Method for acquiring neural network training set and system thereof

Abstract

The invention provides a method for acquiring a neural network training set and a system thereof. The method comprises the steps of: S100, acquiring category information of an image set to be filtered; S200, filtering a target image, and obtaining a target image data set corresponding to the image set to be filtered, wherein the similarity of the target image and the sample image reaches the imagedata of a preset similarity threshold, and the sample image is a template image corresponding to the category information of the image set to be filtered; and S300, performing a confrontation training on the target image data to obtain a sample data set. The invention is capable of reducing manual screening of sample data sets, improving screening efficiency and screening reliability, and improving the accuracy of the neural network.

Description

technical field [0001] The invention relates to the field of data processing, in particular to a method for obtaining a training set of a neural network and a system thereof. Background technique [0002] In recent years, with the continuous development of computer vision technology, especially the rapid development of neural network models, people's demand for image data required for computer vision training, especially for image data with accurate label information, is increasing. [0003] Convolutional Neural Networks (CNN) is a kind of deep learning algorithm and an important processing and analysis tool in the field of image recognition. In recent years, it has become one of the research hotspots in many scientific fields. The advantage of the neural network model algorithm is that it does not need to use any manually labeled features when training the model, and can automatically explore the features hidden in the input variables. At the same time, the weight sharing f...

AI Technical Summary

Problems solved by technology

The problem is that the workload is extremely huge, the screening results are highly subjective, and the screening results are prone to errors. At the same time, using the wrong image sample data set to train the neural network will bring wrong classification results.

When traditional web crawler technology is used to obtain image data, the quality of the crawled images generally shows a downward trend as the number of crawls increases, resulting in large noise in the data crawled by traditional web crawlers, which affects subsequent neural network-based models. Training Results for Image Recognition

Images