Model training method and device, apparatus and storage medium

Abstract

The embodiment of the invention provides a model training method and device, an apparatus and a storage medium, wherein, the method comprises the following steps of: acquiring a first image data set;Performing weighted summation processing on the images in the first image data set to obtain an incremental image; Training to obtain a first model based on the incremental image and the first image data set. The embodiment of the invention can ensure large-scale and effective incremental images under the condition that the image features are clear and unchanged.

Description

technical field [0001] The embodiments of the present invention relate to the field of computer technology, and in particular, to a model training method, device, equipment, and storage medium. Background technique [0002] With the rise of deep neural networks, artificial intelligence (AI for short) can already recognize and recognize image attributes like humans. For example, researchers at Stanford University trained an AI algorithm for diagnosing skin cancer through 130,000 images of skin diseases, and The diagnosis results reached the level of experts. [0003] At present, in the learning of deep neural networks, in addition to requiring a large amount of training data, there is also a very important prerequisite that there is a sufficient amount of labeled data. Taking the medical field as an example, in medical image processing, the labeling of image datasets is not only a diagnostic report, but also requires high-quality and fine labeling, such as image level and le...

AI Technical Summary

Problems solved by technology

Because medical image processing requires medical professional knowledge, that is to say, doctors are more familiar with these diseases before they can label. It is difficult for ordinary people to label, and the cost of labeling is still high

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