How do you implement data augmentation techniques to increase the diversity of your CNN training data?

1 Why use data augmentation?

Data augmentation can address a variety of challenges when training a CNN model, such as limited or imbalanced data, overfitting, and variation and complexity. This technique can increase the size of the dataset and balance the classes by applying different transformations. It can also introduce randomness and noise to the training data to prevent overfitting. Additionally, data augmentation can simulate various scenarios and situations that your model may encounter in the real world, allowing it to capture all relevant features and patterns.

2 How to choose data augmentation techniques?

Data augmentation techniques can be classified into two categories: geometric and photometric. Geometric techniques modify the shape or position of the image, such as cropping, flipping, rotating, scaling, or shifting. Photometric techniques modify the appearance or color of the image, such as changing the brightness, contrast, saturation, hue, or adding noise or blur. Depending on your problem domain and data characteristics, you may want to use different combinations of geometric and photometric techniques. For example, if you are working on a face recognition problem, you may want to use geometric techniques such as flipping or rotating to account for different poses and orientations, but avoid photometric techniques that may alter the facial features or expressions. On the other hand, if you are working on a natural scene classification problem, you may want to use both geometric and photometric techniques to account for different weather conditions, seasons, or times of the day.

3 How to implement data augmentation using TensorFlow?

TensorFlow is a popular framework for building and training deep learning models, and it provides several built-in functions and classes for data augmentation. One way to implement data augmentation using TensorFlow is to use the tf.image module, which contains various functions for manipulating images, such as tf.image.random_crop, tf.image.random_flip_left_right, tf.image.random_brightness, and so on. You can apply these functions to your input images before feeding them to your CNN model, either sequentially or randomly. For example, you can define a function that randomly crops, flips, and changes the brightness of an image as follows:

def augment(image):
  image = tf.image.random_crop(image, size=[224, 224, 3])
  image = tf.image.random_flip_left_right(image)
  image = tf.image.random_brightness(image, max_delta=0.2)
  return image        

Another way to implement data augmentation using TensorFlow is to use the tf.keras.preprocessing.image.ImageDataGenerator class, which allows you to create a generator object that can apply various transformations to your images on the fly. You can specify the parameters for the transformations, such as rotation_range, width_shift_range, height_shift_range, brightness_range, and so on, when creating the generator object. You can then use the generator object to generate batches of augmented images from your original images, either from a directory or from a numpy array. For example, you can create a generator object that applies random rotation, width shift, height shift, and brightness change to your images as follows:

datagen = tf.keras.preprocessing.image.ImageDataGenerator(
  rotation_range=20,
  width_shift_range=0.2,
  height_shift_range=0.2,
  brightness_range=[0.8, 1.2]
)        

4 How to implement data augmentation using Keras?

Keras is a high-level API for building and training deep learning models, and it also provides the tf.keras.preprocessing.image.ImageDataGenerator class for data augmentation. You can use the same approach as described in the previous section to create a generator object and generate batches of augmented images from your original images. Alternatively, you can use the tf.keras.layers.experimental.preprocessing module, which contains various layers for data augmentation, such as RandomCrop, RandomFlip, RandomRotation, RandomContrast, and so on. You can add these layers to your CNN model as part of the preprocessing step, and they will apply the transformations to your input images during training. For example, you can add a random crop, flip, and contrast layer to your CNN model as follows:

model = tf.keras.Sequential([
  tf.keras.layers.experimental.preprocessing.RandomCrop(224, 224),
  tf.keras.layers.experimental.preprocessing.RandomFlip("horizontal"),
  tf.keras.layers.experimental.preprocessing.RandomContrast(0.2),
  tf.keras.layers.Conv2D(64, 3, activation="relu"),
  tf.keras.layers.MaxPooling2D(2),
  tf.keras.layers.Flatten(),
  tf.keras.layers.Dense(10, activation="softmax")
])        

5 How to implement data augmentation using OpenCV?

OpenCV is a library for computer vision and image processing, and it offers various functions and methods for manipulating images, such as cv2.resize, cv2.flip, cv2.rotate, cv2.cvtColor, cv2.addWeighted, and so on. You can use these functions and methods to create your own custom data augmentation functions that can apply different transformations to your images according to your needs and preferences. For example, you can define a function that resizes, flips, rotates, and blends an image with another image as follows:

def augment(image1, image2):
  image1 = cv2.resize(image1, (224, 224))
  image1 = cv2.flip(image1, 1)
  image1 = cv2.rotate(image1, cv2.ROTATE_90_CLOCKWISE)
  image2 = cv2.resize(image2, (224, 224))
  image2 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)
  image2 = cv2.addWeighted(image2, 0.5, image1, 0.5, 0)
  return image2        

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?