Model training method, device and equipment and storage medium

Abstract

The invention discloses a model training method, device and equipment and a storage medium, and belongs to the technical field of deep learning. The method comprises the following steps: obtaining a training video sample, wherein the training video sample comprises a training video and at least two real tags with an association relationship; respectively inputting the training video into an initial convolutional neural network and an initial recurrent neural network to obtain at least two training tags which are output by the initial convolutional neural network and the initial recurrent neural network and have an association relationship; and training the initial convolutional neural network and the initial recurrent neural network based on the difference between the at least two trainingtags and the difference between the at least two training tags and the at least two real tags to obtain a trained target convolutional neural network and a trained target recurrent neural network. According to the technical scheme provided by the embodiment of the invention, a method for training a convolutional neural network and a recurrent neural network capable of identifying associated features in a video at the same time is provided.

Description

technical field [0001] The present application relates to the technical field of deep learning, in particular to a model training method, device, equipment and storage medium. Background technique [0002] In practical applications, some features in the video can be identified by convolutional neural networks, and at the same time, other features in the video can be identified by cyclic neural networks. In related technologies, according to the features to be identified, the convolutional neural network can be selected to identify the video, or the cyclic neural network can be independently selected to identify the video. [0003] However, many features in the video are related. Identifying a feature alone and ignoring other features associated with it will cause a certain negative impact. In order to avoid this negative impact, convolution can be used at the same time. Neural networks and recurrent neural networks recognize associated features, and how to train convolution...

AI Technical Summary

Problems solved by technology

[0003] However, many features in the video are related. Identifying a feature alone and ignoring other features associated with it will cause a certain negative impact. In order to avoid this negative impact, convolution can be used at the same time. Neural networks and recurrent neural networks recognize associated features, and how to train convolutional neural networks and recurrent neural networks in this scenario has become an urgent problem to be solved

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