Convolutional neural network training method and system, object classification method and classifier

A technology of convolutional neural network and training method, applied to a method and classifier of object classification, convolutional neural network training method and system field, which can solve the problems of reducing the accuracy of object classification and prone to unbalanced sample distribution

Active Publication Date: 2016-11-16
BEIJING SENSETIME TECH DEV CO LTD
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Problems solved by technology

[0003] However, the distribution of samples in the training sample set used to train CNN is prone to imbalance
Correspondingly, directly using an unbalanced sample set to train a CNN will reduce the accuracy of object classification

Method used

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  • Convolutional neural network training method and system, object classification method and classifier
  • Convolutional neural network training method and system, object classification method and classifier
  • Convolutional neural network training method and system, object classification method and classifier

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Embodiment Construction

[0040] The present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific implementation manners described herein are only used to explain the present application, rather than to limit the present application. In addition, it should be noted that, for ease of description, only parts relevant to the present application are shown in the drawings. Hereinafter, the present application will be described in detail with reference to the accompanying drawings and in combination with embodiments.

[0041] figure 1 A process 1000 of training a CNN according to an exemplary embodiment of the present application is shown. First, in step S1010, the class (class) containing the number of training samples greater than a predetermined value in the training sample set can be divided into a plurality of sub-categories (cluster), wherein the number of training samples contained in each divid...

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Abstract

The invention discloses a convolutional neural network training method and system, an object classification method and a classifier. The convolutional neural network training method comprises the steps of dividing a category with a training sample number greater than a predetermined value in a training sample set into a plurality of sub-categories, wherein a training sample number of each divided sub-category is smaller than or equal to the predetermined value; and training a convolutional neural network according to the divided sub-categories and undivided categories in the training sample set. According to the training method and system, the object classification method and the classifier, the tolerance of a training process to imbalance of the training sample set is enhanced, and correspondingly, the training quality of the CNN (Convolutional Neural Network) and the accuracy of performing object classification by using the CNN are improved.

Description

technical field [0001] The present application relates to the field of deep learning, in particular to a convolutional neural network training method and system, an object classification method and a classifier. Background technique [0002] As a typical representative of deep learning network, CNN (Conventional Neural Network, Convolutional Neural Network) has been more and more widely used in the field of image recognition. Classifying objects is a common operation in the field of image recognition. Traditionally, a pre-classified training sample set is generally used to train a CNN for object classification, and then the trained CNN is used to classify objects, such as binary classification or multi-classification. [0003] However, the distribution of samples in the training sample set used to train CNN is prone to imbalance. Correspondingly, training CNN directly with an unbalanced sample set will reduce the accuracy of object classification. Contents of the inventi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/23213G06F18/217G06F18/2415
Inventor 汤晓鸥黄琛李亦宁吕健勤
Owner BEIJING SENSETIME TECH DEV CO LTD
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