diff --git a/examples/pytorch/image-classification/run_image_classification.py b/examples/pytorch/image-classification/run_image_classification.py
old mode 100644
new mode 100755
index 27a81f0094..d009297541
--- a/examples/pytorch/image-classification/run_image_classification.py
+++ b/examples/pytorch/image-classification/run_image_classification.py
@@ -28,6 +28,7 @@ from PIL import Image
 from torchvision.transforms import (
     CenterCrop,
     Compose,
+    Lambda,
     Normalize,
     RandomHorizontalFlip,
     RandomResizedCrop,
@@ -325,7 +326,11 @@ def main():
         size = image_processor.size["shortest_edge"]
     else:
         size = (image_processor.size["height"], image_processor.size["width"])
-    normalize = Normalize(mean=image_processor.image_mean, std=image_processor.image_std)
+    normalize = (
+        Normalize(mean=image_processor.image_mean, std=image_processor.image_std)
+        if hasattr(image_processor, "image_mean") and hasattr(image_processor, "image_std")
+        else Lambda(lambda x: x)
+    )
     _train_transforms = Compose(
         [
             RandomResizedCrop(size),
diff --git a/examples/pytorch/image-classification/run_image_classification_no_trainer.py b/examples/pytorch/image-classification/run_image_classification_no_trainer.py
index 3e38a3e79a..52b5fabd89 100644
--- a/examples/pytorch/image-classification/run_image_classification_no_trainer.py
+++ b/examples/pytorch/image-classification/run_image_classification_no_trainer.py
@@ -32,6 +32,7 @@ from torch.utils.data import DataLoader
 from torchvision.transforms import (
     CenterCrop,
     Compose,
+    Lambda,
     Normalize,
     RandomHorizontalFlip,
     RandomResizedCrop,
@@ -331,7 +332,11 @@ def main():
         size = image_processor.size["shortest_edge"]
     else:
         size = (image_processor.size["height"], image_processor.size["width"])
-    normalize = Normalize(mean=image_processor.image_mean, std=image_processor.image_std)
+    normalize = (
+        Normalize(mean=image_processor.image_mean, std=image_processor.image_std)
+        if hasattr(image_processor, "image_mean") and hasattr(image_processor, "image_std")
+        else Lambda(lambda x: x)
+    )
     train_transforms = Compose(
         [
             RandomResizedCrop(size),