Convolutional Neural Networks and Pattern Recognition: Application to Image Classification
Christy Ntambwe Kabamba, Lucie Mpuekela .N, Simon Ntumba .B, Eugene Mbuyi .M · Zenodo (CERN European Organization for Nuclear Research) · 2019
This research study focuses on pattern recognition using convolutional neural network. Deep neural network has been choosing as the best option for the training process because it produced a high percentage of accuracy. We designed different architectures of convolutional neural network in order to find the one with high accuracy of image classification and optimum bias. We used CIFAR-10 data set that contains 60000 Images to train our model on architectures. The best architecture was able to classify images with 95.55% of accuracy and an error of 0.32% using cross validation method. We note that, the numbers of epoch while running the model and the depth of the architecture are factors that contributed to get this performance.