Binary neural network model training method, image processing method and device

Abstract

The invention relates to an image processing technology in the field of computer vision in the field of artificial intelligence, and discloses a binary neural network model training method and device and an image processing method and device. The training method comprises the following steps: S1, determining a knowledge distillation framework, wherein the teacher network is a trained neural network model, and the student network is an initial binary neural network model M0; S2, training a binary neural network model Mj by using the (j + 1) th batch of images and a target loss function to obtain a binary neural network model Mj + 1, wherein the target loss function comprises an angle loss item, and the angle loss item is used for describing the difference between the included angle between the feature matrix and the weight matrix in the teacher network and the included angle between the feature matrix and the weight matrix in the student network; S3, when a preset condition is met, taking the binary neural network model Mj + 1 as a target binary neural network model; otherwise, enabling j to be equal to j + 1, and repeating the step S2. According to the embodiment of the invention, the prediction precision of the binary neural network model can be improved.

Description

technical field [0001] The present application relates to the technical field of artificial intelligence, and in particular to a training method of a binary neural network model, an image processing method and a device. Background technique [0002] Computer vision is an integral part of various intelligent / autonomous systems in various application fields, such as manufacturing, inspection, document analysis, medical diagnosis, it is a science about how to use cameras / video cameras and computers to get what we need, The knowledge of the data and information of the subject. To put it figuratively, it is to install eyes (cameras / video cameras) and brains (algorithms) on computers to replace human eyes to identify, track and measure targets, so that computers can perceive the environment. Because perception can be thought of as extracting information from sensory signals, computer vision can also be thought of as the science of how to make artificial systems "perceive" from im...

AI Technical Summary

Problems solved by technology

[0004] However, compared with the neural network model, the binary neural network model in the prior art has a much lower prediction accuracy

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