An image classification method combining darknet and capsulenet models

Abstract

The invention discloses an image classification method integrating DarkNet and CapsuleNet models, which belongs to the technical field of image processing and solves the problem of poor classification effect due to unbalanced data. The technical scheme includes the following steps: constructing a DarkNet-Capsule network fusion classification model, realizing Define the loss function of the fusion classification model, input the image to be classified in the fusion classification model, use DarkNet for forward training, and extract the feature map of the target image; further process the feature map of the target image, and complete the error back propagation update through loss. parameters of the entire network; after multiple rounds of iterative learning, the fusion classification model is used to complete the image classification; in the field of image classification, the invention can further improve the classification accuracy when the data is unbalanced, and also lays a solid foundation for the research of machine vision. a more solid foundation.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to an image classification method integrating DarkNet and CapsuleNet models. Background technique [0002] Image classification is an important technology in the field of image processing. In recent years, with the development of deep learning, image classification technology has achieved tremendous development. [0003] The DarkNet model is an improved image feature extraction model based on the residual concept in the YOLO detection framework. It not only has the property of the residual network to avoid network degradation, but also reduces the amount of parameters of the model. However, as the amount of training data decreases, the generalization performance of the model will deteriorate, resulting in a sharp drop in classification accuracy. [0004] The CapsuleNet model proposes that convolutional neural networks use convolution kernels to extract image features, whic...

AI Technical Summary

Problems solved by technology

[0006] The present invention overcomes the deficiencies of the prior art, provides an image classification method that integrates DarkNet and CapsuleNet models, and solves the problem of poor classification effect due to data imbalance

Images