Picture training set screening method, device and equipment, and readable storage medium

Abstract

The embodiment of the invention discloses a picture training set screening method, device and equipment and a readable storage medium, and relates to the neural network training technology, the methodcomprises the steps of adopting a training set to train a target detection network and a semantic segmentation network, wherein the training set comprises a plurality of labeled pictures; predictingthe plurality of unlabeled pictures by adopting the trained target detection network and semantic segmentation network to obtain a target detection result and a semantic segmentation result, and determining at least one of position distribution, prediction precision, area distribution and size features of each target in each unlabeled picture; and screening a target picture of which the attributeinformation of each target meets the requirements of a network training task from the plurality of unlabeled pictures, labeling the target picture, and adding the labeled target picture to the training set. According to the embodiment, the training set meeting the requirements of the network training task is automatically screened out from the multiple unlabeled pictures, manual screening is not needed, and the flexibility is high.

Description

technical field [0001] Embodiments of the present invention relate to neural network training technology, and in particular, to a method, device, device, and readable storage medium for screening a picture training set. Background technique [0002] The training set is the data sample used to fit the network. When predicting the attribute information of people, vehicles, and objects in pictures through neural networks, a large number of pictures of different environments and scenes are required to form a training set to ensure the accuracy of the neural network. [0003] At present, when training the target detection network and semantic segmentation network, it is necessary to manually browse and screen different pictures, which has many obvious disadvantages: 1) high labor cost and low screening efficiency; 2) need to browse all pictures for screening, resulting in The cost of screening time is high; 3) Manual screening is highly subjective and difficult to form a unified...

AI Technical Summary

Problems solved by technology

[0003] At present, when training the target detection network and semantic segmentation network, it is necessary to manually browse and screen different pictures, which has many obvious disadvantages: 1) high labor cost and low screening efficiency; 2) need to browse all pictures for screening, resulting in The cost of screening time is high; 3) Manual screening is highly subjective and difficult to form a unified standard, resulting in poor reliability of the training set; 4) If the screening standard changes, the already screened training set needs to be readjusted one by one, resulting in poor flexibility

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