diff --git a/examples/dnn_introduction3_ex.cpp b/examples/dnn_introduction3_ex.cpp
index 2f4c0140f..3b5b0ece3 100644
--- a/examples/dnn_introduction3_ex.cpp
+++ b/examples/dnn_introduction3_ex.cpp
@@ -29,7 +29,7 @@ namespace model
     using net_type = loss_metric<
         fc_no_bias<128,
         avg_pool_everything<
-        typename resnet<BN>::template backbone_50<
+        typename resnet::def<BN>::template backbone_50<
         input_rgb_image
         >>>>;
 
@@ -75,7 +75,7 @@ int main() try
     {
         // Now, let's define the classic ResNet50 network and load the pretrained model on
         // ImageNet.
-        resnet<bn_con>::n50 resnet50;
+        resnet::train_50 resnet50;
         std::vector<string> labels;
         deserialize("resnet50_1000_imagenet_classifier.dnn") >> resnet50 >> labels;
 
diff --git a/examples/resnet.h b/examples/resnet.h
index 57e98c4ec..61759f5d5 100644
--- a/examples/resnet.h
+++ b/examples/resnet.h
@@ -3,99 +3,104 @@
 
 #include <dlib/dnn.h>
 
-// BATCHNORM must be bn_con or affine layer
-template<template<typename> class BATCHNORM>
-struct resnet
+namespace resnet
 {
-    // the resnet basic block, where BN is bn_con or affine
-    template<long num_filters, template<typename> class BN, int stride, typename SUBNET>
-    using basicblock = BN<dlib::con<num_filters, 3, 3, 1, 1,
-                  dlib::relu<BN<dlib::con<num_filters, 3, 3, stride, stride, SUBNET>>>>>;
-
-    // the resnet bottleneck block
-    template<long num_filters, template<typename> class BN, int stride, typename SUBNET>
-    using bottleneck = BN<dlib::con<4 * num_filters, 1, 1, 1, 1,
-                  dlib::relu<BN<dlib::con<num_filters, 3, 3, stride, stride,
-                  dlib::relu<BN<dlib::con<num_filters, 1, 1, 1, 1, SUBNET>>>>>>>>;
-
-    // the resnet residual
-    template<
-        template<long, template<typename> class, int, typename> class BLOCK, // basicblock or bottleneck
-        long num_filters,
-        template<typename> class BN, // bn_con or affine
-        typename SUBNET
-    > // adds the block to the result of tag1 (the subnet)
-    using residual = dlib::add_prev1<BLOCK<num_filters, BN, 1, dlib::tag1<SUBNET>>>;
-
-    // a resnet residual that does subsampling on both paths
-    template<
-        template<long, template<typename> class, int, typename> class BLOCK, // basicblock or bottleneck
-        long num_filters,
-        template<typename> class BN, // bn_con or affine
-        typename SUBNET
-    >
-    using residual_down = dlib::add_prev2<dlib::avg_pool<2, 2, 2, 2,
-                          dlib::skip1<dlib::tag2<BLOCK<num_filters, BN, 2,
-                          dlib::tag1<SUBNET>>>>>>;
-
-    // residual block with optional downsampling and custom regularization (bn_con or affine)
-    template<
-        template<template<long, template<typename> class, int, typename> class, long, template<typename>class, typename> class RESIDUAL,
-        template<long, template<typename> class, int, typename> class BLOCK,
-        long num_filters,
-        template<typename> class BN, // bn_con or affine
-        typename SUBNET
-    >
-    using residual_block = dlib::relu<RESIDUAL<BLOCK, num_filters, BN, SUBNET>>;
-
-    template<long num_filters, typename SUBNET>
-    using resbasicblock_down = residual_block<residual_down, basicblock, num_filters, BATCHNORM, SUBNET>;
-    template<long num_filters, typename SUBNET>
-    using resbottleneck_down = residual_block<residual_down, bottleneck, num_filters, BATCHNORM, SUBNET>;
-
-    // some definitions to allow the use of the repeat layer
-    template<typename SUBNET> using resbasicblock_512 = residual_block<residual, basicblock, 512, BATCHNORM, SUBNET>;
-    template<typename SUBNET> using resbasicblock_256 = residual_block<residual, basicblock, 256, BATCHNORM, SUBNET>;
-    template<typename SUBNET> using resbasicblock_128 = residual_block<residual, basicblock, 128, BATCHNORM, SUBNET>;
-    template<typename SUBNET> using resbasicblock_64  = residual_block<residual, basicblock,  64, BATCHNORM, SUBNET>;
-    template<typename SUBNET> using resbottleneck_512 = residual_block<residual, bottleneck, 512, BATCHNORM, SUBNET>;
-    template<typename SUBNET> using resbottleneck_256 = residual_block<residual, bottleneck, 256, BATCHNORM, SUBNET>;
-    template<typename SUBNET> using resbottleneck_128 = residual_block<residual, bottleneck, 128, BATCHNORM, SUBNET>;
-    template<typename SUBNET> using resbottleneck_64  = residual_block<residual, bottleneck,  64, BATCHNORM, SUBNET>;
-
-    // common processing for standard resnet inputs
-    template<template<typename> class BN, typename INPUT>
-    using input_processing = dlib::max_pool<3, 3, 2, 2, dlib::relu<BN<dlib::con<64, 7, 7, 2, 2, INPUT>>>>;
-
-    // the resnet backbone with basicblocks
-    template<long nb_512, long nb_256, long nb_128, long nb_64, typename INPUT>
-    using backbone_basicblock =
-        dlib::repeat<nb_512, resbasicblock_512, resbasicblock_down<512,
-        dlib::repeat<nb_256, resbasicblock_256, resbasicblock_down<256,
-        dlib::repeat<nb_128, resbasicblock_128, resbasicblock_down<128,
-        dlib::repeat<nb_64,  resbasicblock_64, input_processing<BATCHNORM, INPUT>>>>>>>>;
-
-    // the resnet backbone with bottlenecks
-    template<long nb_512, long nb_256, long nb_128, long nb_64, typename INPUT>
-    using backbone_bottleneck =
-        dlib::repeat<nb_512, resbottleneck_512, resbottleneck_down<512,
-        dlib::repeat<nb_256, resbottleneck_256, resbottleneck_down<256,
-        dlib::repeat<nb_128, resbottleneck_128, resbottleneck_down<128,
-        dlib::repeat<nb_64,  resbottleneck_64, input_processing<BATCHNORM, INPUT>>>>>>>>;
-
-    // the backbones for the classic architectures
-    template<typename INPUT> using backbone_18  = backbone_basicblock<1, 1, 1, 2, INPUT>;
-    template<typename INPUT> using backbone_34  = backbone_basicblock<2, 5, 3, 3, INPUT>;
-    template<typename INPUT> using backbone_50  = backbone_bottleneck<2, 5, 3, 3, INPUT>;
-    template<typename INPUT> using backbone_101 = backbone_bottleneck<2, 22, 3, 3, INPUT>;
-    template<typename INPUT> using backbone_152 = backbone_bottleneck<2, 35, 7, 3, INPUT>;
-
-    // the typical classifier models
-    using n18  = dlib::loss_multiclass_log<dlib::fc<1000, dlib::avg_pool_everything<backbone_18<dlib::input_rgb_image>>>>;
-    using n34  = dlib::loss_multiclass_log<dlib::fc<1000, dlib::avg_pool_everything<backbone_34<dlib::input_rgb_image>>>>;
-    using n50  = dlib::loss_multiclass_log<dlib::fc<1000, dlib::avg_pool_everything<backbone_50<dlib::input_rgb_image>>>>;
-    using n101 = dlib::loss_multiclass_log<dlib::fc<1000, dlib::avg_pool_everything<backbone_101<dlib::input_rgb_image>>>>;
-    using n152 = dlib::loss_multiclass_log<dlib::fc<1000, dlib::avg_pool_everything<backbone_152<dlib::input_rgb_image>>>>;
-};
+    using namespace dlib;
+    // BN is bn_con or affine layer
+    template<template<typename> class BN>
+    struct def
+    {
+        // the resnet basic block, where BN is bn_con or affine
+        template<long num_filters, int stride, typename SUBNET>
+        using basicblock = BN<con<num_filters, 3, 3, 1, 1,
+                relu<BN<con<num_filters, 3, 3, stride, stride, SUBNET>>>>>;
+
+        // the resnet bottleneck block
+        template<long num_filters, int stride, typename SUBNET>
+        using bottleneck = BN<con<4 * num_filters, 1, 1, 1, 1,
+                relu<BN<con<num_filters, 3, 3, stride, stride,
+                relu<BN<con<num_filters, 1, 1, 1, 1, SUBNET>>>>>>>>;
+
+        // the resnet residual, where BLOCK is either basicblock or bottleneck
+        template<template<long, int, typename> class BLOCK, long num_filters, typename SUBNET>
+        using residual = add_prev1<BLOCK<num_filters, 1, tag1<SUBNET>>>;
+
+        // a resnet residual that does subsampling on both paths
+        template<template<long, int, typename> class BLOCK, long num_filters, typename SUBNET>
+        using residual_down = add_prev2<avg_pool<2, 2, 2, 2,
+                              skip1<tag2<BLOCK<num_filters, 2,
+                              tag1<SUBNET>>>>>>;
+
+        // residual block with optional downsampling
+        template<
+            template<template<long, int, typename> class, long, typename> class RESIDUAL,
+            template<long, int, typename> class BLOCK,
+            long num_filters,
+            typename SUBNET
+        >
+        using residual_block = relu<RESIDUAL<BLOCK, num_filters, SUBNET>>;
+
+        template<long num_filters, typename SUBNET>
+        using resbasicblock_down = residual_block<residual_down, basicblock, num_filters, SUBNET>;
+        template<long num_filters, typename SUBNET>
+        using resbottleneck_down = residual_block<residual_down, bottleneck, num_filters, SUBNET>;
+
+        // some definitions to allow the use of the repeat layer
+        template<typename SUBNET> using resbasicblock_512 = residual_block<residual, basicblock, 512, SUBNET>;
+        template<typename SUBNET> using resbasicblock_256 = residual_block<residual, basicblock, 256, SUBNET>;
+        template<typename SUBNET> using resbasicblock_128 = residual_block<residual, basicblock, 128, SUBNET>;
+        template<typename SUBNET> using resbasicblock_64  = residual_block<residual, basicblock,  64, SUBNET>;
+        template<typename SUBNET> using resbottleneck_512 = residual_block<residual, bottleneck, 512, SUBNET>;
+        template<typename SUBNET> using resbottleneck_256 = residual_block<residual, bottleneck, 256, SUBNET>;
+        template<typename SUBNET> using resbottleneck_128 = residual_block<residual, bottleneck, 128, SUBNET>;
+        template<typename SUBNET> using resbottleneck_64  = residual_block<residual, bottleneck,  64, SUBNET>;
+
+        // common processing for standard resnet inputs
+        template<typename INPUT>
+        using input_processing = max_pool<3, 3, 2, 2, relu<BN<con<64, 7, 7, 2, 2, INPUT>>>>;
+
+        // the resnet backbone with basicblocks
+        template<long nb_512, long nb_256, long nb_128, long nb_64, typename INPUT>
+        using backbone_basicblock =
+            repeat<nb_512, resbasicblock_512, resbasicblock_down<512,
+            repeat<nb_256, resbasicblock_256, resbasicblock_down<256,
+            repeat<nb_128, resbasicblock_128, resbasicblock_down<128,
+            repeat<nb_64,  resbasicblock_64, input_processing<INPUT>>>>>>>>;
+
+        // the resnet backbone with bottlenecks
+        template<long nb_512, long nb_256, long nb_128, long nb_64, typename INPUT>
+        using backbone_bottleneck =
+            repeat<nb_512, resbottleneck_512, resbottleneck_down<512,
+            repeat<nb_256, resbottleneck_256, resbottleneck_down<256,
+            repeat<nb_128, resbottleneck_128, resbottleneck_down<128,
+            repeat<nb_64,  resbottleneck_64, input_processing<INPUT>>>>>>>>;
+
+        // the backbones for the classic architectures
+        template<typename INPUT> using backbone_18  = backbone_basicblock<1, 1, 1, 2, INPUT>;
+        template<typename INPUT> using backbone_34  = backbone_basicblock<2, 5, 3, 3, INPUT>;
+        template<typename INPUT> using backbone_50  = backbone_bottleneck<2, 5, 3, 3, INPUT>;
+        template<typename INPUT> using backbone_101 = backbone_bottleneck<2, 22, 3, 3, INPUT>;
+        template<typename INPUT> using backbone_152 = backbone_bottleneck<2, 35, 7, 3, INPUT>;
+
+        // the typical classifier models
+        using n18  = loss_multiclass_log<fc<1000, avg_pool_everything<backbone_18<input_rgb_image>>>>;
+        using n34  = loss_multiclass_log<fc<1000, avg_pool_everything<backbone_34<input_rgb_image>>>>;
+        using n50  = loss_multiclass_log<fc<1000, avg_pool_everything<backbone_50<input_rgb_image>>>>;
+        using n101 = loss_multiclass_log<fc<1000, avg_pool_everything<backbone_101<input_rgb_image>>>>;
+        using n152 = loss_multiclass_log<fc<1000, avg_pool_everything<backbone_152<input_rgb_image>>>>;
+    };
+
+    using train_18 = def<bn_con>::n18;
+    using train_34 = def<bn_con>::n34;
+    using train_50 = def<bn_con>::n50;
+    using train_101 = def<bn_con>::n101;
+    using train_152 = def<bn_con>::n152;
+
+    using infer_18 = def<affine>::n18;
+    using infer_34 = def<affine>::n34;
+    using infer_50 = def<affine>::n50;
+    using infer_101 = def<affine>::n101;
+    using infer_152 = def<affine>::n152;
+}
 
 #endif // ResNet_H