Data processing method and device, storage medium and electronic equipment

Abstract

The embodiment of the invention discloses a data processing method and device, a storage medium and electronic equipment. The data processing method comprises the following steps: acquiring a first game image corresponding to a first game and a second game image corresponding to a second game; training a preset game depth model based on the first game image to obtain a game depth model corresponding to the first game; according to the image content feature information corresponding to the first game image and the image style feature information corresponding to the second game image, carryingout image style conversion on the first game image to obtain a converted first game image, wherein the converted first game image is a first game image with the image style of the second game; and adjusting the game depth model by adopting the converted first game image to obtain a target game depth model. According to the scheme, the generalization ability of the model can be improved.

Description

technical field [0001] The present application relates to the technical field of information processing, and in particular to a data processing method, device, storage medium and electronic equipment. Background technique [0002] With the development of artificial intelligence, the use of machine learning models is becoming more and more widespread. By training the machine learning model, the machine learning model performs data processing. For example, the game depth model is trained so that the trained game depth model can control virtual objects in the game. [0003] However, due to the different styles of different games, the game depth model corresponding to game A does not perform well in depth estimation on game B. The current conventional method is based on a hard-coded game style conversion method. Hard-coding is only artificially designed rules for data Expansion, such as randomly intercepting, flipping, exchanging color channels, and adding random noise to game...

AI Technical Summary

Problems solved by technology

[0003] However, due to the different styles of different games, the game depth model corresponding to game A does not perform well in depth estimation on game B. The current conventional practice is based on hard-coded game style conversion methods. Hard-coding is only artificially designed rules for data Expansion, such as randomly intercepting, flipping, exchanging color channels, and adding random noise to game images, can enhance the generalization ability of the model to a certain extent through these operations, but its disadvantage is that the generated results are not natural and cannot simulate new games style of

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