Journal of Dali University ›› 2022, Vol. 7 ›› Issue (12): 8-14.

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Comparative Study of Garbage Image Classification Models Based on Transfer Learning

  

  1. （1. College of Mathematics and Computer， Dali University， Dali， Yunnan 671003， China； 2. Engineering Training Center， Dali University， Dali， Yunnan 671003， China；3.College of Agriculture and Biology Science， Dali University， Dali， Yunnan 671003， China）

     

  • Received:2022-04-21 Online:2022-12-15 Published:2022-12-15

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Abstract: Many deep neural network models have been applied to automatic identification and classification of garbage images and have achieved good results. Current mainstream deep neural networks include attention mechanism-based neural networks and convolution-based neural networks. The existing research on garbage classification is mainly based on convolution-based neural network model， while the attention mechanism-based neural network model has not been tried in garbage classification. Which of these two types of deep neural networks performs better on small-scale garbage classification data sets is worth exploring. A systematic comparative study of several representative models shows that compared with convolution-based neural network model， the deep neural network model with pure attention mechanism on small-scale garbage classification data sets shows better performance， which provides a reference for garbage classification model selection. 

Key words: garbage classification, deep convolution neural network, transfer learning, attention mechanism

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