Abstract
As countries around the world improve their garbage recycling and processing policies, the intelligently and efficiently garbage classification and identification has become a key point for implementing policies. However, traditional image recognition methods still have disadvantages, for instance, it needs a large amount of data annotation and a long time is required to train the model. In response to these drawbacks, this paper proposes a transfer learning method based on ResNet model, which aims to solve the problem of efficient classification of small-scale garbage image data sets. For the small sample image data set, after the data augmentation, the pre-training model ResNet50 is migrated to the data set through two migration learning methods of fine-tuning and pre-training model as the feature extractor, so as to realize the training of the target model. The experimental results show that the model classification effect after fine-tuning method and hyperparameter adjustment is better than the model without transfer learning, which can effectively improve the training speed and accuracy, and reduce the impact of over-fitting.
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Acknowledgement
This work was supported by the Major science and technology project of Hainan Province (Grant No. ZDKJ2020012), Key Research and Development Program of Hainan Province (Grant No. ZDYF2020040),Hainan Provincial Natural Science Foundation of China (Grant Nos. 2019RC098) and National Natural Science Foundation of China (NSFC) (Grant No. 62162022, 62162024 and 61762033).
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Liu, L., Cheng, J., Xie, L., Song, J., Zhou, K., Liu, J. (2021). A Transfer Learning Method Based on ResNet Model. In: Tan, Y., Shi, Y., Zomaya, A., Yan, H., Cai, J. (eds) Data Mining and Big Data. DMBD 2021. Communications in Computer and Information Science, vol 1453. Springer, Singapore. https://doi.org/10.1007/978-981-16-7476-1_23
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DOI: https://doi.org/10.1007/978-981-16-7476-1_23
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