A Garbage Image Classification Application Based on Transport Learning
DOI:
https://doi.org/10.53469/ijomsr.2025.08(06).05Keywords:
Transfer learning, ResNet152, Refuse classificationAbstract
With the continuous development of society, the continuous improvement of people's living standards and the great improvement of lifestyle, waste disposal issues have received more and more attention. If there is no taxonomy of science to deal with waste, on the contrary is to throw away, then the soil, water resources and other human survival environment will be a great degree of pollution. Waste sorting is a key part of effectively alleviating environmental pollution problems. Migration learning can effectively solve the problem of model training that results in adaptation when there is not enough data, This paper uses a migration learning method based on pre-trained networks to train the model to classify garbage images by using 80% of the garbage image dataset as a training set and the rest as a test set. By comparing the experimental data of different models under the same parameters, ResNet152 is selected as the pre-trained network model, and the test accuracy is up to 82%.
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