Image recognition model generation method and device, computer equipment and storage medium

An image recognition and model generation technology, applied in the field of artificial intelligence, can solve the problems of poor recognition effect, poor accuracy, and performance discount of image recognition models, etc.
CN111950656AActive Publication Date: 2020-11-17SHENZHEN SMARTMORE TECH CO LTD
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Patent Information

Authority / Receiving Office
CN Β· China
Current Assignee / Owner
SHENZHEN SMARTMORE TECH CO LTD
Publication Date
2020-11-17

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Abstract

The invention relates to an image recognition model generation method and device, computer equipment and a storage medium. The method comprises the steps of obtaining a sample image set; wherein the sample image set comprises a plurality of sample image subsets of which the number of images is sequentially decreased, and the plurality of sample image subsets comprise the same number of image categories; training a to-be-trained image recognition model according to the sample image set to obtain a loss value of the to-be-trained image recognition model; wherein the to-be-trained image recognition model comprises a plurality of branch neural networks; wherein the loss value comprises a target classification loss value and a classification loss value, the target classification loss value is aloss value of the model for the sample image set, and the classification loss value is a loss value of the branch neural network for the corresponding sample image subset; and adjusting model parameters according to the loss value until the loss value is lower than a preset threshold. According to the invention, the image types with a small number of images in the training process can be fully trained, and the effect of image recognition model generation is improved.
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Description

technical field

[0001] The present application relates to the technical field of artificial intelligence, and in particular, to a method, apparatus, computer equipment and storage medium for generating an image recognition model. Background technique

[0002] In deep learning, image recognition technology has made tremendous progress. However, these advances are inseparable from large-scale datasets, such as ImageNet, COCO, etc. Usually, these large-scale datasets are class-balanced; but in real-world scenarios, the data we can obtain usually follows a long-tailed distribution, that is, a small number of classes contain a lot of image data, while most classes contain Image data is less.

[0003] Using this kind of data that conforms to the long-tailed distribution to train the neural network, the result is usually that the neural network can identify a small number of categories with more image data, and identify most categories with less image data. The accuracy of the i...

Claims

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