Data labeling method and device of target element, terminal equipment and computer readable storage medium

Abstract

The invention relates to the technical field of computers, in particular to a data annotation method and device of a target element, terminal equipment and a computer readable storage medium, and the method comprises the following steps: determining a to-be-annotated target element based on a game; the method comprises the following steps: determining to-be-labeled data after screen recording in a game process, labeling a target element in the to-be-labeled data to form labeled data, labeling at least four point locations when labeling the target element, forming a rectangular frame when the four point locations are connected end to end, and enabling the target element to be in the rectangular frame; preprocessing the annotation data through the script to form training data, wherein the training data at least comprises a plurality of to-be-annotated pictures; establishing a training model according to the training data, and training the training model through a script; and training a new model according to the training data, the script and the trained training model. The invention provides a method capable of reducing manual labeling of target elements.

Description

technical field [0001] The present invention relates to the field of computer technology, in particular to a data labeling method, device, terminal equipment and computer-readable storage medium for target elements. Background technique [0002] At present, the labeling of target elements can be divided into two categories: manual labeling and auxiliary labeling. In the labeling process, for some target elements that are more complicated to label, it is not efficient to use purely manual labeling. For example, in the segmentation task, the target elements are manually extracted from the picture. The operator needs to switch between the keyboard and the mouse repeatedly, which is not easy to operate. In addition, there are video tracking tasks, which require the labeler to repeatedly flip through the pictures for labeling, which is also very energy-consuming. [0003] Although manual labeling can accurately label images, the efficiency is extremely low. With the rapid increa...

AI Technical Summary

Problems solved by technology

In the labeling process, for some target elements that are more complicated to label, it is not efficient to use purely manual labeling. For example, in the segmentation task, the target elements are manually extracted from the picture. The operator needs to repeatedly switch between the keyboard and the mouse, which is not easy to operate. In addition, there are video tracking tasks, which require the labeler to repeatedly flip through the pictures for labeling, which is also very energy-consuming

[0003] Although manual labeling can accurately label images, the efficiency is extremely low. With the rapid increase in the number of samples, manual labeling has been difficult to meet the requirements. Therefore, for such application scenarios, computer automatic labeling models can be used instead of labelers for labeling

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