diff --git a/test/test_models_detection_utils.py b/test/test_models_detection_utils.py
index bfb26f24eae..4888eb0c573 100644
--- a/test/test_models_detection_utils.py
+++ b/test/test_models_detection_utils.py
@@ -58,6 +58,34 @@ def test_transform_copy_targets(self):
         self.assertTrue(torch.equal(targets[0]['boxes'], targets_copy[0]['boxes']))
         self.assertTrue(torch.equal(targets[1]['boxes'], targets_copy[1]['boxes']))
 
+    def test_normalize_integer(self):
+        transform = GeneralizedRCNNTransform(
+            300, 500,
+            torch.randint(0, 255, (3,)),
+            torch.randint(0, 255, (3,))
+        )
+        image = [torch.randint(0, 255, (3, 200, 300), dtype=torch.uint8)]
+        targets = [{'boxes': torch.randint(0, 255, (3, 4))}]
+        image_list, _ = transform(image, targets)  # noqa: F841
+        # check that original images still have uint8 dtype
+        for img in image:
+            self.assertTrue(img.dtype == torch.uint8)
+        # check that the resulting images have float32 dtype
+        self.assertTrue(image_list.tensors.dtype == torch.float32)
+        # check that no NaN values are produced
+        self.assertFalse(torch.any(torch.isnan(image_list.tensors)))
+
+    def test_normalize_float(self):
+        transform = GeneralizedRCNNTransform(300, 500, torch.zeros(3), torch.ones(3))
+        image = [torch.rand(3, 200, 300)]
+        targets = [{'boxes': torch.rand(3, 4)}]
+        image_list, _ = transform(image, targets)  # noqa: F841
+        # check that original images still have float32 dtype
+        for img in image:
+            self.assertTrue(img.dtype == torch.float32)
+        # check that the resulting images have float32 dtype
+        self.assertTrue(image_list.tensors.dtype == torch.float32)
+
 
 if __name__ == '__main__':
     unittest.main()
diff --git a/torchvision/models/detection/transform.py b/torchvision/models/detection/transform.py
index 0d95361eedb..e870bb7c63d 100644
--- a/torchvision/models/detection/transform.py
+++ b/torchvision/models/detection/transform.py
@@ -117,6 +117,8 @@ def forward(self,
         return image_list, targets
 
     def normalize(self, image):
+        if not image.is_floating_point():
+            image = image.to(torch.float32)
         dtype, device = image.dtype, image.device
         mean = torch.as_tensor(self.image_mean, dtype=dtype, device=device)
         std = torch.as_tensor(self.image_std, dtype=dtype, device=device)