diff --git a/test/test_transforms.py b/test/test_transforms.py
index 5d0275f946f..9319fb0664a 100644
--- a/test/test_transforms.py
+++ b/test/test_transforms.py
@@ -9,6 +9,11 @@
 except ImportError:
     accimage = None
 
+try:
+    from scipy import stats
+except ImportError:
+    stats = None
+
 
 GRACE_HOPPER = 'assets/grace_hopper_517x606.jpg'
 
@@ -327,6 +332,34 @@ def test_ndarray_gray_int32_to_pil_image(self):
         assert img.mode == 'I'
         assert np.allclose(img, img_data[:, :, 0])
 
+    @unittest.skipIf(stats is None, 'scipy.stats not available')
+    def test_random_vertical_flip(self):
+        img = transforms.ToPILImage()(torch.rand(3, 10, 10))
+        vimg = img.transpose(Image.FLIP_TOP_BOTTOM)
+
+        num_vertical = 0
+        for _ in range(100):
+            out = transforms.RandomVerticalFlip()(img)
+            if out == vimg:
+                num_vertical += 1
+
+        p_value = stats.binom_test(num_vertical, 100, p=0.5)
+        assert p_value > 0.05
+
+    @unittest.skipIf(stats is None, 'scipy.stats not available')
+    def test_random_horizontal_flip(self):
+        img = transforms.ToPILImage()(torch.rand(3, 10, 10))
+        himg = img.transpose(Image.FLIP_LEFT_RIGHT)
+
+        num_horizontal = 0
+        for _ in range(100):
+            out = transforms.RandomHorizontalFlip()(img)
+            if out == himg:
+                num_horizontal += 1
+
+        p_value = stats.binom_test(num_horizontal, 100, p=0.5)
+        assert p_value > 0.05
+
 
 if __name__ == '__main__':
     unittest.main()
diff --git a/torchvision/transforms.py b/torchvision/transforms.py
index da58aa12b9a..6202ec9c147 100644
--- a/torchvision/transforms.py
+++ b/torchvision/transforms.py
@@ -338,6 +338,22 @@ def __call__(self, img):
         return img
 
 
+class RandomVerticalFlip(object):
+    """Vertically flip the given PIL.Image randomly with a probability of 0.5"""
+
+    def __call__(self, img):
+        """
+        Args:
+            img (PIL.Image): Image to be flipped.
+
+        Returns:
+            PIL.Image: Randomly flipped image.
+        """
+        if random.random() < 0.5:
+            return img.transpose(Image.FLIP_TOP_BOTTOM)
+        return img
+
+
 class RandomSizedCrop(object):
     """Crop the given PIL.Image to random size and aspect ratio.