Çiçek Sınıflandırmada AlexNet ve MobileNetV2 mimarilerinin performans karşılaştırması

Öz

Çeşitliliğin fazla olması nedeniyle çiçek türlerini sınıflandırma problemi zorlu bir süreçtir. Bu alanla ilgili çalışmalar yapan araştırmacıların işlerini kolaylaştırmak için bilgisayarlı görü ve derin öğrenme uygulamaları büyük avantaj sağlamaktadır. Derin öğrenme yöntemleri yeni algoritmaların geliştirilmesiyle yüksek başarılara ulaşabilmektedir Birçok alanda kullanılan bu yöntemler çiçek türlerini sınıflandırmada da başarılı sonuçlar vermektedir. Yapılan bu çalışmada Oxford-17 veri seti kullanılmıştır. Veri setinde 17 sınıfa ait 1360 adet çiçek görüntüsü yer almaktadır. Evrişimsel sinir ağları kullanılarak oluşturulan bu çalışmada derin öğrenme mimarilerinden AlexNet ve MobileNetV2 mimarilerinin performans karşılaştırmaları yapılarak AlexNet mimarisinden %93,1, MobileNetV2 mimarisinden %93,9 başarı oranı elde edilmiştir.

Anahtar Kelimeler

• Derin Öğrenme
• Çiçek Sınıflandırma
• AlexNet
• MobileNetV2

Performance comparison of AlexNet and MobileNetV2 architectures in flower classification

Öz

The problem of classifying flower species is a challenging process due to the high diversity. Computer vision and deep learning applications provide great advantages to facilitate the work of researchers working in this field. Deep learning methods can achieve high success with the development of new algorithms. These methods, which are used in many fields, also provide successful results in classifying flower species. Oxford-17 data set was used in this study. The data set includes 1360 flower images belonging to 17 classes. In this study, which was created using convolutional neural networks, the performance comparisons of the deep learning architectures AlexNet and MobileNetV2 architectures were made and a success rate of 93.1% was obtained from the AlexNet architecture and 93.9% from the MobileNetV2 architecture.

Anahtar Kelimeler

• Deep Learning
• Flower Classification
• AlexNet
• MobileNetV2

Kaynakça

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