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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. |