diff --git a/src/twomartens/masterthesis/cli.py b/src/twomartens/masterthesis/cli.py
index 59ce0f1..223e4f6 100644
--- a/src/twomartens/masterthesis/cli.py
+++ b/src/twomartens/masterthesis/cli.py
@@ -85,13 +85,55 @@ def _ssd_train(args: argparse.Namespace) -> None:
     del file_names_train, instances_train, file_names_val, instances_val
     
     if args.debug:
+        from matplotlib import pyplot
+        import numpy as np
+        from PIL import Image
+        
+        from twomartens.masterthesis.ssd_keras.eval_utils import coco_utils
+        
         train_data = next(train_generator)
         train_length -= batch_size
-        from PIL import Image
         train_images = train_data[0]
+        train_labels = train_data[1]
+
+        annotation_file_train = f"{args.coco_path}/annotations/instances_train2014.json"
+        _, _, _, classes_to_names = coco_utils.get_coco_category_maps(annotation_file_train)
+        colors = pyplot.cm.hsv(np.linspace(0, 1, 81)).tolist()
+
+        nr_images = len(train_images)
+        nr_digits = math.ceil(math.log10(nr_images))
+        
         for i, train_image in enumerate(train_images):
+            instances = train_labels[i]
             image = Image.fromarray(train_image)
-            image.save(f"{args.summary_path}/train/{args.network}/{args.iteration}/train_image{i}.png")
+            image.save(f"{args.summary_path}/train/{args.network}/{args.iteration}/"
+                       f"train_image{str(i).zfill(nr_digits)}.png")
+            
+            if not instances:
+                continue
+
+            figure = pyplot.figure(figsize=(20, 12))
+            pyplot.imshow(image)
+
+            current_axis = pyplot.gca()
+
+            for instance in instances:
+                xmin = instance[-12] * image_size
+                ymin = instance[-11] * image_size
+                xmax = instance[-10] * image_size
+                ymax = instance[-9] * image_size
+                class_id = np.argmax(instance[:-12])
+                print(class_id)
+                color = colors[class_id]
+                label = f"{classes_to_names[class_id]}"
+                current_axis.add_patch(
+                    pyplot.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin, color=color, fill=False,
+                                     linewidth=2))
+                current_axis.text(xmin, ymin, label, size='x-large', color='white',
+                                  bbox={'facecolor': color, 'alpha': 1.0})
+            pyplot.savefig(f"{args.summary_path}/train/{args.network}/{args.iteration}/bboxes{str(i).zfill(nr_digits)}.png")
+            pyplot.close(figure)
+    
     
     nr_batches_train = int(math.floor(train_length / batch_size))
     nr_batches_val = int(math.floor(val_length / batch_size))