diff --git a/utils/datasets.py b/utils/datasets.py
index 2103e45e..a0b17421 100755
--- a/utils/datasets.py
+++ b/utils/datasets.py
@@ -247,6 +247,19 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
                     pass  # print('Warning: missing labels for %s' % self.img_files[i])  # missing label file
             assert len(np.concatenate(self.labels, 0)) > 0, 'No labels found. Incorrect label paths provided.'
 
+        # Cache images into memory for faster training (~5GB)
+        cache_images = False
+        if cache_images and augment:  # if training
+            for i in tqdm(range(min(len(self.img_files), 10000)), desc='Caching images'):  # max 10k images
+                img_path = self.img_files[i]
+                img = cv2.imread(img_path)  # BGR
+                assert img is not None, 'Image Not Found ' + img_path
+                r = self.img_size / max(img.shape)  # size ratio
+                if self.augment and r < 1:  # if training (NOT testing), downsize to inference shape
+                    h, w, _ = img.shape
+                    img = cv2.resize(img, (int(w * r), int(h * r)), interpolation=cv2.INTER_LINEAR)  # or INTER_AREA
+                self.imgs[i] = img
+
         # Detect corrupted images https://medium.com/joelthchao/programmatically-detect-corrupted-image-8c1b2006c3d3
         detect_corrupted_images = False
         if detect_corrupted_images:
@@ -284,9 +297,6 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
                 h, w, _ = img.shape
                 img = cv2.resize(img, (int(w * r), int(h * r)), interpolation=cv2.INTER_LINEAR)  # INTER_LINEAR fastest
 
-            if index < 5000:  # cache first 5000 images into memory (~5GB)
-                self.imgs[index] = img
-
         # Augment colorspace
         augment_hsv = True
         if self.augment and augment_hsv:
@@ -320,7 +330,6 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
             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)
-                    self.labels[index] = x  # save for next time
 
             if x.size > 0:
                 # Normalized xywh to pixel xyxy format