Classification of Turkish Folk Dances using Deep Learning



Deep learning, ResNet50, Turkish folk dances, VGG16, CNN


The folk dances, which reflect the cultural values of the society consist of regular and continuous activities performed singly or in groups, accompanied by music. Folk dances show differences according to climate, geographical position and socio-economic status of the society. Classification of Turkish folk dances is an interesting subject due to the different hand, arm and foot postures of the dancers. The complexity of the background, camera angle, special clothing can cause the algorithms to give incorrect results. It gives a playground to experiment with different deep learning techniques. In this study, the classification of Turkish folk dances is carried out from video images with deep learning methods. Zeybek, Çiftetelli, Horon and Halay dances which are among Turkish folk dances were selected. The dataset consists of 24000 images in total, prepared as 6000 images belonging to each class (Halay, Zeybek, Çiftetelli, Horon). 75% of the images make up the training set and the validation set is 25% of the images. CNN model and pre-trained VGG16 and ResNet50 architectures with transfer learning technique are used in the classification of Turkish folk dances in video images. The models have been tested with YouTube data. The accuracy rates of the proposed CNN model, VGG16 and ResNet50 architectures are 94%, 97% and 98%, respectively.


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 Classic CNN architecture




How to Cite

Nazari, H. ., & Kaynak, S. . (2022). Classification of Turkish Folk Dances using Deep Learning . International Journal of Intelligent Systems and Applications in Engineering, 10(3), 226–232. Retrieved from



Research Article