@wgcban
Hello sir,
thank you for your outstanding work and providing code on HyperTransformer. In order to cite your paper better, I have a few questions. Firstly, in the paper, it was mentioned that the best performance was achieved when the number of heads in the multi-head attention was 16. However, the best model provided by you in config_HSIT.json was using 8 heads, and there were errors in the RGB parameters in the same file. Can you provide the correct best model and config_HSIT.json file? It is difficult to reproduce your method without the correct files.
Secondly, for the calculation of the metrics, did you use the results generated by the code or did you re-calculate them using MATLAB?
Your response is crucially important, and I am very grateful for your work.
@wgcban
Hello sir,
thank you for your outstanding work and providing code on HyperTransformer. In order to cite your paper better, I have a few questions. Firstly, in the paper, it was mentioned that the best performance was achieved when the number of heads in the multi-head attention was 16. However, the best model provided by you in config_HSIT.json was using 8 heads, and there were errors in the RGB parameters in the same file. Can you provide the correct best model and config_HSIT.json file? It is difficult to reproduce your method without the correct files.
Secondly, for the calculation of the metrics, did you use the results generated by the code or did you re-calculate them using MATLAB?
Your response is crucially important, and I am very grateful for your work.