changes for v0.5.0 release (see patch notes!)

README.md

@@ -21,10 +21,10 @@ Goals:
 * torch- and python-idiomatic
 * hackable 
 * few external dependencies (currently only torch and torchvision)
-* ~world-record single-GPU training time (this repo holds the current world record at ~<10 seconds on an A100, down from ~18.1 seconds originally). 
+* ~world-record single-GPU training time (this repo holds the current world record at ~<7 (!!) seconds on an A100, down from ~18.1 seconds originally). 
 * <2 seconds training time in <2 years (yep!)
 
-This is a neural network implementation of a very speedily-training network that originally started as a painstaking reproduction of [David Page's original ultra-fast CIFAR-10 implementation on a single GPU](https://myrtle.ai/learn/how-to-train-your-resnet/), but written nearly from the ground-up to be extremely rapid-experimentation-friendly. Part of the benefit of this is that we now hold the world record for single GPU training speeds on CIFAR10 (under 10 seconds on an A100!!!)
+This is a neural network implementation of a very speedily-training network that originally started as a painstaking reproduction of [David Page's original ultra-fast CIFAR-10 implementation on a single GPU](https://myrtle.ai/learn/how-to-train-your-resnet/), but written nearly from the ground-up to be extremely rapid-experimentation-friendly. Part of the benefit of this is that we now hold the world record for single GPU training speeds on CIFAR10, though it will likely get to be _much_ harder for us to continue to improve our speeds significantly from here on out. 
 
 What we've added:
 * squeeze and excite layers