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Image classification method and system, electronic equipment and storage medium

A classification method and image technology, applied in neural learning methods, character and pattern recognition, instruments, etc., can solve problems such as poor performance of image classification models, inability to detect and classify images, etc., to improve experience, improve performance, and be fast and accurate. The effect of detection and classification

Pending Publication Date: 2022-02-15
携程旅游信息技术(上海)有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is to provide an image classification method, system, electronic equipment and storage medium

Method used

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  • Image classification method and system, electronic equipment and storage medium
  • Image classification method and system, electronic equipment and storage medium
  • Image classification method and system, electronic equipment and storage medium

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Experimental program
Comparison scheme
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Embodiment 1

[0053] This embodiment provides an image classification method, such as figure 1 As shown, the image classification method includes the following steps:

[0054] S101. Construct a data set, where the data set includes several training data.

[0055] Image classification can classify images into one of several categories according to different image features. As an example, the algorithm model in the embodiment of the present application is applied to OTA (Online Travel). The pictures selected for constructing the dataset are pictures of various types of hotels under OTA, including but not limited to indoor, outdoor, room type, landscape and other pictures, so as to ensure the diversity of the dataset. For ease of description, it is assumed that the data set in the embodiment of the present application is used to train a picture rotation judgment model, and of course the present invention is not limited to judging picture rotation.

[0056] As an optional implementation, diff...

Embodiment 2

[0121] This embodiment provides an image classification system, such as Figure 4 As shown, the image classification system includes a data set construction module 31 , a model construction module 32 , a teacher model training module 33 , a model result acquisition module 34 , a model result combination module 35 , a student model training module 36 , and a classification prediction module 37 .

[0122] As an example, the algorithm model in the embodiment of the present application is applied to OTA. The pictures selected for constructing the dataset are pictures of various types of hotels under OTA, including but not limited to indoor, outdoor, room type, landscape and other pictures, so as to ensure the diversity of the dataset. For ease of description, it is assumed that the data set in the embodiment of the present application is used to train a picture rotation judgment model, and of course the present invention is not limited to judging picture rotation.

[0123] As an ...

Embodiment 3

[0156] Figure 5A schematic structural diagram of an electronic device provided in this embodiment. The electronic device includes a memory, a processor, and a computer program stored on the memory and operable on the processor, and the processor implements the image classification method of Embodiment 1 when executing the program. Figure 5 The electronic device 40 shown is only an example, and should not impose any limitation on the functions and application scope of the embodiments of the present invention.

[0157] Such as Figure 5 As shown, electronic device 40 may take the form of a general-purpose computing device, which may be a server device, for example. Components of the electronic device 40 may include, but are not limited to: at least one processor 41 , at least one memory 42 , and a bus 43 connecting different system components (including the memory 42 and the processor 41 ).

[0158] The bus 43 includes a data bus, an address bus and a control bus.

[0159]...

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Abstract

The invention discloses an image classification method and system, electronic equipment and a storage medium. The image classification method comprises the following steps: constructing a data set; constructing at least two classification models, training the classification models to obtain teacher models, and inputting training data to the teacher models to obtain first teacher model results; combining the first teacher model results to obtain a second teacher model result; constructing a student model, and inputting training data to the student model to obtain a student model result; training the student model according to the second teacher model result and the student model result to obtain a target model; and performing image classification according to the target model. According to the method and system, the multiple teacher models are weighted, the student model adopts an autonomous weight learning mode, and the advantages of different teacher models are selected for learning, so that the calculation speed of the models and the accuracy of model prediction are ensured, the images can be quickly and accurately detected and classified, and the accuracy of the picture information is ensured.

Description

technical field [0001] The present invention relates to the technical field of image classification, in particular to an image classification method, system, electronic equipment and storage medium. Background technique [0002] With the development of the artificial intelligence era, deep learning models have been widely used in the field of image classification technology. The complex model has high prediction accuracy but the number of parameters is too large, while the accuracy of the simple model is relatively low. In practical applications, we need fewer parameters. High-precision models, and knowledge distillation, as an important model compression method, can transfer the knowledge of complex models to simple models, so that simple models can improve their own accuracy without changing the number of parameters. Among them, complex models It is also called the teacher model, and the simple model is called the student model. Among them, the accuracy of the teacher mode...

Claims

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Application Information

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IPC IPC(8): G06V10/764G06V10/34G06V10/774G06V10/82G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06N3/045G06F18/2414G06F18/254G06F18/214
Inventor 彭佳慧成丹妮罗超邹宇李巍
Owner 携程旅游信息技术(上海)有限公司
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