Akademik Veri Yönetim Sistemi
2023 Innovations in Intelligent Systems and Applications Conference (ASYU), Sivas, Türkiye, 11 - 13 Ekim 2023, ss.1-5
Convolutional Neural Networks are one of the
most successful methods used in image classification. In
convolutional neural networks, the image is processed by
passing it through successive layers of convolution and pooling.
For feature extraction, the convolution process is applied to the
image using filters in convolution layers. In this study, the effect
of the number of filters in the VGG16 model on classification
performance was examined. CIFAR10 and Fashion-MNIST
datasets were used in the experiments which shown that
increasing the number of filters, especially in the first layers,
increased the classification accuracy. In the experiments
performed with the CIFAR10 dataset the accuracy is increased
from 85.20% to 88.58 when the number of filters is increased
from minimum to maximum. The improvement with FashionMNIST database is from 91.74% to %92.62, with all the
remaining parameters the same. Depending on the increasing
number of filters, it is seen that the number of transactions, and
consequently the duration, is increased in training.