Image identification method and apparatus

Abstract

The invention discloses an image identification method and apparatus. The image identification method includes the steps: inputting an image to be identified into a trained first convolution neural network which includes P groups of convolution layers, wherein each group of convolution layers includes two convolution layers; the sizes of the convolution cores of the two convolution layers are respectively a 1*N first convolution core and an N*1 second convolution core; by means of each group of convolution layers in the first convolution neural network, extracting the characteristics of the image to be identified and obtaining the characteristics, of each group of convolution layers, to be extracted; and according to the characteristics, of each group of convolution layers, to be extracted, determining the identifying result of the image to be identified. The technical scheme of the image identification method and apparatus solves the problem that in the related technologies, a square convolution core causes high computational complexity and high computing cost.

Description

technical field [0001] The present disclosure relates to the field of convolution technology, in particular to an image recognition method and device. Background technique [0002] Convolutional Neural Network (CNN) is an efficient image recognition method. In CNN, the image undergoes a series of feature extraction and processing of convolutional layers, activation layers, pooling layers, and fully connected layers to obtain the recognition result of the image to be recognized. In related technologies, the calculation principle of the convolution layer is to use a square convolution kernel to slide on the input rectangular area in the form of a sliding window, and calculate the product of the convolution kernel and the value of the position every time it slides to a new position , the calculation amount of the square convolution kernel is O(N 2 ), the amount of calculation is relatively large, and the calculation cost is relatively high. Contents of the invention [000...

AI Technical Summary

Problems solved by technology

In related technologies, the calculation principle of the convolution layer is to use a square convolution kernel to slide on the input rectangular area in the form of a sliding window, and calculate the product of the convolution ker

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