Neural network model training method and device and electronic equipment

Abstract

The invention provides a neural network model training method and device, electronic equipment and a computer readable storage medium. The neural network model training method of the neural network model comprises the following steps: executing initial training by utilizing a first training sample set to obtain an initial neural network model; performing prediction on the second training sample set by utilizing the initial neural network model to obtain a prediction result of each training sample in the second training sample set; determining a plurality of preferred samples from the second training sample set based on the prediction result; receiving a labeling result for the plurality of preferred samples, and adding the labeled plurality of preferred samples into a first training sample set to obtain an expanded first training sample set; performing update training by using the extended first training sample set to obtain an updated neural network model; under the condition that a training ending condition is met, ending the training; and repeating the prediction step, the preferred sample determination step, the sample expansion step and the training updating step under the condition that the training ending condition is not met.

Description

technical field [0001] The present disclosure relates to the field of artificial intelligence, and more specifically, the present disclosure relates to a neural network model training method, an image processing method, a neural network model training device, electronic equipment, and a computer-readable storage medium. Background technique [0002] Neural networks are a tool for large-scale, multi-parameter optimization. Relying on a large amount of training data, the neural network can learn hidden features that are difficult to summarize in the data, thereby completing multiple complex tasks, such as image semantic segmentation, object detection, action tracking, natural language translation, etc. Neural networks have been widely used in the artificial intelligence community. [0003] When using the neural network model to perform the above complex tasks such as image semantic segmentation, object detection, action tracking, natural language translation, etc., it is nece...

AI Technical Summary

Problems solved by technology

In addition, the decrease in the number of samples in the data set after sampling will make the training of the neural network model easier to overfit and lead to performance degradation

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