Open ThibaultGROUEIX opened 5 years ago
It seems that you do not do data-augmentation indeed, from your calls to provider.py in train.py. Do you have insight on why ? Can you confirm that no further preprocessing than normalization is done ?
It seems that you do not do data-augmentation indeed, from your calls to provider.py in train.py. Do you have insight on why ? Can you confirm that no further preprocessing than normalization is done ?
Hi, awesome work and great repo ! I am trying to reimplement it in pytorch and get the paper's result of 83.7 mean IOU for part segmentation.
To do so, I have two questions on part segmentation : 1)- Do you perform data augmentation for part segmentation on Shapenet16 ? I couldn't find the info in the paper or the code.
If yes: could you say which kind ? If no: why do it for classification on modelnet40 and not on shapenet16 ?
2)- What is the difference in performance with and without the T-NET ?
Thanks in advance for your help, Best regards, Thibault
Hi, can you get the accuracy in paper without data augment?
Hi, awesome work and great repo ! I am trying to reimplement it in pytorch and get the paper's result of 83.7 mean IOU for part segmentation.
To do so, I have two questions on part segmentation : 1)- Do you perform data augmentation for part segmentation on Shapenet16 ? I couldn't find the info in the paper or the code.
If yes: could you say which kind ? If no: why do it for classification on modelnet40 and not on shapenet16 ?
2)- What is the difference in performance with and without the T-NET ?
Thanks in advance for your help, Best regards, Thibault