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syntax = "proto2";
package object_detection.protos;
// Configuration for Feature Pyramid Networks.
message FeaturePyramidNetworks {
// We recommend to use multi_resolution_feature_map_generator with FPN, and
// the levels there must match the levels defined below for better
// performance.
// Correspondence from FPN levels to Resnet/Mobilenet V1 feature maps:
// FPN Level Resnet Feature Map Mobilenet-V1 Feature Map
// 2 Block 1 Conv2d_3_pointwise
// 3 Block 2 Conv2d_5_pointwise
// 4 Block 3 Conv2d_11_pointwise
// 5 Block 4 Conv2d_13_pointwise
// 6 Bottomup_5 bottom_up_Conv2d_14
// 7 Bottomup_6 bottom_up_Conv2d_15
// 8 Bottomup_7 bottom_up_Conv2d_16
// 9 Bottomup_8 bottom_up_Conv2d_17
// minimum level in feature pyramid
optional int32 min_level = 1 [default = 3];
// maximum level in feature pyramid
optional int32 max_level = 2 [default = 7];
// channel depth for additional coarse feature layers.
optional int32 additional_layer_depth = 3 [default = 256];
}
// Configuration for Bidirectional Feature Pyramid Networks.
message BidirectionalFeaturePyramidNetworks {
// minimum level in the feature pyramid.
optional int32 min_level = 1 [default = 3];
// maximum level in the feature pyramid.
optional int32 max_level = 2 [default = 7];
// The number of repeated top-down bottom-up iterations for BiFPN-based
// feature extractors (bidirectional feature pyramid networks).
optional int32 num_iterations = 3;
// The number of filters (channels) to use in feature pyramid layers for
// BiFPN-based feature extractors (bidirectional feature pyramid networks).
optional int32 num_filters = 4;
// Method used to combine inputs to BiFPN nodes.
optional string combine_method = 5 [default = 'fast_attention'];
// If true, will use tf.compat.v1.image.resize_nearest_neighbor for
// upsampling
optional bool use_native_resize_op = 6;
}