CNN strategy in recognizing spinned images
Learning for a CNN depends on the width and depth of a network. Wider and deeper networks can learn more complex data structures, including data augmented images. Increasing the width and depth of a network increase the capacity of the model to learn features. 

Width is generally associated with the number of features. The wider a network is the more features it is able to learn. Imagine a network that is very thin. It would only learn the single feature that is correlated with high performance on the task.

Depth is will increase the capacity of the model better weigh lower-level features. Each successive layer is a combination of the previous layer. The lower layers learn simple features which are combined to create complex features in higher layers. In the case of faces, lower layers learn lines of different orientations. Higher layers learn to combine those lines to form eyes and noses.

Increasing the number of kernels is one way to increase the width of the network. 

Increasing the number of layers (i.e., depth) is generally more useful.