diff --git a/examples/dnn_instance_segmentation_train_ex.cpp b/examples/dnn_instance_segmentation_train_ex.cpp
index 619d4bb1a..99187bb43 100644
--- a/examples/dnn_instance_segmentation_train_ex.cpp
+++ b/examples/dnn_instance_segmentation_train_ex.cpp
@@ -528,21 +528,54 @@ std::vector<std::vector<truth_instance>> load_all_truth_instances(const std::vec
 
 // ----------------------------------------------------------------------------------------
 
+void filter_listing(
+    std::vector<image_info>& listing,
+    std::vector<std::vector<truth_instance>>& truth_instances,
+    const std::string& desired_classlabel
+)
+{
+    DLIB_CASSERT(listing.size() == truth_instances.size());
+
+    std::vector<image_info> filtered_listing;
+    std::vector<std::vector<truth_instance>> filtered_truth_instances;
+
+    const auto represents_desired_class = [desired_classlabel](const truth_instance& truth_instance) {
+        return truth_instance.mmod_rect.label == desired_classlabel;
+    };
+
+    for (int i = 0, end = listing.size(); i < end; ++i)
+    {
+        const auto has_desired_class = std::any_of(
+            truth_instances[i].begin(),
+            truth_instances[i].end(),
+            represents_desired_class
+        );
+
+        if (has_desired_class) {
+            filtered_listing.push_back(listing[i]);
+            filtered_truth_instances.push_back(truth_instances[i]);
+        }
+    }
+
+    std::swap(listing, filtered_listing);
+    std::swap(truth_instances, filtered_truth_instances);
+}
+
 int main(int argc, char** argv) try
 {
-    if (argc < 2 || argc > 4)
+    if (argc < 2 || argc > 5)
     {
         cout << "To run this program you need a copy of the PASCAL VOC2012 dataset." << endl;
         cout << endl;
         cout << "You call this program like this: " << endl;
-        cout << "./dnn_instance_segmentation_train_ex /path/to/VOC2012 [det-minibatch-size] [seg-minibatch-size]" << endl;
+        cout << "./dnn_instance_segmentation_train_ex /path/to/VOC2012 [det-minibatch-size] [seg-minibatch-size] [class-label]" << endl;
         return 1;
     }
 
     cout << "\nSCANNING PASCAL VOC2012 DATASET\n" << endl;
 
-    const auto listing = get_pascal_voc2012_train_listing(argv[1]);
-    cout << "images in dataset: " << listing.size() << endl;
+    auto listing = get_pascal_voc2012_train_listing(argv[1]);
+    cout << "images in entire dataset: " << listing.size() << endl;
     if (listing.size() == 0)
     {
         cout << "Didn't find the VOC2012 dataset. " << endl;
@@ -550,16 +583,23 @@ int main(int argc, char** argv) try
     }
 
     // mini-batches smaller than the default can be used with GPUs having less memory
-    const unsigned int det_minibatch_size = argc >= 3 ? std::stoi(argv[2]) : 40;
+    const unsigned int det_minibatch_size = argc >= 3 ? std::stoi(argv[2]) : 60;
     const unsigned int seg_minibatch_size = argc >= 4 ? std::stoi(argv[3]) : 25;
     cout << "det mini-batch size: " << det_minibatch_size << endl;
     cout << "seg mini-batch size: " << seg_minibatch_size << endl;
 
+    const std::string desired_classlabel = argc >= 5 ? argv[4] : "sheep";
+    cout << "desired classlabel: " << desired_classlabel << endl;
+
     // extract the MMOD rects
-    cout << "\nExtracting all truth instances...";
-    const auto truth_instances = load_all_truth_instances(listing);
+    cout << endl << "Extracting all truth instances...";
+    auto truth_instances = load_all_truth_instances(listing);
     cout << " Done!" << endl << endl;
 
+    filter_listing(listing, truth_instances, desired_classlabel);
+
+    cout << "images in dataset filtered by class: " << listing.size() << endl << endl;
+
     // First train a detection network (loss_mmod), and then a mask segmentation network (loss_log_per_pixel)
     const auto det_net = train_detection_network    (listing, truth_instances, det_minibatch_size);
     const auto seg_net = train_segmentation_network (listing, truth_instances, seg_minibatch_size);