A training method and device for training set

Abstract

Embodiments of the present invention provide a training method and device for a training set. The method includes: obtaining a training set for training a preset model; filtering out the interference sample pictures from all sample pictures to construct the first training set, and keep all the first remaining sample pictures except the interference sample pictures; filter out the first target picture from all the first remaining sample pictures to construct the second training set, and keep all the first remaining sample pictures except All second remaining sample pictures except the interference sample picture and the first target picture; acquiring the second target picture from all second remaining sample pictures to construct the third training set; The first training set, the second training set and the third training set are constructed for training. The apparatus performs the method described above. The training method and device for the training set provided by the embodiments of the present invention can improve the rationality of the training set construction, and further train the training set more reasonably.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of image processing, and in particular, to a training method and device for a training set. Background technique [0002] Capsule endoscopy has the advantages of being painless, non-invasive, and the captured images contain a large amount of information, and has a wide range of application values. [0003] The existing technology uses artificial methods to identify and classify the original pictures taken by capsule endoscopy. In order to identify the original pictures more accurately and efficiently, it is necessary to build a model, but the model usually needs to be trained before it is used. training set so that the model can perform image recognition more accurately, however, the existing training methods for the training set, due to the unreasonable construction of the training set, the accuracy of the trained model is not high. [0004] Therefore, how to avoid the above defects, ...

AI Technical Summary

Problems solved by technology

[0003] The existing technology uses artificial methods to identify and classify the original pictures taken by capsule endoscopy. In order to identify the original pictures more accurately and efficiently, it is necessary to build a model, but the model usually needs to be trained before it is used. training set so that the model can perform image recognition more accurately, however, the existing training methods for the training set, because the training set constructed is not reasonable enough, resulting in low accuracy of the trained model

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