diff --git a/utils/datasets.py b/utils/datasets.py
index f83a17be..1f9b9e3f 100755
--- a/utils/datasets.py
+++ b/utils/datasets.py
@@ -370,7 +370,7 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
                             if not os.path.exists(Path(f).parent):
                                 os.makedirs(Path(f).parent)  # make new output folder
 
-                            b = x[1:] * np.array([w, h, w, h])  # box
+                            b = x[1:] * [w, h, w, h]  # box
                             b[2:] = b[2:].max()  # rectangle to square
                             b[2:] = b[2:] * 1.3 + 30  # pad
                             b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(np.int)
@@ -731,7 +731,7 @@ def cutout(image, labels):
         return inter_area / box2_area
 
     # create random masks
-    scales = [0.5] * 1  # + [0.25] * 4 + [0.125] * 16 + [0.0625] * 64 + [0.03125] * 256  # image size fraction
+    scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16  # image size fraction
     for s in scales:
         mask_h = random.randint(1, int(h * s))
         mask_w = random.randint(1, int(w * s))
@@ -743,14 +743,13 @@ def cutout(image, labels):
         ymax = min(h, ymin + mask_h)
 
         # apply random color mask
-        mask_color = [random.randint(0, 255) for _ in range(3)]
-        image[ymin:ymax, xmin:xmax] = mask_color
+        image[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)]
 
         # return unobscured labels
         if len(labels) and s > 0.03:
             box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32)
             ioa = bbox_ioa(box, labels[:, 1:5])  # intersection over area
-            labels = labels[ioa < 0.90]  # remove >90% obscured labels
+            labels = labels[ioa < 0.60]  # remove >60% obscured labels
 
     return labels