Pruning method applied to target detection and terminal

Abstract

The invention discloses a pruning method applied to target detection and a terminal. The pruning method comprises the following steps: training a preset target detection algorithm until convergence; determining a pruning proportion corresponding to each network layer according to the norm of the weight of each network layer in the converged deep neural network model of the target detection algorithm; clustering the weights of all channels in the network layer corresponding to the pruning proportion according to the pruning proportion, and determining a clustering center corresponding to each network layer; according to the clustering center and the pruning proportion corresponding to each network layer, pruning channels in the network layer where the network layer is located; realizing pruning of channels of each network layer in the deep neural network model based on norm weight clustering; and deleting redundant channels to compress the deep neural network model, thus the pruning process is simple, consumed time is short, dependence is little in the pruning process, dependence on any parameter and a specific layer is not needed, and meanwhile precision loss is reduced while compression is guaranteed.

Description

technical field [0001] The invention relates to the technical field of computer vision model compression, in particular to a pruning method and terminal applied to target detection. Background technique [0002] In computer vision, model compression has always been an important direction for applying deep learning to embedded devices, and model pruning is one of the important sub-directions. Compared with quantization and distillation, pruning is easier to use and more Suitable for deployment and other advantages. Among them, model pruning is to measure the importance of each neuron weight in deep learning by different methods, and subtract unimportant neurons according to the importance of neurons, so as to achieve the purpose of compressing the model. [0003] As a commonly used model compression method, model pruning is widely used to reduce the heavy calculation of deep models. It is a research hotspot in the current academic and industrial circles, such as: [0004] (...

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Problems solved by technology

[0007] Although all the methods proposed above can compress the model, the pruning in the method proposed in the first article is only a fine-grained parameter-level pruning, and it must cooperate with corresponding hardware (such as FPGA) to complete the acceleration. In addition, the quantization scheme and the conversion model format storage scheme must be implemented with fixed hardware, which has considerable limitations; in the method proposed in the second article, the gamma parameter needs to be sparse before pruning, and the network recovery accuracy and pruning should be fine-tuned again. , re-fine-tuning and other steps, and the situation of each data set is different, requires a lot of experience and time, and must rely on the BN layer in the model; although the method proposed in the third article is simple, it has problems when facing complex networks. The problem of loss of precision

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