Neural network training and image processing method and device, equipment and storage medium
A neural network training and neural network technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve problems such as difficulty in adapting to diverse scene requirements, single model function, and difficulty in adapting to multiple usage scenarios.
Pending Publication Date: 2021-12-14
SHENZHEN SENSETIME TECH CO LTD
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However, the current models used for text and image recognition are often difficult to apply to a variety of usage scenarios after training, resulting in a single model function that is difficult to adapt to diverse scene requirements
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[0047] In order to make the purpose, technical solutions and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only It is a part of the embodiments of the present disclosure, but not all of them. The components of the disclosed embodiments generally described and illustrated in the figures herein may be arranged and designed in a variety of different configurations. Accordingly, the following detailed description of the embodiments of the present disclosure provided in the accompanying drawings is not intended to limit the scope of the claimed disclosure, but merely represents selected embodiments of the present disclosure. Based on the embodiments of the present disclosure, all other embodiments obtained by those ski...
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The invention provides a neural network training and image processing method and device, equipment and a storage medium. The method comprises the steps: obtaining at least two types of training samples; based on a target sampling proportion between different types of training samples in the at least two types of training samples, reading a target training sample used for each time of training in multiple times of training from the obtained at least two types of training samples; wherein the number of different types of target training samples read each time accords with the target sampling proportion; and training a target neural network based on the read target training sample, the target neural network being used for recognizing different types of to-be-recognized images. According to the method, the selection number of different types of training samples can be well controlled based on the target sampling proportion, so that the influence of the training samples with relatively large direct mixed data size difference on feature learning can be reduced to a certain extent, and the recognition precision of the target neural network is improved.
Description
technical field [0001] The present disclosure relates to the technical field of character recognition, and in particular, to a method, device, device and storage medium for neural network training and image processing. Background technique [0002] With the rapid development of image technology and the gradual expansion of market demand, image character recognition technology has received extensive attention. Image text recognition is the process of converting text images into a series of symbols that can be represented and processed by a computer. However, the current models used for text and image recognition are often difficult to apply to a variety of usage scenarios after training, resulting in a model with a single function and difficult to adapt to the needs of diverse scenarios. Contents of the invention [0003] Embodiments of the present disclosure at least provide a method, device, device, and storage medium for neural network training and image processing. ...
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IPC IPC(8): G06K9/32G06K9/62G06N3/04G06N3/08
CPCG06N3/04G06N3/08G06F18/241
Inventor 张正夫梁鼎吴一超
Owner SHENZHEN SENSETIME TECH CO LTD