diff --git a/examples/dnn_instance_segmentation_train_ex.cpp b/examples/dnn_instance_segmentation_train_ex.cpp
index 633da585d..1f3ef9d92 100644
--- a/examples/dnn_instance_segmentation_train_ex.cpp
+++ b/examples/dnn_instance_segmentation_train_ex.cpp
@@ -312,7 +312,8 @@ matrix<uint16_t> keep_only_current_instance(const matrix<rgb_pixel>& rgb_label_i
 seg_bnet_type train_segmentation_network(
     const std::vector<image_info>& listing,
     const std::vector<std::vector<truth_instance>>& truth_instances,
-    unsigned int seg_minibatch_size
+    unsigned int seg_minibatch_size,
+    const std::string& classlabel
 )
 {
     seg_bnet_type seg_net;
@@ -321,10 +322,15 @@ seg_bnet_type train_segmentation_network(
     const double weight_decay = 0.0001;
     const double momentum = 0.9;
 
+    const std::string synchronization_file_name
+        = "pascal_voc2012_seg_trainer_state_file"
+        + (classlabel.empty() ? "" : ("_" + classlabel))
+        + ".dat";
+
     dnn_trainer<seg_bnet_type> seg_trainer(seg_net, sgd(weight_decay, momentum));
     seg_trainer.be_verbose();
     seg_trainer.set_learning_rate(initial_learning_rate);
-    seg_trainer.set_synchronization_file("pascal_voc2012_seg_trainer_state_file.dat", std::chrono::minutes(10));
+    seg_trainer.set_synchronization_file(synchronization_file_name, std::chrono::minutes(10));
     seg_trainer.set_iterations_without_progress_threshold(5000);
     set_all_bn_running_stats_window_sizes(seg_net, 1000);
 
@@ -626,10 +632,10 @@ int main(int argc, char** argv) try
 
     filter_listing(listing, truth_instances, desired_classlabels);
 
-    cout << "images in dataset filtered by class: " << listing.size() << endl << endl;
+    cout << "images in dataset filtered by class: " << listing.size() << endl;
 
     // First train a detection network (loss_mmod), and then a mask segmentation network (loss_log_per_pixel)
-    cout << "Training detector network:" << endl;
+    cout << endl << "Training detector network:" << endl;
     const auto det_net = train_detection_network    (listing, truth_instances, det_minibatch_size);
 
     std::map<std::string, seg_bnet_type> seg_nets_by_class;
@@ -644,16 +650,16 @@ int main(int argc, char** argv) try
             auto truth_instances_for_classlabel = truth_instances;
             filter_listing(listing_for_classlabel, truth_instances_for_classlabel, { classlabel });
 
-            cout << "Training segmentation network for class " << classlabel << ":" << endl;
+            cout << endl << "Training segmentation network for class " << classlabel << ":" << endl;
             seg_nets_by_class[classlabel] = train_segmentation_network(
-                listing_for_classlabel, truth_instances_for_classlabel, seg_minibatch_size
+                listing_for_classlabel, truth_instances_for_classlabel, seg_minibatch_size, classlabel
             );
         }
     }
     else
     {
         cout << "Training a single segmentation network:" << endl;
-        seg_nets_by_class[""] = train_segmentation_network(listing, truth_instances, seg_minibatch_size);
+        seg_nets_by_class[""] = train_segmentation_network(listing, truth_instances, seg_minibatch_size, "");
     }
 
     cout << "Saving networks" << endl;