Image recognition method and device, electronic equipment and storage medium
A technology for image recognition and training images, applied in the field of image processing, can solve the problems of low accuracy and no distinction of branch importance, etc., and achieve the effect of improving accuracy, improving information extraction ability, and improving extraction ability
Pending Publication Date: 2022-07-15
INSPUR BEIJING ELECTRONICS INFORMATION IND
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However, although the model uses a multi-branch structure for network connection between each node, the importance of each branch is not distinguished, resulting in low accuracy when the model is applied to image recognition
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[0070] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of this application. In addition, in the embodiments of the present application, "first", "second", etc. are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.
[0071] The embodiment of the present application discloses an image recognition method, which improves the accuracy of image recognition.
[0072] see figure 2 , according to a flowchart of an image recognition method shown in an exemplary embodiment, ...
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The invention discloses an image recognition method and apparatus, an electronic device and a storage medium. The method comprises the steps of training model parameters of a convolutional neural network by using a training image and a corresponding category label; the convolutional neural network comprises a plurality of stacked basic units connected in series, each stacked basic unit comprises a plurality of branches connected in parallel, and the model parameters comprise weighting parameters and convolutional layer parameters corresponding to each branch; fusing the weighting parameter and the convolutional layer parameter corresponding to each branch in each stacked basic unit in the trained convolutional neural network to obtain a fusion parameter corresponding to each branch in each stacked basic unit; deploying a target convolutional neural network based on fusion parameters corresponding to each branch in each stacked basic unit on the basis of the trained convolutional neural network; the target image is acquired, and the target image is input into the target convolutional neural network for image recognition, so that the accuracy of the convolutional neural network for image recognition is improved.
Description
technical field [0001] The present application relates to the technical field of image processing, and more particularly, to an image recognition method, an apparatus, an electronic device, and a computer-readable storage medium. Background technique [0002] With the continuous development of artificial intelligence technology, artificial intelligence technology has been gradually applied to our lives. In the field of artificial intelligence technology, deep learning is one of the more typical technologies. The key to deep neural network lies in the design of network model structure. Generally speaking, the most direct way to improve network performance is to increase the depth and width of the neural network. Depth refers to the number of network layers, and width refers to the number of neurons (channels) in each layer. Following these two design points, the current commonly used network model structure design multi-branch network model structure design, such as ResNet's...
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IPC IPC(8): G06V10/774G06V10/82G06V10/80G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/214G06F18/253
Inventor 梁玲燕温东超董刚赵雅倩
Owner INSPUR BEIJING ELECTRONICS INFORMATION IND