diff --git a/bob/paper/mccnn/tifs2018/config/FASNet_config.py b/bob/paper/mccnn/tifs2018/config/FASNet_config.py
index 861bbd3016d700ab7fbe220607678d8e998bd60c..86978a92822c2744fb4d70bfef6f833dface8377 100644
--- a/bob/paper/mccnn/tifs2018/config/FASNet_config.py
+++ b/bob/paper/mccnn/tifs2018/config/FASNet_config.py
@@ -96,24 +96,31 @@ from bob.learn.pytorch.extractor.image import FASNetExtractor
 
 from bob.bio.video.extractor import Wrapper
 
-# MODEL_FILE= None # Replace with '<PATH_TO_MODEL>'
-# ####################################################################
-
 # If you want to use the pretrained model
 
-import pkg_resources
+USE_PRETRAINED_MODEL=True
+
+if USE_PRETRAINED_MODEL:
+
+  import pkg_resources
+
+  MODEL_FILE = pkg_resources.resource_filename('bob.paper.mccnn.tifs2018', 'models/fasnet.pth')
+
+  URL = 'https://www.idiap.ch/software/bob/data/bob/bob.paper.mccnn.tifs2018/master/fasnet.pth'
+
+  if not os.path.exists(MODEL_FILE):
+
+      logger.info('Downloading the FASNet model')
 
-MODEL_FILE = pkg_resources.resource_filename('bob.paper.mccnn.tifs2018', 'models/fasnet.pth')
+      bob.extension.download.download_file(URL, MODEL_FILE)
 
-URL = 'https://www.idiap.ch/software/bob/data/bob/bob.paper.mccnn.tifs2018/master/fasnet.pth'
+      logger.info('Downloaded FASNet model to location: {}'.format(MODEL_FILE))
 
-if not os.path.exists(MODEL_FILE):
+else:
 
-    logger.info('Downloading the FASNet model')
+  MODEL_FILE= None # Replace with '<PATH_TO_MODEL>'
 
-    bob.extension.download.download_file(URL, MODEL_FILE)
 
-    logger.info('Downloaded FASNet model to location: {}'.format(MODEL_FILE))
 
 
 _img_transform = transforms.Compose([transforms.ToPILImage(), transforms.Resize(224, interpolation=2), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406],
diff --git a/bob/paper/mccnn/tifs2018/config/MCCNN_config.py b/bob/paper/mccnn/tifs2018/config/MCCNN_config.py
index b36b5b7ca4b51db19eac46730e50c5dc28cab3b5..93efd159ad4c505d5ee0832fad7ab26d6fe8706a 100644
--- a/bob/paper/mccnn/tifs2018/config/MCCNN_config.py
+++ b/bob/paper/mccnn/tifs2018/config/MCCNN_config.py
@@ -15,6 +15,8 @@ from torchvision import transforms
 
 from bob.learn.pytorch.datasets import ChannelSelect
 
+import os
+
 # names of the channels to process:
 _channel_names = ['color', 'depth', 'infrared', 'thermal']
 
@@ -92,24 +94,32 @@ from bob.learn.pytorch.extractor.image import MCCNNExtractor
 
 from bob.bio.video.extractor import Wrapper
 
-# MODEL_FILE= None # Replace with '<PATH_TO_MODEL>'
-# ####################################################################
+
 
 # If you want to use the pretrained model
 
-import pkg_resources
+USE_PRETRAINED_MODEL=True
+
+if USE_PRETRAINED_MODEL:
+
+  import pkg_resources
+
+  MODEL_FILE = pkg_resources.resource_filename('bob.paper.mccnn.tifs2018', 'models/mccnn_best_C1-B1-FFC.pth')
+
+  URL = 'http://www.idiap.ch/software/bob/data/bob/bob.paper.mccnn.tifs2018/master/mccnn_best_C1-B1-FFC.pth'
+
+  if not os.path.exists(MODEL_FILE):
 
-MODEL_FILE = pkg_resources.resource_filename('bob.paper.mccnn.tifs2018', 'models/mccnn_best_C1-B1-FFC.pth')
+      logger.info('Downloading the MCCNN model')
 
-URL = 'http://www.idiap.ch/software/bob/data/bob/bob.paper.mccnn.tifs2018/master/mccnn_best_C1-B1-FFC.pth'
+      bob.extension.download.download_file(URL, MODEL_FILE)
 
-if not os.path.exists(MODEL_FILE):
+      logger.info('Downloaded MCCNN model to location: {}'.format(MODEL_FILE))
+else:
 
-    logger.info('Downloading the MCCNN model')
+  MODEL_FILE= None # Replace with '<PATH_TO_MODEL>'
 
-    bob.extension.download.download_file(URL, MODEL_FILE)
 
-    logger.info('Downloaded MCCNN model to location: {}'.format(MODEL_FILE))
 
 
 ADAPTED_LAYERS = 'conv1-group1-ffc'