Project leader: Azat Absadyk
Development and optimization of neural networks for accurate extraction of anthropometric data from images, in order to minimize the number of returns of clothing in online stores due to size mismatches, as well as reduce the associated environmental impacts.
A dataset was created using the new NVIDIA Omniverse technology. With this technology, you can generate synthetic data with a very realistic picture and the ability to get different scenarios, which reduces manual work. Synthetically obtained a data set of more than 14,000 images. Training on this dataset showed the applicability of the model to real photos.
To determine the appropriate model, the main architectures of convolutional neural networks were modified and tested with pre-trained semantic segmentation models as an encoder. For training, a synthetically created data set was used. Out of 16 models, 1 model showed good results and applicability to real drawings.
Absadyk A. M.T.Sc,
Web of Science ResearcherID: IXN-3525-2023
ORCID – 0009-0003-6566-8693
Scopus ID – 57480517400
Funding amount: 18 979 301,5