Question about fine-tuning on Middlebury2014

[kongdebug]

Hi @lahavlipson,
Thank you for your great work!

I am now using the weights trained on the scenefrlow dataset to fine-tune on the middlebury dataset. After I finish the fine-tuning, the results on the D1 of the full middlebury dataset are even worse than before. Is this normal?

The raftstereo-sceneflow.pth result is consistent with Table 1 of the paper:

However, the result after fine tuning on the Middlebury2014 dataset are relatively poor：

[lahavlipson]

I've found that the performance is better and more stable if the learning rate is small, e.g. --lr 0.00002, similar to what we use for KITTI; I've updated the command in the README.

[kongdebug]

I've found that the performance is better and more stable if the learning rate is small, e.g. --lr 0.00002, similar to what we use for KITTI; I've updated the command in the README.

Thank you for your reply and look forward to the updated README.

[kongdebug]

I've found that the performance is better and more stable if the learning rate is small, e.g. --lr 0.00002, similar to what we use for KITTI; I've updated the command in the README.

In addition, how much learning rate do you use to fine-tune the KITTI 2015 dataset?
In section 4.2 of the paper, it is mentioned that the minimum learning rate used for fine-tuning the KITTI 2015 dataset is 1e-5. What is the maximum learning rate? I hope you can tell me, thank you!

[lahavlipson]

On KITTI, we use --lr 0.00001

[kongdebug]

On KITTI, we use --lr 0.00001

Thank you. I used --lr 0.0002 and submitted the results to KITTI website for testing. The metrics of D1-all are consistent with those of RAFT-Stereo on the list. However, fine-tuning on the middlebury dataset with --lr 0.00002 did not get the same precision as the Middlebury.pth weights you supplied.