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syntax = "proto2";
package object_detection.protos;
import "object_detection/protos/calibration.proto";
// Configuration proto for non-max-suppression operation on a batch of
// detections.
message BatchNonMaxSuppression {
// Scalar threshold for score (low scoring boxes are removed).
optional float score_threshold = 1 [default = 0.0];
// Scalar threshold for IOU (boxes that have high IOU overlap
// with previously selected boxes are removed).
optional float iou_threshold = 2 [default = 0.6];
// Maximum number of detections to retain per class.
optional int32 max_detections_per_class = 3 [default = 100];
// Maximum number of detections to retain across all classes.
optional int32 max_total_detections = 5 [default = 100];
// Whether to use the implementation of NMS that guarantees static shapes.
optional bool use_static_shapes = 6 [default = false];
// Whether to use class agnostic NMS.
// Class-agnostic NMS function implements a class-agnostic version
// of Non Maximal Suppression where if max_classes_per_detection=k,
// 1) we keep the top-k scores for each detection and
// 2) during NMS, each detection only uses the highest class score for sorting.
// 3) Compared to regular NMS, the worst runtime of this version is O(N^2)
// instead of O(KN^2) where N is the number of detections and K the number of
// classes.
optional bool use_class_agnostic_nms = 7 [default = false];
// Number of classes retained per detection in class agnostic NMS.
optional int32 max_classes_per_detection = 8 [default = 1];
// Soft NMS sigma parameter; Bodla et al, https://arxiv.org/abs/1704.04503)
optional float soft_nms_sigma = 9 [default = 0.0];
// Whether to use partitioned version of non_max_suppression.
optional bool use_partitioned_nms = 10 [default = false];
// Whether to use tf.image.combined_non_max_suppression.
optional bool use_combined_nms = 11 [default = false];
// Whether to change coordinate frame of the boxlist to be relative to
// window's frame.
optional bool change_coordinate_frame = 12 [default = true];
// Use hard NMS. Note that even if this field is set false, the behavior of
// NMS will be equivalent to hard NMS; This field when set to true forces the
// tf.image.non_max_suppression function to be called instead
// of tf.image.non_max_suppression_with_scores and can be used to
// export models for older versions of TF.
optional bool use_hard_nms = 13 [default = false];
}
// Configuration proto for post-processing predicted boxes and
// scores.
message PostProcessing {
// Non max suppression parameters.
optional BatchNonMaxSuppression batch_non_max_suppression = 1;
// Enum to specify how to convert the detection scores.
enum ScoreConverter {
// Input scores equals output scores.
IDENTITY = 0;
// Applies a sigmoid on input scores.
SIGMOID = 1;
// Applies a softmax on input scores
SOFTMAX = 2;
}
// Score converter to use.
optional ScoreConverter score_converter = 2 [default = IDENTITY];
// Scale logit (input) value before conversion in post-processing step.
// Typically used for softmax distillation, though can be used to scale for
// other reasons.
optional float logit_scale = 3 [default = 1.0];
// Calibrate score outputs. Calibration is applied after score converter
// and before non max suppression.
optional CalibrationConfig calibration_config = 4;
}
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