A-RepVGG: Research on Classification Algorithms based on Deep Learning and Wood CT Images
Zhishuai Zheng,
Zhedong Ge,
Xiaoxia Yang
et al.
Abstract:To address the issue of limited expressive ability and performance degradation of the model caused by the limited depth and width of the CNN because of low computational overhead of lightweight convolutional neural networks, this paper introduces a classification method called A-RepVGG for wood CT images. A-RepVGG aims to enhance the model's classification accuracy in terms of wood microstructure by increasing the model complexity without increasing the depth and width of the network. The method utilizes adapt… Show more
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