  Convolutional neural networks (CNN) are known to be an effective means to
detect and analyze images. Their power is essentially based on the ability to
extract out images common features. There exist, however, images involving
unique, irregular features or details. Such is a collection of unusual children
drawings reflecting the kids imagination and individuality. These drawings were
analyzed by means of a CNN constructed by means of Keras-TensorFlow. The same
problem - on a significantly higher level - was solved with newly developed
family of networks called IgNet that is described in this paper. It proved able
to learn by 100 % all the categorical characteristics of the drawings. In the
case of a regression task (learning the young artists ages) IgNet performed
with an error of no more than 0.4 %. The principles are discussed of IgNet
design that made it possible to reach such substantial results with rather
simple network topology.

Computer Vision and Pattern Recognition;Machine Learning