diff --git a/utils/utils.py b/utils/utils.py
index 156da436..6a33e5f2 100755
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -37,16 +37,17 @@ def load_classes(path):
     return list(filter(None, names))  # filter removes empty strings (such as last line)
 
 
-def model_info(model):
+def model_info(model, report='summary'):
     # Plots a line-by-line description of a PyTorch model
     n_p = sum(x.numel() for x in model.parameters())  # number parameters
     n_g = sum(x.numel() for x in model.parameters() if x.requires_grad)  # number gradients
-    print('%5s %40s %9s %12s %20s %10s %10s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma'))
-    for i, (name, p) in enumerate(model.named_parameters()):
-        name = name.replace('module_list.', '')
-        print('%5g %40s %9s %12g %20s %10.3g %10.3g' % (
-            i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
-    print('Model Summary: %g layers, %g parameters, %g gradients' % (i + 1, n_p, n_g))
+    if report is 'full':
+        print('%5s %40s %9s %12s %20s %10s %10s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma'))
+        for i, (name, p) in enumerate(model.named_parameters()):
+            name = name.replace('module_list.', '')
+            print('%5g %40s %9s %12g %20s %10.3g %10.3g' %
+                  (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
+    print('Model Summary: %g layers, %g parameters, %g gradients' % (len(list(model.parameters())), n_p, n_g))
 
 
 def labels_to_class_weights(labels, nc=80):
@@ -61,12 +62,12 @@ def labels_to_class_weights(labels, nc=80):
 
 
 def coco_class_weights():  # frequency of each class in coco train2014
-    weights = 1 / torch.FloatTensor(
-        [187437, 4955, 30920, 6033, 3838, 4332, 3160, 7051, 7677, 9167, 1316, 1372, 833, 6757, 7355, 3302, 3776, 4671,
+    n = [187437, 4955, 30920, 6033, 3838, 4332, 3160, 7051, 7677, 9167, 1316, 1372, 833, 6757, 7355, 3302, 3776, 4671,
          6769, 5706, 3908, 903, 3686, 3596, 6200, 7920, 8779, 4505, 4272, 1862, 4698, 1962, 4403, 6659, 2402, 2689,
          4012, 4175, 3411, 17048, 5637, 14553, 3923, 5539, 4289, 10084, 7018, 4314, 3099, 4638, 4939, 5543, 2038, 4004,
          5053, 4578, 27292, 4113, 5931, 2905, 11174, 2873, 4036, 3415, 1517, 4122, 1980, 4464, 1190, 2302, 156, 3933,
-         1877, 17630, 4337, 4624, 1075, 3468, 135, 1380])
+         1877, 17630, 4337, 4624, 1075, 3468, 135, 1380]
+    weights = 1 / torch.Tensor(n)
     weights /= weights.sum()
     return weights