Flower classification based on bilinear RepVGG attention network
To further improve the accuracy of flower classification,a new network model was proposed based on bilinear convolutional neural network,RepVGG and attention mechanism.Firstly,RepVGG network was used to re-place the original feature extraction network VGG to improve the ability to extract the main features of flowers.Then,channel attention and spatial attention mechanisms were introduced into the two RepVGG networks respectively,and the high-dimensional bilinear features generated by the cross-product of the two RepVGG networks were used to ex-tract the fine-grained features of flowers.Finally,the RepVGG layers are transformed into single-way structures by structure reparameterization to improve the speed of model reasoning.Experimental results show that on the enhanced Oxford-102 data set,the inference speed and classification accuracy of the new model are greatly improved compared with the original model and the common model,and the classification accuracy is also improved compared with that be-fore the introduction of attention.
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