diff --git a/modules/dnn/include/opencv2/dnn/dnn.hpp b/modules/dnn/include/opencv2/dnn/dnn.hpp
index d055cadf42..15ec786d0d 100644
--- a/modules/dnn/include/opencv2/dnn/dnn.hpp
+++ b/modules/dnn/include/opencv2/dnn/dnn.hpp
@@ -545,33 +545,6 @@ CV__DNN_INLINE_NS_BEGIN
         virtual void setProg(const std::vector<Ptr<Layer> >& newprog) = 0;
     };
 
-    /** @brief Single entry in a @ref PerfProfile.
-     *
-     * In DNN_PROFILE_DETAILED mode, @p label is "layer_name (type)" and @p count is 1.
-     * In DNN_PROFILE_SUMMARY mode, @p label is the layer type and @p count is the
-     * number of layers of that type that contributed to @p timeMs.
-     */
-    struct CV_EXPORTS_W_SIMPLE PerfProfileEntry
-    {
-        CV_WRAP PerfProfileEntry() : timeMs(0.0), count(0) {}
-        CV_PROP_RW String label;
-        CV_PROP_RW double timeMs;
-        CV_PROP_RW int count;
-    };
-
-    /** @brief Self-describing snapshot of profiling data from one inference.
-     *
-     * Carries the @ref ProfilingMode it was captured in so it can be saved, kept across
-     * runs (e.g. best-of-N by total time), and printed later via @ref Net::printPerfProfile
-     * without needing access to the originating @ref Net.
-     */
-    struct CV_EXPORTS_W_SIMPLE PerfProfile
-    {
-        CV_WRAP PerfProfile() : mode(DNN_PROFILE_NONE) {}
-        CV_PROP_RW ProfilingMode mode;
-        CV_PROP_RW std::vector<PerfProfileEntry> entries;
-    };
-
     /** @brief This class allows to create and manipulate comprehensive artificial neural networks.
      *
      * Neural network is presented as directed acyclic graph (DAG), where vertices are Layer instances,
diff --git a/modules/dnn/src/net_impl.hpp b/modules/dnn/src/net_impl.hpp
index c5b3159191..b6cff99b29 100644
--- a/modules/dnn/src/net_impl.hpp
+++ b/modules/dnn/src/net_impl.hpp
@@ -48,6 +48,33 @@ typedef std::unordered_map<std::string, int64_t> NamesHash;
 struct OrtNamesCache;
 #endif
 
+/** @brief Single entry in a @ref PerfProfile.
+ *
+ * In DNN_PROFILE_DETAILED mode, @p label is "layer_name (type)" and @p count is 1.
+ * In DNN_PROFILE_SUMMARY mode, @p label is the layer type and @p count is the
+ * number of layers of that type that contributed to @p timeMs.
+ */
+struct PerfProfileEntry
+{
+    PerfProfileEntry() : timeMs(0.0), count(0) {}
+    String label;
+    double timeMs;
+    int count;
+};
+
+/** @brief Self-describing snapshot of profiling data from one inference.
+ *
+ * Carries the @ref ProfilingMode it was captured in so it can be saved, kept across
+ * runs (e.g. best-of-N by total time), and printed later via @ref Net::printPerfProfile
+ * without needing access to the originating @ref Net.
+ */
+struct PerfProfile
+{
+    PerfProfile() : mode(DNN_PROFILE_NONE) {}
+    ProfilingMode mode;
+    std::vector<PerfProfileEntry> entries;
+};
+
 // NB: Implementation is divided between of multiple .cpp files
 struct Net::Impl : public detail::NetImplBase
 {