Picture screening method, system and equipment, and storage medium

Abstract

The invention provides a picture screening method, system and equipment, and a storage medium. The method comprises the following steps: obtaining to-be-tested pictures; scoring the to-be-tested pictures by using the trained target deep learning model to obtain a score value of each to-be-tested picture, wherein the target deep learning model is obtained by training an initial deep learning model based on a second training set, the initial deep learning model is obtained by training a preset deep learning model based on a first training set, the second learning rate in the initial deep learning model training process is smaller than the first learning rate in the preset deep learning model training process, and the preset deep learning model is a Transform model; and screening the to-be-tested pictures according to the score values. The accuracy of screening the tourism display pictures is improved.

Description

technical field [0001] The present invention relates to the field of computer technology, in particular to a picture screening method, system, equipment and storage medium. Background technique [0002] For the OTA (Online Travel Agency, online travel agency) platform, when promoting various tourist attractions, it is necessary to select pictures with better parameters including picture quality and shooting angle as display pictures to attract tourists. That is, each picture needs to be scored, and pictures with higher scores are screened out for recommended display. [0003] In the prior art, the convolutional neural network is generally used to score pictures, but the convolutional neural network cannot capture the internal relationship of the image blocks inside the picture, and it is impossible to understand the content of the picture more accurately, so as to achieve a more accurate score for the provided picture the goal of. That is to say, these scoring models are l...

AI Technical Summary

Problems solved by technology

[0003] In the prior art, the convolutional neural network is generally used to score pictures, but the convolutional neural network cannot capture the internal relationship of the image blocks inside the picture, and it is impossible to understand the content of the picture more accurately, so as to achieve a more accurate score for the provided picture the goal of

That is, these scoring models are less accurate in scoring tourism display pictures

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