Çiçek Görüntü Sınıflandırılmasında Ön Eğitimli Evrişimsel Sinir Ağlarının Performans Karşılaştırması

Year 2022, Issue: 35, 315 - 321, 07.05.2022

İpek Atik

https://doi.org/10.31590/ejosat.1082023

Cited By: 2

Abstract

Çiçek sınıflandırması botonikten, ekolojik çalışmalara kadar birçok alan için önemlidir. Çiçek görüntülerinin net şekilde belirgin olmaması, yaprakların, dalların görüntüyü kapatması ve benzer özellikte çiçeklerin çok olması sınıflandırma çalışmalarında rastlanan zorluklardandır. Çalışmada internetten alınan 3670 çiçekten oluşan veri seti kullanılarak sınıflandırma çalışması yapılmıştır. Son dönemde görüntü sınıflandırma çalışmalarında derin öğrenme yöntemleri kullanılarak oldukça başarılı sonuçlara ulaşılmaktadır. Bu çalışma derin öğrenme modellerinden ön eğitimli evrişimsel sinir ağları (ESA) AlexNet, GoogLeNet, SqueezeNet, ShuffleNet ve Resnet-18 ile sınıflandırma çalışması yapılarak performansları karşılaştırmalı olarak irdelenmiştir. Karşılaştırma neticesinde en başarılı sonuca %97.26 doğruluk oranına sahip olan GoogLeNet ile ulaşılmıştır. Diğer modeller için elde edilen başarı oranları sırasıyla ShuffleNet, SqueezeNet, ResNet-18 ve AlexNet için %97.23, %92.84, %91.42 %89.05’tir. Çalışmada GoogLeNet modeli bu çalışmadaki modellerin yanı sıra aynı veri seti ile yapılan diğer alışmalar içinde en yüksek başarıya ulaşan model olmuştur.

Keywords

Derin Öğrenme, Sınıflandırma, Evrişimsel Sinir Ağları, çiçek, GoogLeNet

Performance Comparison of Pre-Trained Convolutional Neural Networks in Flower Image Classification

Abstract

Flower classification is important for many fields from botonics to ecological studies. The difficulties encountered in classification studies are that flower images are not clearly evident, leaves and branches obscure the image, and there are many flowers with similar characteristics. In the study, classification study was carried out using the data set consisting of 3670 flowers taken from the internet. Recently, very successful results have been achieved by using deep learning methods in image classification studies. In this study, the performances of the deep learning models were examined comparatively by making a classification study with the pre-trained convolutional neural networks (CNN) AlexNet, GoogLeNet, SqueezeNet, ShuffleNet and Resnet-18. As a result of the comparison, the most successful result was obtained with GoogLeNet, which has an accuracy rate of 97.26%. The accuracy rate was calculated as 97.23%, 92.84%, 91.42% 89.05% for ShuffleNet, SqueezeNet, ResNet-18 and AlexNet, respectively. In addition to the models in this study, the GoogLeNet model in the study was the model that achieved the highest success among other studies conducted with the same data set.

Keywords

Deep Learning, Classification, Convolutional Neural Networks, flower, GoogLeNet

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