Image processing model training method, image processing method and related equipment

Abstract

The invention belongs to the technical field of artificial intelligence, and particularly relates to an image processing model training method, an image processing method, an image processing model training device, an image processing device, a computer readable medium and electronic equipment. The image processing model training method in the embodiment of the invention comprises the steps of obtaining a first image processing model which is obtained through quantitative perception training and is embedded with a pseudo-quantization operator, wherein the pseudo-quantization operator is used for performing quantitative processing and inverse quantization processing on image data; removing a pseudo quantization operator in the first image processing model to obtain a second image processing model having the same model structure as the first image processing model; and taking the second image processing model as a teacher model, and performing knowledge distillation training on the first image processing model to obtain a target image processing model. The stability of the image processing model can be improved.

Description

technical field [0001] The present application belongs to the technical field of artificial intelligence, and specifically relates to an image processing model training method, an image processing method, an image processing model training device, an image processing device, a computer-readable medium, and electronic equipment. Background technique [0002] Deep neural networks can well complete image processing tasks such as image classification and object detection, but the processing effect of deep neural networks in image processing tasks is generally proportional to the amount of network parameters and network calculations, which also makes related models more difficult for hardware devices. Higher computing power is required. Therefore, how to reduce the hardware requirements of the model is an urgent problem to be solved. Contents of the invention [0003] The purpose of this application is to provide an image processing model training method, an image processing m...

AI Technical Summary

Problems solved by technology

[0002] Deep neural networks can well complete image processing tasks such as image classification and object detection, but the processing effect of deep neural networks in image processing tasks is generally proportional to the amount of network parameters and network calculations, which also makes related models more difficult for hardware devices. Higher computing power is required

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