diff --git a/detect.py b/detect.py
index d4ad635e..06083988 100644
--- a/detect.py
+++ b/detect.py
@@ -36,7 +36,6 @@ def detect(save_img=False):
 
     # Fuse Conv2d + BatchNorm2d layers
     # model.fuse()
-    # torch_utils.model_info(model, report='summary')  # 'full' or 'summary'
 
     # Eval mode
     model.to(device).eval()
diff --git a/models.py b/models.py
index 69618066..04224ac0 100755
--- a/models.py
+++ b/models.py
@@ -240,6 +240,7 @@ class Darknet(nn.Module):
         # Darknet Header https://github.com/AlexeyAB/darknet/issues/2914#issuecomment-496675346
         self.version = np.array([0, 2, 5], dtype=np.int32)  # (int32) version info: major, minor, revision
         self.seen = np.array([0], dtype=np.int64)  # (int64) number of images seen during training
+        self.info()  # print model description
 
     def forward(self, x, verbose=False):
         img_size = x.shape[-2:]
@@ -291,6 +292,7 @@ class Darknet(nn.Module):
 
     def fuse(self):
         # Fuse Conv2d + BatchNorm2d layers throughout model
+        print('Fusing Conv2d() and BatchNorm2d() layers...')
         fused_list = nn.ModuleList()
         for a in list(self.children())[0]:
             if isinstance(a, nn.Sequential):
@@ -303,7 +305,10 @@ class Darknet(nn.Module):
                         break
             fused_list.append(a)
         self.module_list = fused_list
-        # model_info(self)  # yolov3-spp reduced from 225 to 152 layers
+        self.info()  # yolov3-spp reduced from 225 to 152 layers
+
+    def info(self, verbose=False):
+        torch_utils.model_info(self, verbose)
 
 
 def get_yolo_layers(model):
diff --git a/train.py b/train.py
index 18d2d0f1..2e4a9744 100644
--- a/train.py
+++ b/train.py
@@ -207,7 +207,6 @@ def train():
     # torch.autograd.set_detect_anomaly(True)
     results = (0, 0, 0, 0, 0, 0, 0)  # 'P', 'R', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification'
     t0 = time.time()
-    torch_utils.model_info(model, report='summary')  # 'full' or 'summary'
     print('Using %g dataloader workers' % nw)
     print('Starting training for %g epochs...' % epochs)
     for epoch in range(start_epoch, epochs):  # epoch ------------------------------------------------------------------
diff --git a/utils/torch_utils.py b/utils/torch_utils.py
index b394b642..98e80345 100644
--- a/utils/torch_utils.py
+++ b/utils/torch_utils.py
@@ -75,11 +75,11 @@ def fuse_conv_and_bn(conv, bn):
         return fusedconv
 
 
-def model_info(model, report='summary'):
+def model_info(model, verbose=False):
     # 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
-    if report == 'full':
+    if verbose:
         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.', '')