On the Applicability of Synthetic Data for Re-Identification in Warehousing Logistics

The herein included files are part of our paper proposal "On the Applicability of Synthetic Data for Re-Identification in Warehousing Logistics" for the International Conference on Computational Logistics (ICCL) 2022. The rest of the code needed to reproduce our results can be found on our github.

The data contains the following:

• The weights of our trained CycleGAN model
• The perspective classifier model
• The input and output data of our CycleGAN model (filtered and unfiltered)

This work is part of the project "Silicon Economy Logistics Ecosystem" which is funded by the German Federal Ministry of Transport and Digital Infrastructure.