ctmkrs17 / image-augmentation-layers-keras

Implementation of custom image augmentation layers using Keras.

Custom Image Augmentation Layers with Keras

This repository contains an implementation of 4 custom image augmentation layers in Keras:

• RandomGaussianNoise: applies additive normal-distributed pixel noise to the image with given standard deviation
• RandomResizedCrop: crops and resizes a part of the image with given aspect ratio and zoom range to its original resolution
• RandomColorJitter: distorts the color distibutions of images in torchvision-style
• RandomColorAffine: distorts the color distibutions of images by applying random affine transformations in color space

Using custom Keras preprocessing layers for data augmentation has the following two advantages:

• the data augmentation will run on GPU in batches, so the training will not be bottlenecked by the data pipeline in environments with constrained CPU resources (such as a Colab Notebook, or a personal machine)
• deployment is easier as the data preprocessing pipeline is encapsulated in the model, and does not have to be reimplemented when deploying it

The default augmentation parameters correspond to mild, conservative image augmentations.

Usage: minimal example

import augmentations
import tensorflow as tf
import tensorflow_datasets as tfds

from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.layers.experimental import preprocessing

num_epochs = 10
batch_size = 64
width = 128

train_dataset = (
    tfds.load("cifar10", split="train", as_supervised=True, shuffle_files=True)
    .shuffle(10 * batch_size)
    .batch(batch_size)
    .prefetch(buffer_size=tf.data.AUTOTUNE)
)
test_dataset = (
    tfds.load("cifar10", split="test", as_supervised=True)
    .batch(batch_size)
    .prefetch(buffer_size=tf.data.AUTOTUNE)
)

model = keras.Sequential(
    [
        layers.Input(shape=(32, 32, 3)),
        preprocessing.Rescaling(1 / 255),
        augmentations.RandomColorJitter(),
        augmentations.RandomResizedCrop(),
        layers.Conv2D(width, kernel_size=3, strides=2, activation="relu"),
        layers.Conv2D(width, kernel_size=3, strides=2, activation="relu"),
        layers.Conv2D(width, kernel_size=3, strides=2, activation="relu"),
        layers.Flatten(),
        layers.Dense(10, activation="softmax"),
    ]
)
model.compile(
    optimizer="adam",
    loss="sparse_categorical_crossentropy",
    metrics=["sparse_categorical_accuracy"],
)
model.fit(train_dataset, epochs=num_epochs, validation_data=test_dataset)

Visualizations