Image recognition method and device, computer equipment, and computer readable medium

Abstract

The invention provides an image recognition method and device, computer equipment, and a computer readable medium. The method comprises the steps: obtaining a to-be-recognized image; obtaining a prediction class label of the to-be-recognized image according to the to-be-recognized image and a pre-trained image recognition model, wherein the pre-trained image recognition model employs a convolution neural network model and the convolution neural network model comprises at least one nuclear pool layer which enables the channel dimension to be changed into many dimensions from one dimension; and recognizing the type of the to-be-recognized image according to the prediction class label of the to-be-recognized image. According to the technical scheme of the invention, the method can achieve the recognition of the type of an image with the finer granularity through employing the image recognition model with the nuclear pool layer when the method is used for the recognition of the to-be-recognized image, and effectively improves the accuracy and efficiency of image recognition.

Description

[0001] 【Technical field】 [0002] The present invention relates to the field of computer application technology, in particular to a picture recognition method and device, computer equipment, and a computer-readable medium. [0003] 【Background technique】 [0004] In many application scenarios, it is necessary to classify pictures to facilitate the classification and management of pictures. When there are few pictures, the pictures can be classified manually. However, with the development of network technology, it is usually necessary to classify thousands of pictures in the network scene, and manual processing has become too impractical. Therefore, in the network scene, how to intelligently identify pictures, classification becomes more important. [0005] In the prior art, the convolutional neural network model can be used to identify the category of the picture. The existing convolutional neural network model includes convolution operations and pooling operations, wherein t...

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Problems solved by technology

[0006] However, these three pooling operations in the existing convolutional neural network model will not enrich the granularity of image recognition, resulting in the use of the convolutional neural network model in the prior art to have a larger granularity for image recognition and accuracy for image recognition. Poor sex

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