Image augmentation is an important technique to improve model accuracy.
Many people use libraries such as albumentations and imgaug to apply various augmentations, but since these libraries are created using non-TF libraries, you need to use tf.numpy_function to use them in tf.data.
However, this approach cannot work in a TPU Node environment such as Google Colab. Google Colab TPU is often used in competitions such as kaggle because it is very strong and inexpensive.
So I have created a new image augmentation library that works with TPU! I have actually used it in a kaggle competition and it works!
Please give it a try!
library: GitHub - hirune924/imgaug-tf: An image augmentation library for tensorflow.
kaggle solution: UW-Madison GI Tract Image Segmentation | Kaggle