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Image processing method and device based on convolutional neural network model

A convolutional neural network and neural network technology, applied in biological neural network models, image data processing, neural architecture, etc., can solve problems such as large energy consumption, and achieve the effect of improving accuracy and reducing errors

Active Publication Date: 2019-10-22
HUAWEI TECH CO LTD
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Problems solved by technology

From the perspective of the required storage space, the storage and transmission of model parameters also consume a lot of energy

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  • Image processing method and device based on convolutional neural network model
  • Image processing method and device based on convolutional neural network model
  • Image processing method and device based on convolutional neural network model

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[0038] The technical solution in this application will be described below with reference to the accompanying drawings.

[0039]For ease of understanding, the neural network is first introduced in detail. A neural network generally includes multiple neural network layers, and each neural network layer can implement different calculations or operations. Common neural network layers include convolution layers, pooling layers, and full-connection layers.

[0040] figure 1 It is the basic frame diagram of convolutional neural networks (CNN). see figure 1 , the convolutional neural network includes convolutional layers, pooling layers, and fully connected layers. Wherein, multiple convolutional layers and multiple pooling layers are arranged alternately, and the convolutional layer may be followed by a convolutional layer or a pooling layer.

[0041] The convolutional layer is mainly used to perform convolution operation on the input matrix, and the pooling layer is mainly used...

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Abstract

The invention provides an image processing method and device based on a convolutional neural network model. The method comprises: obtaining a first weight parameter set corresponding to a neural network layer, wherein the first weight parameter set comprises N1 first weight parameters, and N1 is an integer larger than or equal to 1; calculating the ratio of the N1 first weight parameters to the first numerical value m respectively, and obtaining N1 second weight parameters, wherein m is larger than or equal to |Wmax| and smaller than or equal to 2|Wmax|, and Wmax is the weight parameter with the maximum absolute value in the first weight parameter set; quantizing the N1 second weight parameters into the sum of at least two Q powers of 2, and obtaining N1 third weight parameters, wherein Qis smaller than or equal to 0, and Q is an integer; and obtaining a to-be-processed image; and processing the to-be-processed image according to the N1 third weight parameters to obtain an output image. According to the invention, errors caused by weight quantization can be reduced, so that precision loss is reduced.

Description

technical field [0001] The present application relates to the field of image processing, and more specifically, to an image processing method and device based on a convolutional neural network model. Background technique [0002] In recent years, neural networks, especially convolutional neural networks, have achieved great success in image processing and image recognition applications. A typical convolutional neural network is generally composed of multiple convolutional layers, fully connected layers, etc. From a computational point of view, multiplication is the main bottleneck. From the perspective of the required storage space, the storage and transmission of model parameters also consume a lot of energy. Many researchers are studying methods of compressing and accelerating neural networks, that is, while reducing the storage space of the model, the amount of calculation (that is, the number of multiplications) can also be greatly reduced. [0003] Among them, quantiz...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06T3/40
CPCG06T3/4053G06N3/045G06T3/40G06N3/04
Inventor 胡慧
Owner HUAWEI TECH CO LTD
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