Image processing method and device, equipment, storage medium and computer program product

Abstract

The embodiment of the invention discloses an image processing method and device, equipment, a storage medium and a computer product, which can be applied to various scenes such as cloud technology, artificial intelligence, intelligent traffic and auxiliary driving. The image processing method comprises the steps of obtaining input features of a to-be-processed image; the dimension of the space where the input features are located is a first dimension; obtaining an image processing network, the image processing network comprises N convolution layers, and each convolution layer comprises at least one convolution kernel; the dimension of the space where the weight value corresponding to the convolution kernel in each convolution layer is located is the first dimension; calling N convolution layers to map the input features and the weight value corresponding to the convolution kernel in each convolution layer to a mapping space, and then performing convolution operation; the dimension of the mapping space is a second dimension, and the second dimension is greater than the first dimension; and performing image processing on the to-be-processed image according to the convolution operation result to obtain a processing result. According to the method of the invention, the accuracy of image classification or recognition can be improved.

Description

technical field [0001] The present application relates to the field of computer technology, and in particular to an image processing method, device, equipment, storage medium and computer program product. Background technique [0002] Deep learning network is the most popular machine learning method in recent years, driving the implementation of artificial intelligence algorithms in all walks of life. Among them, the convolutional neural network is one of the popular research directions in the deep learning network, which is mainly used to classify or recognize images. The convolutional layer is the most important part of the convolutional neural network. It mainly performs convolution calculations with the features of the input through the sharing of weight values. Usually, the convolution operation of the convolutional layer is to directly perform the inner product operation on the original space, but the expressive ability of the original space is limited, so that the fe...

AI Technical Summary

Problems solved by technology

Usually, the convolution operation of the convolutional layer is to directly perform the inner product operation on the original space, but the expressive ability of the original space is limited, so that the feature expressive ability of the convolutional neural network is not high, which leads to the image The classification or identification accuracy of the

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