diff --git a/utils/datasets.py b/utils/datasets.py
index 7ef44b03..9c271a9d 100755
--- a/utils/datasets.py
+++ b/utils/datasets.py
@@ -414,9 +414,6 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
         if self.image_weights:
             index = self.indices[index]
 
-        img_path = self.img_files[index]
-        label_path = self.label_files[index]
-
         hyp = self.hyp
         if self.mosaic:
             # Load mosaic
@@ -434,19 +431,14 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
 
             # Load labels
             labels = []
-            if os.path.isfile(label_path):
-                x = self.labels[index]
-                if x is None:  # labels not preloaded
-                    with open(label_path, 'r') as f:
-                        x = np.array([x.split() for x in f.read().splitlines()], dtype=np.float32)
-
-                if x.size > 0:
-                    # Normalized xywh to pixel xyxy format
-                    labels = x.copy()
-                    labels[:, 1] = ratio[0] * w * (x[:, 1] - x[:, 3] / 2) + pad[0]  # pad width
-                    labels[:, 2] = ratio[1] * h * (x[:, 2] - x[:, 4] / 2) + pad[1]  # pad height
-                    labels[:, 3] = ratio[0] * w * (x[:, 1] + x[:, 3] / 2) + pad[0]
-                    labels[:, 4] = ratio[1] * h * (x[:, 2] + x[:, 4] / 2) + pad[1]
+            x = self.labels[index]
+            if x is not None and x.size > 0:
+                # Normalized xywh to pixel xyxy format
+                labels = x.copy()
+                labels[:, 1] = ratio[0] * w * (x[:, 1] - x[:, 3] / 2) + pad[0]  # pad width
+                labels[:, 2] = ratio[1] * h * (x[:, 2] - x[:, 4] / 2) + pad[1]  # pad height
+                labels[:, 3] = ratio[0] * w * (x[:, 1] + x[:, 3] / 2) + pad[0]
+                labels[:, 4] = ratio[1] * h * (x[:, 2] + x[:, 4] / 2) + pad[1]
 
         if self.augment:
             # Augment imagespace
@@ -496,7 +488,7 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
         img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
         img = np.ascontiguousarray(img)
 
-        return torch.from_numpy(img), labels_out, img_path, shapes
+        return torch.from_numpy(img), labels_out, self.img_files[index], shapes
 
     @staticmethod
     def collate_fn(batch):