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']; }