diff --git a/test/external/external_test_datasets.py b/test/external/external_test_datasets.py
index 04ddf8739e..0ae7e92d55 100644
--- a/test/external/external_test_datasets.py
+++ b/test/external/external_test_datasets.py
@@ -122,7 +122,7 @@ class TestOpenImagesDataset(ExternalTestDatasets):
     return iter(dataloader)
 
   def test_training_set(self):
-    img_size, img_mean, img_std, anchors = (800, 800), [0.485, 0.456, 0.406], [0.229, 0.224, 0.225], torch.ones((120087, 4))
+    img_size, img_mean, img_std, anchors = (800, 800), [0.0, 0.0, 0.0], [1.0, 1.0, 1.0], torch.ones((120087, 4))
     tinygrad_dataloader, ref_dataloader = self._create_tinygrad_dataloader("train", anchors.numpy()), self._create_ref_dataloader("train")
     transform = GeneralizedRCNNTransform(img_size, img_mean, img_std)
 
diff --git a/test/external/mlperf_retinanet/transforms.py b/test/external/mlperf_retinanet/transforms.py
index 3ae9f25156..e63c26d87d 100644
--- a/test/external/mlperf_retinanet/transforms.py
+++ b/test/external/mlperf_retinanet/transforms.py
@@ -181,7 +181,7 @@ class GeneralizedRCNNTransform(nn.Module):
             if image.dim() != 3:
                 raise ValueError("images is expected to be a list of 3d tensors "
                                  "of shape [C, H, W], got {}".format(image.shape))
-            # image = self.normalize(image)
+            image = self.normalize(image)
             image, target_index = self.resize(image, target_index)
             images[i] = image
             if targets is not None and target_index is not None: