The algorithm is state of the art. There are several implementations of the algorithm, the original is with the mxnet framework, and my implementation with tensorflow framework has a slightly lower accuracy.
For model file size I have a few ideas of things that could be tried easily.
For a faster model, this model is based on resnet50 architecture. The original authors also published a lighter mobileNet architecture. I havent converted it to tensorflow but I could try to take a look
Generally it means something roughly like "the best known approach for this specific problem". Often it means "the best known approach for this specific dataset" (eg "SOTA on ImageNet").
> why is lower accuracy acceptable?
Lower accuracy is worse, but these numbers look close enough that it's probably acceptable for most people.
There are plenty of environments where TF is preferable to MXNet (eg, you have TPUs/want to use TFLite on mobile/want to slice the model weights up and use it for your own custom TF model).
It's probably lower accuracy because it wasn't trained as long. Those extra couple of points could take days (or more) of training.