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Image recognition and neural network model training method, device and system

A neural network model and image recognition technology, applied in the field of image processing, can solve the problem of unbalanced performance of traditional models, and achieve the effect of balancing image recognition performance

Active Publication Date: 2022-02-01
MEGVII BEIJINGTECH CO LTD
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  • Claims
  • Application Information

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Problems solved by technology

In short, for different data sets, the traditional model has the problem of unbalanced performance

Method used

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  • Image recognition and neural network model training method, device and system
  • Image recognition and neural network model training method, device and system
  • Image recognition and neural network model training method, device and system

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Embodiment Construction

[0047] In order to make the purpose, technical solution and advantages of the present application clearer, 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 embodiments described here are only used to explain the present application, and are not intended to limit the present application.

[0048] The image recognition method provided by this application can be applied, but not limited to, such as figure 1 shown in the application environment. Wherein, the photographing device 12 can obtain the image to be recognized of the object to be recognized, and send the image to be recognized to the computer device 11; the computer device 11 can extract the target image feature from the image to be recognized, and perform image processing according to the target image feature. Image recognition processing such as verification, image search, image clustering, etc. Wh...

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Abstract

The present application relates to an image recognition and neural network model training method, device, system and readable storage medium. The method includes: obtaining an image to be recognized; inputting the image to be recognized into a neural network model, and outputting target image features of the image to be recognized; the neural network model is trained based on each sample image belonging to a plurality of training data sets, and any two training The difference between the feature distances of the datasets corresponding to the dataset is less than the preset threshold; the feature distance of the dataset is the feature distance between the datasets or the feature distance within the dataset; according to the judgment threshold corresponding to the neural network model, the target image The features are subjected to image recognition processing to obtain the image recognition result of the image to be recognized. For different data sets, this method can show a more balanced image recognition performance.

Description

technical field [0001] The present application relates to the technical field of image processing, in particular to an image recognition and neural network model training method, device, system and readable storage medium. Background technique [0002] At present, face recognition tasks are divided into three categories, face verification (verifying whether multiple face images correspond to the same person), face search (finding the image most similar to the face image to be recognized in multiple face images in the bottom library) ) and face image clustering (classifying multiple face images to be recognized). At present, the popular method is to perform different types of face recognition tasks by training deep network models to convert face images into points in feature space (feature space). On this basis, the face recognition task is equivalent to training a good deep network model, converting the face image to be recognized into points in the feature space, that is, ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V40/168G06V40/172G06N3/045G06F18/23213G06F18/24
Inventor 王塑王泽荣刘宇赵俊杰杜佳慧肖琳程昌茂
Owner MEGVII BEIJINGTECH CO LTD
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