Research of image recognition method based on enhanced inception-ResNet-V2

In order to improve the accuracy of CNN (convolutional neural network) in image classification, an enhanced Inception-ResNet-v2 model based on CNN is designed through the comparative study and analysis of the structure of classification model. This paper proposes to use multi-scale depthwise separable convolution to replace the convolution structure in Inception-ResNet-v2 model, which can reduce the amount of model parameters and extract features under different receptive fields. At the same time, this paper establishes channel filtering module based on global information comparison to filter and join channels, which realizes the effective extraction of features. Finally, through data enhancement, batch normalization and learning rate adjustment, the effect of the model used in this paper is better than most other models in each dataset, and the accuracy rate can reach 94.8%.

Automated summary

In order to improve the accuracy of CNN in image classification, an enhanced Inception-ResNet-v2 model based on CNN is designed by comparing and analyzing the structure of classification models. The use of multi-scale depthwise separable convolution reduces the amount of model parameters and extracts features under different receptive fields. The establishment of a channel filtering module based on global information comparison enables effective feature extraction, and the model achieves better accuracy than most other models in each dataset, with an accuracy rate of 94.8%.