category = 24 [default=false];

  // Recall range within which precision should be computed.
  optional float recall_lower_bound = 26 [default = 0.0];
  optional float recall_upper_bound = 27 [default = 1.0];
}
