[_coveredInBees]

Well, that blog post was rather uninspiring. The winning solution basically amounted to a whole bunch of hyperparameter tweaking using tons of GPU compute on a UNet architecture that has existed for many years now. We've definitely reached a plateau of sorts when it comes to vision-based deep learning. There is definitely progress still happening, but a lot of it is very incremental and there hasn't really been any big fundamental overall performance improvements in a while. That being said, there has been a bunch of work on improving efficiency. Which is nice, but it only buys you so much when the only way to "improve" overall performance is to throw in several million more parameters with larger models to eke out another few percent in accuracy or whatever metric you are tracking.