Image classification method and device, data processing method and device

Abstract

The invention provides an image classification method and device, and relates to the field of artificial intelligence, in particular to the field of computing vision. The image classification method comprises the following steps: obtaining a convolution kernel parameter of a reference convolution kernel of a neural network and a mask tensor of the neural network, and performing Hadamard operationon the reference convolution kernel of the neural network and the mask tensor corresponding to the reference convolution kernel to obtain a plurality of sub-convolution kernels; and performing convolution processing on the to-be-processed image according to the plurality of sub-convolution kernels, and classifying the to-be-processed image according to the convolution feature map finally obtainedby convolution to obtain a classification result of the to-be-processed image. Due to the fact that the mask tensor occupies a smaller storage space relative to the convolution kernel, some devices with limited storage resources can deploy a neural network comprising the reference convolution kernel and the mask tensor, and then image classification is achieved.

Description

technical field [0001] The present application relates to the field of artificial intelligence, and more specifically, relates to an image classification method, data processing method and device. Background technique [0002] With the rapid development of artificial intelligence technology, the processing capacity of neural networks has become stronger and stronger, and the parameters contained in neural networks are also increasing, which makes these neural networks often need to take up a lot of time when deploying or applying. Storage space to store the parameters of the neural network. This affects the deployment and application of neural networks on some devices with limited storage resources. [0003] Taking the neural network for image classification as an example, many neural networks used for image classification (especially some neural networks with complex network structures and powerful functions) are difficult to deploy to some storage devices because they con...

AI Technical Summary

Problems solved by technology

This affects the deployment and application of neural networks on some devices with limited storage resources

[0003] Taking the neural network for image classification as an example, many neural networks used for image classification (especially some neural networks with complex network structures and powerful functions) are difficult to deploy to some storage devices because they contain many parameters. Space is relatively limited on devices (eg, mobile phones, cameras, smart homes), which affects the application of neural networks

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