diff --git a/utils/torch_utils.py b/utils/torch_utils.py
index 7eea4b4..e069792 100644
--- a/utils/torch_utils.py
+++ b/utils/torch_utils.py
@@ -7,6 +7,7 @@ import torch
 import torch.backends.cudnn as cudnn
 import torch.nn as nn
 import torch.nn.functional as F
+import torchvision.models as models
 
 
 def init_seeds(seed=0):
@@ -120,18 +121,22 @@ def model_info(model, verbose=False):
 
 def load_classifier(name='resnet101', n=2):
     # Loads a pretrained model reshaped to n-class output
-    import pretrainedmodels  # https://github.com/Cadene/pretrained-models.pytorch#torchvision
-    model = pretrainedmodels.__dict__[name](num_classes=1000, pretrained='imagenet')
+    model = models.__dict__[name](pretrained=True)
 
     # Display model properties
-    for x in ['model.input_size', 'model.input_space', 'model.input_range', 'model.mean', 'model.std']:
+    input_size = [3, 224, 224]
+    input_space = 'RGB'
+    input_range = [0, 1]
+    mean = [0.485, 0.456, 0.406]
+    std = [0.229, 0.224, 0.225]
+    for x in [input_size, input_space, input_range, mean, std]:
         print(x + ' =', eval(x))
 
     # Reshape output to n classes
-    filters = model.last_linear.weight.shape[1]
-    model.last_linear.bias = torch.nn.Parameter(torch.zeros(n))
-    model.last_linear.weight = torch.nn.Parameter(torch.zeros(n, filters))
-    model.last_linear.out_features = n
+    filters = model.fc.weight.shape[1]
+    model.fc.bias = torch.nn.Parameter(torch.zeros(n), requires_grad=True)
+    model.fc.weight = torch.nn.Parameter(torch.zeros(n, filters), requires_grad=True)
+    model.fc.out_features = n
     return model