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A multi-label classification method and system

A classification method and multi-label technology, applied in the field of multi-label classification methods and systems, can solve problems such as inability to accurately identify multiple targets, and achieve the effect of avoiding missed identification

Active Publication Date: 2020-09-08
苏州飞搜科技有限公司
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Problems solved by technology

[0004] Embodiments of the present invention provide a method and system for classifying labels to solve the problem that multiple targets cannot be accurately identified in the prior art

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  • A multi-label classification method and system
  • A multi-label classification method and system
  • A multi-label classification method and system

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Embodiment Construction

[0021] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0022] figure 1 It is a flowchart of a multi-label classification method according to an embodiment of the present invention, such as figure 1 As shown, the method includes:

[0023] S1. Obtain all target objects in the image to be tested according to the image to be tested and the trained improved neural network, wherein the improved neural net...

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Abstract

An embodiment of the present invention provides a multi-label classification method and system, the method includes: according to the image to be tested and the improved neural network after training, obtain all target objects in the image to be tested, wherein the improved neural network passes Obtained by combining a neural network with an attention mechanism. A multi-label classification method provided by the embodiment of the present invention combines the attention mechanism with the neural network to highlight the importance of each target object in the image to be tested, so that when extracting multiple targets, it can be more accurate Each target object is recognized, avoiding the problem of missing recognition in the prior art.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of object recognition and classification, and in particular to a multi-label classification method and system. Background technique [0002] In the process of multi-label classification, a picture often contains multiple targets. In the prior art, for image multi-label classification tasks, the main deep learning method is to determine an input picture size and then train on the data set. , by setting multiple binary classifiers, if the output of the binary classifier of a certain class is 0, it means that the picture contains this class. [0003] However, there are many cases of false detection by this method. If the response value of the relevant response area on the last feature layer is small, the model will not be able to determine whether this category is included. Contents of the invention [0004] Embodiments of the present invention provide a method and system for classifyin...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06N3/08
CPCG06N3/08G06F18/24
Inventor 雷宇董远白洪亮熊风烨
Owner 苏州飞搜科技有限公司
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