The CNN is based on the kaggle challenge for classifying cats and dogs using a dispersed dataset consisting of immages of cats and dogs. Augmented image processing is a technique used to improve the accuracy of a convolutional neural network (CNN) by artificially generating additional training data for the network. This can be done by applying various transformations to the original training images, such as cropping, scaling, rotating, and flipping. These transformed images can be used to augment the original training dataset, allowing the CNN to learn from a larger and more diverse set of data. This can help the network to generalize better and improve its accuracy on new, unseen data. In addition to improving the accuracy of the CNN, augmented image processing can also help to reduce overfitting, which is a common issue in deep learning models.