diff --git a/src/transformers/image_utils.py b/src/transformers/image_utils.py
index 120d7b3c1bd2..437e7c568558 100644
--- a/src/transformers/image_utils.py
+++ b/src/transformers/image_utils.py
@@ -131,7 +131,7 @@ def convert_rgb(self, image):
 
         return image.convert("RGB")
 
-    def rescale_image(self, image: np.ndarray, scale: Union[float, int]) -> np.ndarray:
+    def rescale(self, image: np.ndarray, scale: Union[float, int]) -> np.ndarray:
         """
         Rescale a numpy image by scale amount
         """
@@ -163,7 +163,7 @@ def to_numpy_array(self, image, rescale=None, channel_first=True):
         rescale = isinstance(image.flat[0], np.integer) if rescale is None else rescale
 
         if rescale:
-            image = self.rescale_image(image.astype(np.float32), 1 / 255.0)
+            image = self.rescale(image.astype(np.float32), 1 / 255.0)
 
         if channel_first and image.ndim == 3:
             image = image.transpose(2, 0, 1)
@@ -214,9 +214,9 @@ def normalize(self, image, mean, std, rescale=False):
         # type it may need rescaling.
         elif rescale:
             if isinstance(image, np.ndarray):
-                image = self.rescale_image(image.astype(np.float32), 1 / 255.0)
+                image = self.rescale(image.astype(np.float32), 1 / 255.0)
             elif is_torch_tensor(image):
-                image = self.rescale_image(image.float(), 1 / 255.0)
+                image = self.rescale(image.float(), 1 / 255.0)
 
         if isinstance(image, np.ndarray):
             if not isinstance(mean, np.ndarray):
diff --git a/src/transformers/models/owlvit/feature_extraction_owlvit.py b/src/transformers/models/owlvit/feature_extraction_owlvit.py
index f8a45706835d..0af33eccaef0 100644
--- a/src/transformers/models/owlvit/feature_extraction_owlvit.py
+++ b/src/transformers/models/owlvit/feature_extraction_owlvit.py
@@ -85,6 +85,13 @@ def __init__(
         image_std=None,
         **kwargs
     ):
+        # Early versions of the OWL-ViT config on the hub had "rescale" as a flag. This clashes with the
+        # vision feature extractor method `rescale` as it would be set as an attribute during the super().__init__
+        # call. This is for backwards compatibility.
+        if "rescale" in kwargs:
+            rescale_val = kwargs.pop("rescale")
+            kwargs["do_rescale"] = rescale_val
+
         super().__init__(**kwargs)
         self.size = size
         self.resample = resample