This thesis investigates the effectiveness of different pruning strategies in VGG16 models. The study begins with the evaluation of the CIFAR-10 dataset using a VGG16 model, followed by the calculation of the model’s relevance using a method from the TorchLRP repository. Two pruning strategies were then implemented based on the calculated relevance data. The first strategy involved pruning the model based on the absolute mean value of the relevance of the channels. The second strategy used the absolute mean of the model parameters for pruning. Both methods were applied at varying pruning percentages, and their performances were compared. However, significant performance differences between the two methods were not observed until a combined approach was implemented.