Image classification model training method and device, image classification method and device and calculation equipment
A technology for classifying models and training methods, applied in computing, computer parts, character and pattern recognition, etc. It can solve problems such as poor model usage and model overfitting, and achieve better results, improved accuracy, and efficient recognition. The effect of classification difficulty
Inactive Publication Date: 2020-06-26
TENCENT TECH (SHENZHEN) CO LTD
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[0003] However, during the training process of the image classification model, if the training sample set contains unbalanced samples, it is easy to overfit the model, which makes the trained model less effective on some samples.
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[0068] In order to better understand the technical solutions provided by the embodiments of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation manners.
[0069] Artificial Intelligence (AI): It is a theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technique of computer science that attempts to understand the nature of intelligence and produce a new kind of intelligent machine that can respond in a similar way to human intelligence. Artificial intelligence is to study the design principles and implementation methods of various intelligent machines, so that the machines have the functions of perception, reasoning and decis...
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The invention relates to the technical field of artificial intelligence, and provides an image classification model training method, an image classification method, an image classification device andcalculation equipment, which are used for improving the processing capability of a trained model for a difficult sample. The method comprises the steps of training an image prediction model based on afirst image sample set; obtaining a difficulty estimation value of each second image sample in the second image sample set based on the image prediction model, wherein each second image sample has atarget classification label; training a first image classification model based on the second image sample set until the training loss of the first image classification model meets the target loss, andobtaining a second image classification model; wherein the training loss of the first image classification model is obtained by weighting the classification loss of each second image sample in the second image sample set, and the weighting weight corresponding to the classification loss of each second image sample is determined according to the difficulty estimation value of each second image sample.
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technical field [0001] The present application relates to the technical field of artificial intelligence, and in particular to an image classification model training method, image classification method, device and computing equipment. Background technique [0002] The image classification model generally refers to the model used for classification. At present, most image classification models are trained in advance through supervised learning, that is, the classification results corresponding to the training samples are predicted by the image classification model, and the model parameters are adjusted to make the output of the image classification model It is closer to the real classification result. [0003] However, during the training process of the image classification model, if the training sample set contains unbalanced samples, it is easy to overfit the model, which makes the trained model less effective on some samples. Contents of the invention [0004] Embodimen...
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IPC IPC(8): G06K9/62
CPCG06F18/2155G06F18/22G06F18/2414G06F18/2415
Inventor 张恩伟蒋忻洋孙星余宗桥彭湃郭晓威黄小明黄飞跃吴永坚
Owner TENCENT TECH (SHENZHEN) CO LTD