diff --git a/ultralytics/data/augment.py b/ultralytics/data/augment.py
index 90cdd2313..cd084f3e6 100644
--- a/ultralytics/data/augment.py
+++ b/ultralytics/data/augment.py
@@ -2322,7 +2322,7 @@ def classify_transforms(
     size=224,
     mean=DEFAULT_MEAN,
     std=DEFAULT_STD,
-    interpolation=Image.BILINEAR,
+    interpolation="BILINEAR",
     crop_fraction: float = DEFAULT_CROP_FRACTION,
 ):
     """
@@ -2337,7 +2337,7 @@ def classify_transforms(
             tuple, it defines (height, width).
         mean (tuple): Mean values for each RGB channel used in normalization.
         std (tuple): Standard deviation values for each RGB channel used in normalization.
-        interpolation (int): Interpolation method for resizing.
+        interpolation (str): Interpolation method of either 'NEAREST', 'BILINEAR' or 'BICUBIC'.
         crop_fraction (float): Fraction of the image to be cropped.
 
     Returns:
@@ -2360,7 +2360,7 @@ def classify_transforms(
     # Aspect ratio is preserved, crops center within image, no borders are added, image is lost
     if scale_size[0] == scale_size[1]:
         # Simple case, use torchvision built-in Resize with the shortest edge mode (scalar size arg)
-        tfl = [T.Resize(scale_size[0], interpolation=interpolation)]
+        tfl = [T.Resize(scale_size[0], interpolation=getattr(T.InterpolationMode, interpolation))]
     else:
         # Resize the shortest edge to matching target dim for non-square target
         tfl = [T.Resize(scale_size)]
@@ -2389,7 +2389,7 @@ def classify_augmentations(
     hsv_v=0.4,  # image HSV-Value augmentation (fraction)
     force_color_jitter=False,
     erasing=0.0,
-    interpolation=Image.BILINEAR,
+    interpolation="BILINEAR",
 ):
     """
     Creates a composition of image augmentation transforms for classification tasks.
@@ -2411,7 +2411,7 @@ def classify_augmentations(
         hsv_v (float): Image HSV-Value augmentation factor.
         force_color_jitter (bool): Whether to apply color jitter even if auto augment is enabled.
         erasing (float): Probability of random erasing.
-        interpolation (int): Interpolation method.
+        interpolation (str): Interpolation method of either 'NEAREST', 'BILINEAR' or 'BICUBIC'.
 
     Returns:
         (torchvision.transforms.Compose): A composition of image augmentation transforms.
@@ -2427,6 +2427,7 @@ def classify_augmentations(
         raise TypeError(f"classify_transforms() size {size} must be integer, not (list, tuple)")
     scale = tuple(scale or (0.08, 1.0))  # default imagenet scale range
     ratio = tuple(ratio or (3.0 / 4.0, 4.0 / 3.0))  # default imagenet ratio range
+    interpolation = getattr(T.InterpolationMode, interpolation)
     primary_tfl = [T.RandomResizedCrop(size, scale=scale, ratio=ratio, interpolation=interpolation)]
     if hflip > 0.0:
         primary_tfl.append(T.RandomHorizontalFlip(p=hflip))