diff --git a/src/transformers/models/efficientnet/image_processing_efficientnet.py b/src/transformers/models/efficientnet/image_processing_efficientnet.py
index 2a5b5c93749b..2aff4ae4db83 100644
--- a/src/transformers/models/efficientnet/image_processing_efficientnet.py
+++ b/src/transformers/models/efficientnet/image_processing_efficientnet.py
@@ -66,7 +66,7 @@ class EfficientNetImageProcessor(BaseImageProcessor):
             `do_resize` in `preprocess`.
         size (`dict[str, int]` *optional*, defaults to `{"height": 346, "width": 346}`):
             Size of the image after `resize`. Can be overridden by `size` in `preprocess`.
-        resample (`PILImageResampling` filter, *optional*, defaults to 0):
+        resample (`PILImageResampling` filter, *optional*, defaults to `PIL.Image.BICUBIC`):
             Resampling filter to use if resizing the image. Can be overridden by `resample` in `preprocess`.
         do_center_crop (`bool`, *optional*, defaults to `False`):
             Whether to center crop the image. If the input size is smaller than `crop_size` along any edge, the image
@@ -102,7 +102,7 @@ def __init__(
         self,
         do_resize: bool = True,
         size: Optional[dict[str, int]] = None,
-        resample: PILImageResampling = PIL.Image.NEAREST,
+        resample: PILImageResampling = PIL.Image.BICUBIC,
         do_center_crop: bool = False,
         crop_size: Optional[dict[str, int]] = None,
         rescale_factor: Union[int, float] = 1 / 255,
@@ -113,7 +113,7 @@ def __init__(
         image_std: Optional[Union[float, list[float]]] = None,
         include_top: bool = True,
         **kwargs,
-    ) -> None:
+    ):
         super().__init__(**kwargs)
         size = size if size is not None else {"height": 346, "width": 346}
         size = get_size_dict(size)
@@ -133,12 +133,12 @@ def __init__(
         self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD
         self.include_top = include_top
 
-    # Copied from transformers.models.vit.image_processing_vit.ViTImageProcessor.resize with PILImageResampling.BILINEAR->PILImageResampling.NEAREST
+
     def resize(
         self,
         image: np.ndarray,
         size: dict[str, int],
-        resample: PILImageResampling = PILImageResampling.NEAREST,
+        resample: PILImageResampling = PILImageResampling.BICUBIC,
         data_format: Optional[Union[str, ChannelDimension]] = None,
         input_data_format: Optional[Union[str, ChannelDimension]] = None,
         **kwargs,
@@ -151,8 +151,8 @@ def resize(
                 Image to resize.
             size (`dict[str, int]`):
                 Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image.
-            resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.NEAREST`):
-                `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.NEAREST`.
+            resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
+                `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BICUBIC`.
             data_format (`ChannelDimension` or `str`, *optional*):
                 The channel dimension format for the output image. If unset, the channel dimension format of the input
                 image is used. Can be one of:
diff --git a/src/transformers/models/efficientnet/image_processing_efficientnet_fast.py b/src/transformers/models/efficientnet/image_processing_efficientnet_fast.py
index 93e1237f061c..4d29b8adf0e3 100644
--- a/src/transformers/models/efficientnet/image_processing_efficientnet_fast.py
+++ b/src/transformers/models/efficientnet/image_processing_efficientnet_fast.py
@@ -33,7 +33,7 @@
 
 @auto_docstring
 class EfficientNetImageProcessorFast(BaseImageProcessorFast):
-    resample = PILImageResampling.NEAREST
+    resample = PILImageResampling.BICUBIC
     image_mean = IMAGENET_STANDARD_MEAN
     image_std = IMAGENET_STANDARD_STD
     size = {"height": 346, "width": 346}
diff --git a/src/transformers/models/metaclip_2/convert_metaclip_2_to_hf.py b/src/transformers/models/metaclip_2/convert_metaclip_2_to_hf.py
index b086a5844b34..a5292ccae108 100644
--- a/src/transformers/models/metaclip_2/convert_metaclip_2_to_hf.py
+++ b/src/transformers/models/metaclip_2/convert_metaclip_2_to_hf.py
@@ -26,7 +26,6 @@
 
 # Import MetaCLIP modules
 from src.mini_clip.factory import create_model_and_transforms
-
 from transformers import (
     AutoTokenizer,
     CLIPImageProcessor,