Image classification method and device, electronic equipment and readable storage medium

Abstract

The invention relates to the field of intelligent decision, and discloses an image classification method, which comprises the following steps: training a pre-constructed first convolutional neural network model by using a first annotated image set to obtain a first image classification model; performing image screening and segmentation processing on the to-be-labeled image set to obtain a segmented image set; performing classification labeling on the segmented image set by using a first image classification model to obtain a second labeled image set; combining the first annotated image set and the second annotated image set to obtain an annotated image set; using the annotation image set to carry out iteration annotation training on a pre-constructed second convolutional neural network model to obtain a target image classification model; and classifying the to-be-classified image by using the target image classification model to obtain a classification result. The invention also relates to a block chain technology, and the annotated image set can be stored in a block chain node. The invention further provides an image classification device, electronic equipment and a storage medium. According to the invention, the accuracy of image classification can be improved.

Description

technical field [0001] The invention relates to the field of intelligent decision-making, in particular to an image classification method, device, electronic equipment and readable storage medium. Background technique [0002] With the development of science and technology, artificial intelligence technology is gradually applied in various fields. For example, in order to better obtain information from crop images and judge the growth of crops, people gradually use artificial intelligence to classify crop images. [0003] At present, the main application is to use tagged images to train deep learning models to classify images, such as using models to classify crop images to judge crop growth stages (for example: wheat growth is divided into emergence, rooting, leaf growth, tillering, jointing, booting, and earing) , flowering, and fruiting stages), so as to judge the growth of crops and provide suggestions for planters. [0004] However, due to the low efficiency of image l...

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Problems solved by technology

[0004] However, due to the low efficiency of image labeling, it is difficult to obtain labeled images, resulting in fewer training samples for the model, affecting the training effect of the model, and resulting in lower accuracy of image classification

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