diff --git a/pl_examples/domain_templates/computer_vision_fine_tuning.py b/pl_examples/domain_templates/computer_vision_fine_tuning.py
index 4371c86945..e2db1b98fd 100644
--- a/pl_examples/domain_templates/computer_vision_fine_tuning.py
+++ b/pl_examples/domain_templates/computer_vision_fine_tuning.py
@@ -450,5 +450,4 @@ def get_args() -> argparse.Namespace:
 
 
 if __name__ == '__main__':
-
     main(get_args())
diff --git a/pl_examples/domain_templates/generative_adversarial_net.py b/pl_examples/domain_templates/generative_adversarial_net.py
index 68197e32b8..4417e5e02c 100644
--- a/pl_examples/domain_templates/generative_adversarial_net.py
+++ b/pl_examples/domain_templates/generative_adversarial_net.py
@@ -7,7 +7,7 @@ After a few epochs, launch TensorBoard to see the images being generated at ever
 tensorboard --logdir default
 """
 import os
-from argparse import ArgumentParser
+from argparse import ArgumentParser, Namespace
 from collections import OrderedDict
 
 import numpy as np
@@ -183,7 +183,7 @@ class GAN(LightningModule):
         self.logger.experiment.add_image('generated_images', grid, self.current_epoch)
 
 
-def main(args):
+def main(args: Namespace) -> None:
     # ------------------------
     # 1 INIT LIGHTNING MODEL
     # ------------------------
diff --git a/pl_examples/domain_templates/imagenet.py b/pl_examples/domain_templates/imagenet.py
index e6584d7655..19a85b8794 100644
--- a/pl_examples/domain_templates/imagenet.py
+++ b/pl_examples/domain_templates/imagenet.py
@@ -1,7 +1,7 @@
 """
 This example is largely adapted from https://github.com/pytorch/examples/blob/master/imagenet/main.py
 """
-import argparse
+from argparse import ArgumentParser, Namespace
 import os
 import random
 from collections import OrderedDict
@@ -183,7 +183,7 @@ class ImageNetLightningModel(LightningModule):
 
     @staticmethod
     def add_model_specific_args(parent_parser):  # pragma: no-cover
-        parser = argparse.ArgumentParser(parents=[parent_parser])
+        parser = ArgumentParser(parents=[parent_parser])
         parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet18', choices=MODEL_NAMES,
                             help='model architecture: ' +
                                  ' | '.join(MODEL_NAMES) +
@@ -210,7 +210,7 @@ class ImageNetLightningModel(LightningModule):
 
 
 def get_args():
-    parent_parser = argparse.ArgumentParser(add_help=False)
+    parent_parser = ArgumentParser(add_help=False)
     parent_parser.add_argument('--data-path', metavar='DIR', type=str,
                                help='path to dataset')
     parent_parser.add_argument('--save-path', metavar='DIR', default=".", type=str,
@@ -228,20 +228,23 @@ def get_args():
     return parser.parse_args()
 
 
-def main(hparams):
-    model = ImageNetLightningModel(hparams)
-    if hparams.seed is not None:
-        random.seed(hparams.seed)
-        torch.manual_seed(hparams.seed)
+def main(args: Namespace) -> None:
+    model = ImageNetLightningModel(**vars(args))
+
+    if args.seed is not None:
+        random.seed(args.seed)
+        torch.manual_seed(args.seed)
         cudnn.deterministic = True
+
     trainer = pl.Trainer(
-        default_root_dir=hparams.save_path,
-        gpus=hparams.gpus,
-        max_epochs=hparams.epochs,
-        distributed_backend=hparams.distributed_backend,
-        precision=16 if hparams.use_16bit else 32,
+        default_root_dir=args.save_path,
+        gpus=args.gpus,
+        max_epochs=args.epochs,
+        distributed_backend=args.distributed_backend,
+        precision=16 if args.use_16bit else 32,
     )
-    if hparams.evaluate:
+
+    if args.evaluate:
         trainer.run_evaluation()
     else:
         trainer.fit(model)