diff --git a/pysegcnn/core/graphics.py b/pysegcnn/core/graphics.py
index 82badfc54bbdb776a2be85ff4d56ae6e7664f6d1..2eeaa30c41717053b5712133bfcc4b20dad7c28c 100644
--- a/pysegcnn/core/graphics.py
+++ b/pysegcnn/core/graphics.py
@@ -481,7 +481,7 @@ def plot_loss(state_file, figsize=(10, 10), step=5,
     # load the model state
     state_file = pathlib.Path(state_file)
     model_state = torch.load(state_file)
-    LOGGER.info('Plot model statistics: {}'.format(state_file.stem))
+    LOGGER.info('Plot model statistics: {}'.format(state_file.name))
 
     # get all non-zero elements, i.e. get number of epochs trained before
     # early stop
diff --git a/pysegcnn/core/models.py b/pysegcnn/core/models.py
index d37daca54a6ee9eb52f3c5b252d7fc67d0970eea..30703939b7161fea580896a76466b01844ce9ee3 100644
--- a/pysegcnn/core/models.py
+++ b/pysegcnn/core/models.py
@@ -197,7 +197,6 @@ class Network(nn.Module):
         state_file = pathlib.Path(check_filename_length(state_file))
         if not state_file.exists():
             raise FileNotFoundError('{} does not exist.'.format(state_file))
-        LOGGER.info('Loading pretrained weights from: {}'.format(state_file))
 
         # load the model state
         model_state = torch.load(state_file)
@@ -285,6 +284,8 @@ class Network(nn.Module):
                                    SupportedOptimizers)
 
         # load the pretrained model configuration
+        LOGGER.info('Loading pretrained weights from: {}'
+                    .format(state_file.name))
         model_state = Network.load(state_file)
 
         # instanciate the pretrained model architecture