Practical Convolutional Neural Networks

Image augmentation

While training a CNN model, we do not want the model to change any prediction based on the size, angle, and position of the image. The image is represented as a matrix of pixel values, so the size, angle, and position have a huge effect on the pixel values. To make the model more size-invariant, we can add different sizes of the image to the training set. Similarly, in order to make the model more rotation-invariant, we can add images with different angles. This process is known as image data augmentation. This also helps to avoid overfitting. Overfitting happens when a model is exposed to very few samples. Image data augmentation is one way to reduce overfitting, but it may not be enough because augmented images are still ...