Image processing method and device and computer readable storage medium

Abstract

The invention discloses an image processing method, image processing equipment and a computer readable medium, and is applied to the technical field of image processing. The method comprises the following steps: inputting a target image into a preset field detection model to obtain a target field level image corresponding to the target image; determining a plurality of target crowdsourcing users from the crowdsourcing users according to the annotation information of the crowdsourcing users, and allocating the target field level image to the plurality of target crowdsourcing users for annotation; respectively obtaining labeling results of the plurality of target crowdsourcing users on the target field level image to obtain a plurality of labeling results; And determining a target labeling result from the plurality of labeling results according to a preset determination rule, and performing training according to the target field-level image and the target labeling result to obtain an image recognition model. By adopting the method and the device, the data annotation efficiency is improved, the annotation time is saved, and the cost is reduced.

Description

technical field [0001] The present application relates to the technical field of image processing, and in particular to an image processing method, device, and computer-readable storage medium. Background technique [0002] The traditional method of obtaining labeled data sets is to hire experienced personnel to manually label the data sets. For example, for image recognition business, most of them are based on deep learning. Effective school models are obtained through supervised learning training to complete the corresponding tasks. recognition task. When building a learning model, it is necessary to train the model with a large amount of training data such as images and their corresponding category labels, and these training data need to be labeled manually. Since the data samples involved are tens of thousands, and sometimes even reach millions or more, it is very labor-intensive and time-consuming to label data of this magnitude with only experienced people, resulting ...

AI Technical Summary

Problems solved by technology

Since the data samples involved are tens of thousands, sometimes even reaching millions or more, it is very labor-intensive and time-consuming to label data of this magnitude with only experienced people, resulting in long time-consuming labeling, low efficiency, and high cost. high cost

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