Method of monitoring image mark quality and apparatus thereof

Abstract

The invention discloses a method of monitoring image mark quality and an apparatus thereof. The method comprises the following steps of executing an acquisition step and acquiring accuracy of mark data of a single task; issuing an image to be marked to a mark account, wherein the image to be marked includes a sample image of a predetermined proportion; acquiring manual mark data of the image to be marked, wherein the manual mark data includes current mark data of the sample image; acquiring an accurate specific value of the current mark data relative to standard mark data of the sample image; responding to the specific value which is greater than a pre-determined value; inputting the image to be marked into a picture identification model so as to acquire automatic mark data; comparing the manual mark data and the automatic mark data so as to acquire accuracy of the mark data of the single task; calculating a variance of the accuracy of mark data of multiple tasks and preset average mark accuracy; and based on the variance, determining the number of the sample images issued to the mark account and/or determining an image type suitable for marking of the mark account. Efficiency and accuracy of mark result detection are high.

Description

Technical field [0001] This application relates to the field of computer technology, in particular to the field of Internet technology, and in particular to methods and devices for monitoring the quality of image annotations. Background technique [0002] In the process of image recognition, a large number of manually annotated images are usually required as input data to train the image recognition model. However, when the number of images that need to be manually labeled is large, some labelers may randomly label some images during the labeling process in order to complete the labeling task as soon as possible. Therefore, before training the image recognition model, you need to Mark the data for review. [0003] At present, when reviewing the labeling data of labeling personnel, manual operations are used to complete the review. Therefore, the manpower cost of the review is high, and the efficiency and accuracy of the review are low. Summary of the invention [0004] The purpose...

AI Technical Summary

Problems solved by technology

However, when the number of images that need to be manually labeled is large, some annotators may randomly label some images in the process of labeling in order to complete the labeling task as soon as possible. Therefore, before training the image recognition model, it is necessary to Label data for review

[0003] At present, when reviewing the labeling data of the labelers, manual work is used to complete the review, so the labor cost of the review is high, and the efficiency and accuracy of the review are low

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