Why use `val_transforms()` function in image classification example instead of `feature_extractor`?

Intermediate
1

Hello,

In the image classification example, custom transforms are applied on the evaluation dataset: transformers/run_image_classification.py at 6cb19540c9a5288195445e8355646c06605a64fc · huggingface/transformers · GitHub

Why not just use the feature_extractor on the eval dataset?

Instead of transformers/run_image_classification.py at 6cb19540c9a5288195445e8355646c06605a64fc · huggingface/transformers · GitHub we would have

    def val_transforms(example_batch):
        """Apply _val_transforms across a batch."""
        example_batch["pixel_values"] = [feature_extractor(pil_img.convert("RGB")) for pil_img in example_batch["image"]]
        return example_batch

Both do not yield the same output. Therefore, if using the model with feature_extractor as the processing step and not custom transforms, we may have unwanted results.

Cheers!