chunked/nltk_chunking/tasks_semantic_segmentation/chunk_21.txt

Define two separate transformation functions:
- training data transformations that include image augmentation
- validation data transformations that only transpose the images, since computer vision models in 🤗 Transformers expect channels-first layout

import tensorflow as tf
def aug_transforms(image):
     image = tf.keras.utils.img_to_array(image)
     image = tf.image.random_brightness(image, 0.25)
     image = tf.image.random_contrast(image, 0.5, 2.0)
     image = tf.image.random_saturation(image, 0.75, 1.25)
     image = tf.image.random_hue(image, 0.1)
     image = tf.transpose(image, (2, 0, 1))
     return image
def transforms(image):
     image = tf.keras.utils.img_to_array(image)
     image = tf.transpose(image, (2, 0, 1))
     return image

Next, create two preprocessing functions to prepare batches of images and annotations for the model.