diff --git a/train.py b/train.py
index a3d45bc3..e49c5f69 100644
--- a/train.py
+++ b/train.py
@@ -6,7 +6,7 @@ import torch.optim as optim
 import torch.optim.lr_scheduler as lr_scheduler
 from torch.utils.data import DataLoader
 
-import test  # Import test.py to get mAP after each epoch
+import test  # import test.py to get mAP after each epoch
 from models import *
 from utils.datasets import *
 from utils.utils import *
@@ -41,7 +41,6 @@ def train(
     latest = weights + 'latest.pt'
     best = weights + 'best.pt'
     device = torch_utils.select_device()
-    torch.backends.cudnn.benchmark = True  # possibly unsuitable for multiscale
     img_size_test = img_size  # image size for testing
     multi_scale = not opt.single_scale
 
@@ -145,7 +144,7 @@ def train(
     # Start training
     model.hyp = hyp  # attach hyperparameters to model
     model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device)  # attach class weights
-    model_info(model)
+    model_info(model, report='summary')  # 'full' or 'summary'
     nb = len(dataloader)
     maps = np.zeros(nc)  # mAP per class
     results = (0, 0, 0, 0, 0)  # P, R, mAP, F1, test_loss
@@ -330,7 +329,7 @@ if __name__ == '__main__':
             # Mutate hyperparameters
             old_hyp = hyp.copy()
             init_seeds(seed=int(time.time()))
-            s = [.4, .4, .4, .4, .4, .4, .4, .4*0, .4*0, .04*0, .4*0]  # fractional sigmas
+            s = [.4, .4, .4, .4, .4, .4, .4, .4 * 0, .4 * 0, .04 * 0, .4 * 0]  # fractional sigmas
             for i, k in enumerate(hyp.keys()):
                 x = (np.random.randn(1) * s[i] + 1) ** 1.1  # plt.hist(x.ravel(), 100)
                 hyp[k] = hyp[k] * float(x)  # vary by about 30% 1sigma
diff --git a/utils/torch_utils.py b/utils/torch_utils.py
index fc8e2c43..b58be1cb 100644
--- a/utils/torch_utils.py
+++ b/utils/torch_utils.py
@@ -5,6 +5,8 @@ def init_seeds(seed=0):
     torch.manual_seed(seed)
     torch.cuda.manual_seed(seed)
     torch.cuda.manual_seed_all(seed)
+    torch.backends.cudnn.benchmark = True  # set False for reproducible resuls
+    # torch.backends.cudnn.deterministic = True  # https://pytorch.org/docs/stable/notes/randomness.html
 
 
 def select_device(force_cpu=False):