Fine-Grained Flower Image Classification Based on Neural Network Architecture Search
To enhance the automation of deep convolutional neural network(CNN)design and improve fine-grained flower image classification accuracy,an advanced neural network search approach based on differentiable architecture search(DARTS)was proposed.This method automatically constructed fine-grained flower image classification models.Initially,an attention-convolution module was constructed to create a comprehensive attention-convolution search space,thereby increasing the network's focus on discriminative features.Subsequently,a densely connected reduction cell(DCR cell)with more shallow feature input nodes was developed to retain additional shallow feature information,reducing the loss of discriminative feature information and promoting multi-scale feature fusion.Lastly,the positions of DCR cells were adjusted when stacking the best cells to create network models of varying parameter sizes,enabling deployment on a broader range of terminal devices.The results showed that this method took approximately 4.5 hours to find the optimal neural network model,achieving classification accuracies of 96.14％on the Oxford 102 dataset and 94.12％on the Flower 17 dataset.Compared with methods like AGNAS,it improved accuracy by 1.40 percentage points on Oxford 102 and 3.09 percentage points on Flower 17.
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