[3] [3] Lyu R, Li Z, J Zuo, et al. Flower Classification and Recognition Based on Significance Test and Transfer Learning[C]//2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE). IEEE, 2021:649-652.

[4] [4] Xia X, Xu C, Nan B. Inception-v3 for flower classification[C]//2017 2nd International Conferenceon Image, Vision and Computing (ICIVC). Chengdu: IEEE, 2017:783-787.

[5] [5] Narvekar C, Rao M. Flower classification using CNN and transfer learning in CNN- Agriculture Perspective[C]//2020 3rd International Conference on Intelligent Sustainable Systems (ICISS). 2020:660-662.

[6] [6] Musa Cibuk, Umit Budak, Yanhui Guo, et al. Efficient deep features selections and classification for flowerspecies recognition[J]. Measurement, 2019, 137:7-13.

[7] [7] Zhang M, Su H, Wen J. Classification of flower image based on attention mechanism and multi-loss attention network[J]. Computer Communications, 2021, 179: 307-317.

[8] [8] Mastouri R, Khlifa N, Neji H, et al. A bilinear convolutional neural network for lung nodules classification on CT images[J]. International Journal of Computer Assisted Radiology and Surgery. 2021 Jan;16(1): 91-101.

[9] [9] Wang P, Liu J, Xu L, et al. Classification of Amanita Species Based on Bilinear Networks with Attention Mechanism[J]. Agriculture, 2021, 11(5): 393.

[11] [11] Nergiz M. Analysis of RepVGG on Small Sized Dandelion Images Dataset in terms of Transfer Learning, Regularization, Spatial Attention as well as Squeeze and Excitation Blocks[C]//2021 6th International Conference on Computer Science and Engineering (UBMK). IEEE, 2021:378-382.

[12] [12] Xu G, Xu Y, Zhang S, et al. SFRNet: Feature Extraction-Fusion Steganalysis Network Based on Squeeze-and-Excitation Block and RepVgg Block[J]. Security and Communication Networks, 2021, 2021:1-11.

[13] [13] Xu G, Xu Y, Zhang S, et al. SFRNet: Feature Extraction-Fusion Steganalysis Network Based on Squeeze-and-Excitation Block and RepVgg Block[J]. Hindawi Limited, 2021, 36:6720-6725.

[14] [14] LIN T Y, Roychowdhury A, Maji S, Bilinear CNN Models for Fine-Grained Visual Recognition[J]. IEEE International Conference on Computer Vision (ICCV), 2015: 1449-1457.

[15] [15] Ding X, Zhang X, Ma N, et al. RepVGG: Making VGGstyle ConvNets Great Again[J]. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2021:13733-13742.

[16] [16] Sergey Ioffe, Christian Szegedy. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift[C]//Proceedings of the 32nd International Conference on Machine Learning, Lille, France: PMLR, 2015, 37: 448-456.

[17] [17] Hu Jie, Li Shen, Samuel Albanie et al. Squeeze-and-Excitation Networks[J]. IEEE transactions on pattern analysis and machine intelligence, 2020, 42(8): 2011-2023.

[18] [18] Zhong X, Gong O, Huang W, et al. Squeeze-and-Excitation Wide Residual Networks in Image Classification[C]//2019 IEEE International Conference on Image Processing (ICIP). IEEE, 2019: 395-399.

[19] [19] Roy S K, Dubey S R, Chatterjee S, et al. FuSENet: Fused Squeeze-and-Excitation Network for Spectral-Spatial Hyperspectral Image Classification[J]. IET Image Processing, 2020, 14(8): 1653-1661.

[20] [20] Deng J, Ma Y, Li D A, et al. Classification of breast density categories based on SE-Attention neural networks[J]. Computer Methods and Programs in Biomedicine, 2020, 193(1): 105489.

[21] [21] Woo S, Park J, Lee J Y, et al. Cbam: Convolutional Block Attention Module[C]//Proceedings of the European Conference on Computer Vision (ECCV). Munich: ECCV, 2018:3-19.

[22] [22] Kong S, Fowlkes C. Low-Rank Bilinear Pooling for Fine-Grained Classification[J]. IEEE Computer Society, 2017: 7025-7034.

[23] [23] Wu W, Yu J. An Improved Bilinear Pooling Method for Image-Based Action Recognition[C]//2020 25th International Conference on Pattern Recognition (ICPR). IEEE Computer Society. 2021: 8578-8583.

[24] [24] Gao Y, Beijbom O, Zhang N, et al. Compact bilinear pooling[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 317-326.