Open TING-PAN opened 3 years ago
I set Lmoment as 0.1 and trained 1000 epochs, but MSE was 32, which still failed to achieve the effect described in the paper
After experiments, I found that setting the batch size to 16 and using 1080ti can get good results. Thanks for author's help.
Hi @TING-PAN ,
Can you share what modifications you made to the existing code to obtain that result? As from issue #21 that I made, I couldn't even get close to the reported results using the default settings of the given code.
Eugene
Can you share what modifications you made to the existing code to obtain that result? As from issue #21 that I made, I couldn't even get close to the reported results using the default settings of the given code.
Hi : I set lr 0.001 and batch size 16, teacher_forcing_ratio = np.maximum(0, 1 - epoch * 0.003), scheduler_enc = ReduceLROnPlateau(encoder_optimizer, mode = 'min', patience = 3, factor = 0.1, verbose = True)
@TING-PAN ,
Thanks for sharing your training hyperparameters. I'll update you if I can obtain the results shown in the paper.
Hi @TING-PAN @eugenelet , I confirm the modifications explained above by @TING-PAN. In addition, I added the separate encoders and decoders version explained in the CVPR OmniCV workshop paper. I have updated the Github repo and added a pretrained model that attains MSE=24.19. Best, Vincent
@vincent-leguen Thanks for the update!
Eugene
Hello, the paper is very interesting. I tried to run the proposed code, but got MSE 39. After that, I tried to change the Lmoment to 0.1 and 0.01, but got MSE still around 40. What am I missing ?