images = [ | |
val_data_augmentation(convert_to_tf_tensor(image.convert("RGB"))) for image in example_batch["image"] | |
] | |
example_batch["pixel_values"] = [tf.transpose(tf.squeeze(image)) for image in images] | |
return example_batch | |
Use 🤗 Datasets [~datasets.Dataset.set_transform] to apply the transformations on the fly: | |
py | |
food["train"].set_transform(preprocess_train) | |
food["test"].set_transform(preprocess_val) | |
As a final preprocessing step, create a batch of examples using DefaultDataCollator. |