Çeşitli görüntülerin sınıflandırılması için yeni bir Evrişimsel Sinir Ağı önerisi

Year 2024, , 262 - 278, 15.01.2024

Yeşim Tiraki , Hasan Temurtaş , Soydan Serttaş , Çiğdem Bakır

https://doi.org/10.28948/ngumuh.1355726

Abstract

Günümüzde derin öğrenme metotları robotik, ses işleme, tıp ve görüntü gibi birçok alanda yaygın kullanılmaktadır. Bu çalışmada literatürde yapılan görüntü sınıflandırma ve analizine yönelik çalışmalar detaylı bir şekilde incelenmiştir. Ayrıca yapılan çalışmada CNN kullanılarak derin öğrenme metotlarının MNIST, Fashion MNIST, CIFAR-10 ve CIFAR-100 ismindeki 4 farklı veri seti üzerinde performans analizleri yapılmıştır. Oluşturulan derin öğrenme modellerinin yapıları, eğitim için kullanılan parametre değerleri, kullanılan katmanlar, doğrulama verileri için elde edilen karmaşıklık matrisleri, doğruluk ve kayıp grafikleri ayrıntılı olarak gösterilmiştir. Çalışmamız 3 adet konvolüsyon katmanı, 3 adet batch normalizasyon katmanı, 2 adet maxpooling katmanı, 1 flatten, 2 droupout, 2 dense katmanından oluşan farklı bir ağ yapısı ile gerçekleştirilmiştir. Ayrıca önerdiğimiz modelle görüntü sınıflandırılmasının farklı veriler üzerindeki performansı artırılmıştır. Test sonunda çalışmamız çeşitli değerlendirme metriklerine göre doğruluk sonuçları karşılaştırılmıştır. Kullanılan tüm veri setleri için en iyi ve en kötü bulunan görüntüler tespit edilmiştir. Önerilen CNN modeli ile MNIST ve Fashion MNIST veri setleri için yüksek doğruluk oranları gözlemlenmiş olup bu değerler sırasıyla %99.22 ve %99.21’dir.

Keywords

Derin öğrenme, Evrişimli Sinir Ağı, CIFAR-10, CIFAR-100, MNIST, Fashion MNIST

A new Convolutional Neural Network proposal for classifying various images

Abstract

Nowadays, deep learning methods are widely used in many fields such as robotics, sound processing, medicine and imagery. In this study, studies on image classification and analysis in the literature were examined in detail. In addition, performance analyzes of deep learning methods were carried out using CNN on 4 different data sets named MNIST, Fashion MNIST, CIFAR-10 and CIFAR-100. The structures of the created deep learning models, the parameter values used for training, the layers used, the complexity matrices obtained for the validation data, accuracy and loss graphs are shown in detail. Our study was carried out with a different network structure consisting of 3 convolution layers, 3 batch normalization layers, 2 maxpooling layers, 1 flatten, 2 droupout, 2 dense layers. In addition, the performance of image classification on different data has been increased with our proposed model. At the end of the test, the accuracy results of our study were compared according to various evaluation metrics. For all the data sets used, the best and worst images were determined. With the proposed CNN model, high accuracy rates were observed for MNIST and Fashion MNIST datasets, with these values being 99.22% and 99.21%, respectively.

Keywords

Deep learning, Convolutional Neural Networks, CIFAR-10, CIFAR-100, MNIST, Fashion MNIST

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