First, congratulations! It’s awesome to see fastai used for such an important project and in this environment!
In regards to where to improve, new SOTA optimizers and activation functions have come out that you could certainly try. This includes the Mish activation function, and the ranger optimizer (this is then teamed with fit_flat_cos rather than fit_one_cycle). I made a nice function here that can replace all the models activation functions in one line of code to simplify it if you want to try the Mish route.
In regards to Mish and ranger, I notice you guys are using fastai v1. I have some example notebooks using them here, however if you and your team wishes to come to fastai v2, I have notebooks for that as well here. Otherwise you could also of course increase the model depth to a resnet50.
I would not try to mess around with the xresnet series, as fastai’s unet doesn’t play well with them. (until someone decides to make it work 
)
Finally I’d also perhaps try doing progressive resizing and presizing, as this can boost model performance. They were discussed in these two chapters of fastbook: presizing progressive resizing. Again well done!
PS: Would also recommend putting the version of fastai used in that notebook, as currently pip install fastai will install the new version which will break all your code!