(КПІ ім. Ігоря Сікорського, 2024) Khotin, K.; Shymkovych, V.; Kravets, P.; Novatsky, A.; Shymkovych, L.
In this article a dataset with data augmentation for neural network training and convolutional neural network model for dog breed recognition system has been developed. Neural network model architecture using transfer learning to improve classification results was developed. Neural network based on the MobileNetV3-Large architecture. The structure of the dataset has analyzed and decision to use different methods for normalizes data. A large dataset containing 70 distinct categories of dog breeds was collected and balanced through the use of data augmentation techniques. Data augmentation enabled the reduction of the disparity between the minimum and maximum number of instances by eliminating redundant images and adding essential ones. The developed model was tested and the results were demonstrated. The final accuracy of the model is 96%. The result model implement in dog breed recognition system, which is based on mobile platform. The implemented application produces functionality to interact with the resulting model such as real-time process of identifying a dog's breed or from device's gallery. Further improvement the performance of the model classification quality can be achieved by expending the initial dataset or by applying other optimization methods and adjust the learning rate.