  Hadronization is a non-perturbative process, which theoretical description
can not be deduced from first principles. Modeling hadron formation requires
several assumptions and various phenomenological approaches. Utilizing
state-of-the-art Computer Vision and Deep Learning algorithms, it is eventually
possible to train neural networks to learn non-linear and non-perturbative
features of the physical processes. In this study, results of two ResNet
networks are presented by investigating global and kinematical quantities,
indeed jet- and event-shape variables. The widely used Lund string
fragmentation model is applied as a baseline in $\sqrt{s}= 7$ TeV proton-proton
collisions to predict the most relevant observables at further LHC energies.
