To make deep learning really work in real life, it is better for the model to be used with neural networks of small memory requirements and small computations. In this paper, four neural network models, i.e. Fully Connected Neural Network, CNN, MobileNetV1, and MobileNetV2 are applied to deal with the classification of clothing images in the Fashion-MNIST dataset and the results are compared. MobileNetV2, which performs best, is a deep learning network in the field of image classification and recognition. With the development of robots, smart auto, and augmented reality in real-world application, it is better to use a network such as MobileNet in a computationally limited platform. The experimental results show that 1) MobileNet is a more effective method of classification of clothing images compared to Fully Connected Neural Network and CNN, with an accuracy rate of 91%. 2) MobileNetV2 has greater improvement in accuracy than the previous model, which accuracy has reached to 93%. 3) MobileNet is simpler than Fully Connected Neural Network and CNN model structures, although training time has increased, it ensures higher accuracy.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A comparative research on clothing images classification based on neural network models


    Beteiligte:
    Di, Wang (Autor:in)


    Erscheinungsdatum :

    2020-10-14


    Format / Umfang :

    255319 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    An Artificial Neural Network for Classification of Color Images

    Jaramillo, A. / Yamaba, K. / Society for Information Display et al. | British Library Conference Proceedings | 1993


    Classification of binary document images into textual or nontextual data blocks using neural network models

    Le, D. X. / Thoma, G. R. / Wechsler, H. | British Library Online Contents | 1995


    Deep residual neural network-based classification of loaded and unloaded UAV images

    Seidaliyeva, Ulzhalgas / Alduraibi, Manal / Ilipbayeva, Lyazzat et al. | IEEE | 2020


    Convolutional Neural Network-Based In-Vehicle Occupant Detection and Classification Method using Second Strategic Highway Research Program Cabin Images

    Papakis, Ioannis / Sarkar, Abhijit / Svetovidov, Andrei et al. | Transportation Research Record | 2021


    On Certain Hybrid Classification Models Based on Neural Network and Logical Approaches

    Ryazanov, V. V. / Sen ko, O. V. / Aslanyan, L. et al. | British Library Online Contents | 2001