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Image matching and classification methods, as well as synchronous locationand mapping, are widely used on embedded and mobile devices. Their mostresource-intensive part is the detection and description of the key points ofthe images. And if the classical methods of detecting and describing key pointscan be executed in real time on mobile devices, then for modern neural networkmethods with the best quality, such use is difficult. Thus, it is important toincrease the speed of neural network models for the detection and descriptionof key points. The subject of research is distillation as one of the methodsfor reducing neural network models. The aim of thestudy is to obtain a morecompact model of detection and description of key points, as well as adescription of the procedure for obtaining this model. A method for thedistillation of neural networks for the task of detecting and describing keypoints was tested. The objective function and training parameters that providethe best results in the framework of the study are proposed. A new data set hasbeen introduced for testing key point detection methods and a new qualityindicator of the allocated key points and their corresponding local features.As a result of training in the described way, the new model, with the samenumber of parameters, showed greater accuracy in comparing key points than theoriginal model. A new model with a significantly smaller number of parametersshows the accuracy of point matching close to the accuracy of the originalmodel.

Distillation of neural network models for detection and description of key points of images