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Waste recycling is important both in the global economy and in the global climate as a whole. As a result, classification of recyclable waste has become a critical goal for humanity, and deep learning models have important potential to fulfill this task. In this study, six advanced folding network models of neural networks - EfficientNetV2L, EfficientNetB1, EfficientNetB0, MobileNetV2, ResNet50, and VGG16, were compared for the effects of the garbage classification task. The results show that EfficienctNetB0 gave better performance than the other models. Furthermore, data augmentation techniques were used to improve classification accuracy, as data records contained a limited number of samples. Notably, MobileNetV2 not only achieved competitive accuracy, but also became a green choice for its low carbon emissions.

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Waste Classification with Convolutional Neural Networks: A Comparative Study of Various Models

  • 会议论文

  • 发表日期：

    2026年

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  • Manpreet Kour,
  • Naman Jain,
  • Neeraj Gupta,
  • Geetanjali Bhola

摘要

Waste recycling is important both in the global economy and in the global climate as a whole. As a result, classification of recyclable waste has become a critical goal for humanity, and deep learning models have important potential to fulfill this task. In this study, six advanced folding network models of neural networks - EfficientNetV2L, EfficientNetB1, EfficientNetB0, MobileNetV2, ResNet50, and VGG16, were compared for the effects of the garbage classification task. The results show that EfficienctNetB0 gave better performance than the other models. Furthermore, data augmentation techniques were used to improve classification accuracy, as data records contained a limited number of samples. Notably, MobileNetV2 not only achieved competitive accuracy, but also became a green choice for its low carbon emissions.