How many iterations does FlownetC need?

[dvorak0]

I have trained 130400 iterations with batch_size 8 on flying chairs dataset. The predicted flows on test set are still not good. Should I continue or switched to FlownetCS?

[simonmeister]

On which test set are you evaluating? Can you share the EPE you got (and maybe the flow image)? Which learning rate did you use? For the chairs schedule in the config template, you can train for at least 500K iterations for best results.

[dvorak0]

I use flying chairs as training set as well as test set. I got EPE 4.55 after 200k iterations. I start with lr 1e-4 and half it after the first 100k iterations.

About the detailed loss weight:

pyramid_loss = True
mask_occlusion = fb
occ_weight = 12.4
border_mask = True
fb_weight = 0.2
photo_weight = 1.0
ternary_weight = 1.0
smooth_2nd_weight = 3.0

Here are some examples:

[simonmeister]

Do you plan to train on a different dataset using chairs as a pretraining or will you train on flying chairs only? If you only train on this dataset, i would suggest training again, this time using ternary_weight = 1.0 and commenting out photo_weight for better results. As we did not do a parameter search for optimal chairs performance, the optimal smoothness weight might also be different. But disabling photo_weight alone may improve results a lot.

[dvorak0]

Thanks for your reply.

I do use chairs as the pretraining. I am confused about your suggestion. Do you mean the photo loss is not preferred by your network? I thought it is a good guide before, this why I uncomment it in the config file.

I will try following your suggestions.

[simonmeister]

Well, the ternary census term is generally more robust. photo_weigth enables brightness constancy.

[dvorak0]

I do as you suggested. I can see a clear loss jumping during the first 100k iterations. I will keep training. Thanks a lot.