diff --git a/utils/datasets.py b/utils/datasets.py
index af0f5bd..11a4439 100755
--- a/utils/datasets.py
+++ b/utils/datasets.py
@@ -328,6 +328,12 @@ class LoadStreams:  # multiple IP or RTSP cameras
 class LoadImagesAndLabels(Dataset):  # for training/testing
     def __init__(self, path, img_size=640, batch_size=16, augment=False, hyp=None, rect=False, image_weights=False,
                  cache_images=False, single_cls=False, stride=32, pad=0.0, rank=-1):
+
+        def img2label_paths(img_paths):
+            # Define label paths as a function of image paths
+            sa, sb = os.sep + 'images' + os.sep, os.sep + 'labels' + os.sep  # /images/, /labels/ substrings
+            return [x.replace(sa, sb, 1).replace(os.path.splitext(x)[-1], '.txt') for x in img_paths]
+
         try:
             f = []  # image files
             for p in path if isinstance(path, list) else [path]:
@@ -362,11 +368,8 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
         self.mosaic_border = [-img_size // 2, -img_size // 2]
         self.stride = stride
 
-        # Define labels
-        sa, sb = os.sep + 'images' + os.sep, os.sep + 'labels' + os.sep  # /images/, /labels/ substrings
-        self.label_files = [x.replace(sa, sb, 1).replace(os.path.splitext(x)[-1], '.txt') for x in self.img_files]
-
         # Check cache
+        self.label_files = img2label_paths(self.img_files)  # labels
         cache_path = str(Path(self.label_files[0]).parent) + '.cache'  # cached labels
         if os.path.isfile(cache_path):
             cache = torch.load(cache_path)  # load
@@ -375,12 +378,15 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
         else:
             cache = self.cache_labels(cache_path)  # cache
 
-        # Get labels
-        labels, shapes = zip(*[cache[x] for x in self.img_files])
-        self.shapes = np.array(shapes, dtype=np.float64)
+        # Read cache
+        cache.pop('hash')  # remove hash
+        labels, shapes = zip(*cache.values())
         self.labels = list(labels)
+        self.shapes = np.array(shapes, dtype=np.float64)
+        self.img_files = list(cache.keys())  # update
+        self.label_files = img2label_paths(cache.keys())  # update
 
-        # Rectangular Training  https://github.com/ultralytics/yolov3/issues/232
+        # Rectangular Training
         if self.rect:
             # Sort by aspect ratio
             s = self.shapes  # wh
@@ -404,7 +410,7 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
 
             self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(np.int) * stride
 
-        # Cache labels
+        # Check labels
         create_datasubset, extract_bounding_boxes, labels_loaded = False, False, False
         nm, nf, ne, ns, nd = 0, 0, 0, 0, 0  # number missing, found, empty, datasubset, duplicate
         pbar = enumerate(self.label_files)
@@ -483,10 +489,9 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
         for (img, label) in pbar:
             try:
                 l = []
-                image = Image.open(img)
-                image.verify()  # PIL verify
-                # _ = io.imread(img)  # skimage verify (from skimage import io)
-                shape = exif_size(image)  # image size
+                im = Image.open(img)
+                im.verify()  # PIL verify
+                shape = exif_size(im)  # image size
                 assert (shape[0] > 9) & (shape[1] > 9), 'image size <10 pixels'
                 if os.path.isfile(label):
                     with open(label, 'r') as f:
@@ -495,8 +500,7 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
                     l = np.zeros((0, 5), dtype=np.float32)
                 x[img] = [l, shape]
             except Exception as e:
-                x[img] = [None, None]
-                print('WARNING: %s: %s' % (img, e))
+                print('WARNING: Ignoring corrupted image and/or label:%s: %s' % (img, e))
 
         x['hash'] = get_hash(self.label_files + self.img_files)
         torch.save(x, path)  # save for next time