diff --git a/utils/dataloaders.py b/utils/dataloaders.py
index c1ad1f1a4b83..d8ef11fd94b4 100755
--- a/utils/dataloaders.py
+++ b/utils/dataloaders.py
@@ -455,7 +455,7 @@ def __init__(self,
         self.im_files = list(cache.keys())  # update
         self.label_files = img2label_paths(cache.keys())  # update
         n = len(shapes)  # number of images
-        bi = np.floor(np.arange(n) / batch_size).astype(np.int)  # batch index
+        bi = np.floor(np.arange(n) / batch_size).astype(int)  # batch index
         nb = bi[-1] + 1  # number of batches
         self.batch = bi  # batch index of image
         self.n = n
@@ -497,7 +497,7 @@ def __init__(self,
                 elif mini > 1:
                     shapes[i] = [1, 1 / mini]
 
-            self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(np.int) * stride
+            self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(int) * stride
 
         # Cache images into RAM/disk for faster training (WARNING: large datasets may exceed system resources)
         self.ims = [None] * n
@@ -867,7 +867,7 @@ def extract_boxes(path=DATASETS_DIR / 'coco128'):  # from utils.dataloaders impo
                     b = x[1:] * [w, h, w, h]  # box
                     # b[2:] = b[2:].max()  # rectangle to square
                     b[2:] = b[2:] * 1.2 + 3  # pad
-                    b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(np.int)
+                    b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(int)
 
                     b[[0, 2]] = np.clip(b[[0, 2]], 0, w)  # clip boxes outside of image
                     b[[1, 3]] = np.clip(b[[1, 3]], 0, h)