Crop a random part of the image, resize it, and normalize it with the image mean and standard deviation:

from torchvision.transforms import RandomResizedCrop, Compose, Normalize, ToTensor
normalize = Normalize(mean=image_processor.image_mean, std=image_processor.image_std)
size = (
     image_processor.size["shortest_edge"]
     if "shortest_edge" in image_processor.size
     else (image_processor.size["height"], image_processor.size["width"])
 )
_transforms = Compose([RandomResizedCrop(size), ToTensor(), normalize])

Then create a preprocessing function to apply the transforms and return the pixel_values - the inputs to the model - of the image:

def transforms(examples):
     examples["pixel_values"] = [_transforms(img.convert("RGB")) for img in examples["image"]]
     del examples["image"]
     return examples

To apply the preprocessing function over the entire dataset, use 🤗 Datasets [~datasets.Dataset.with_transform] method.