000			03191nam a22002777a 4500
003			KOHA
005			20231027160910.0
008			231027d2023 cy ||||| m||| 00| 0 eng d
040			_aCY-NiCIU _beng _cCY-NiCIU _erda
041			_aeng
090			_aYL 3165 _bA46 2023
100	1		_aAhed, Umar Bindir
245	1	0	_aCOMPARISON OF WELL-KNOWN CONVOLUTIONAL NEURAL NETWORKS IMAGE CLASSIFICATION MODELS ON THE CIFAR-10 DATASET / _cUMAR BINDIR AHMED; SUPERVISOR: ASSOC. PROF. DR. UMAR ÖZGÜNALP
264			_c2023
300			_axi, 62 sheets; _c31 cm. _e1 CD-ROM
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
502			_aThesis (MSc) - Cyprus International University. Institute of Graduate Studies and Research Electrical and Electronics Engineering Department
504			_aIncludes bibliography (sheets 60-62)
520			_aABSTRACT In this research, a supervised learning method is used to assess the performance of well-known deep convolutional neural network models for image classification on the CIFAR-10 dataset. VGGNet, GoogLeNet, ResNet, and AlexNet are the four models that were trained and evaluated using a variety of measures, including loss and accuracy. ResNet and VGGNet obtained the greatest test accuracy of 78.15% when comparing the performances of deep convolutional neural network models during training and testing. AlexNet and GoogleNet came in second and third, with the highest test accuracy of 77.5% and 74.41%, respectively. These outcomes suggest that the deep convolutional neural network models in a supervised method are capable of successfully categorizing CIFAR-10 images with reasonably small loss values, as shown in Table 4.1. The findings underscore the significance of meticulous planning and execution of training and testing methodologies for deep convolutional neural networks in supervised learning scenarios to efficiently exploit data with annotations. One may investigate various strategies, including data augmentation, regularization, and learning rate scheduling, to enhance the effectiveness of the models. The result of this investigation emphasizes the importance of precise preparation and implementation of training and testing procedures in deep convolutional neural networks in supervised learning in order to maximize their capacity for learning. Further investigation is necessary to tackle the limitations and challenges that occur with maximizing the use of data with annotations. The study at issue offers major findings regarding the impact and implications of the models utilized on the CIFAR-10 dataset. The findings contribute to an understanding of the strengths and weaknesses of these approaches, guiding future research to enhance the performance of the image classification models. Keywords: Alexnet, CIFAR-10 Dataset, Convolutional Neural Networks, Googlenet, Image Classification, Resnet, Vggnet
650		0	_a Neural networks (Computer science) _vDissertations, Academic
650		0	_aData sets _vDissertations, Academic
700	1		_aÖzgünalp, Umar _esupervisor
942			_2ddc _cTS
999			_c291575 _d291575