diff --git a/train.py b/train.py
index 0e8f1e28..4f621388 100644
--- a/train.py
+++ b/train.py
@@ -51,7 +51,7 @@ def train(
     start_epoch = 0
     best_loss = float('inf')
     if resume:  # Load previously saved PyTorch model
-        checkpoint = torch.load(latest, map_location=device)  # load checkpoin
+        checkpoint = torch.load(latest, map_location=device)  # load checkpoint
         model.load_state_dict(checkpoint['model'])
         start_epoch = checkpoint['epoch'] + 1
         if checkpoint['optimizer'] is not None:
diff --git a/utils/datasets.py b/utils/datasets.py
index 2280c6a1..655072dc 100755
--- a/utils/datasets.py
+++ b/utils/datasets.py
@@ -108,7 +108,7 @@ class LoadImagesAndLabels:  # for training
 
     def __iter__(self):
         self.count = -1
-        self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF)
+        #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF)
         return self
 
     def __getitem__(self, index):
@@ -133,8 +133,8 @@ class LoadImagesAndLabels:  # for training
     def load_images(self, ia, ib):
         img_all, labels_all, img_paths, img_shapes = [], [], [], []
         for index, files_index in enumerate(range(ia, ib)):
-            img_path = self.img_files[self.shuffled_vector[files_index]]
-            label_path = self.label_files[self.shuffled_vector[files_index]]
+            img_path = self.img_files[files_index]
+            label_path = self.label_files[files_index]
 
             img = cv2.imread(img_path)  # BGR
             assert img is not None, 'File Not Found ' + img_path