The results in this paper are quite astounding. Specifically the improvement in efficiency and accuracy of their EfficientNet architecture over state-of-the-art

The results in this paper are quite astounding. Specifically the improvement in efficiency and accuracy of their EfficientNet architecture over state-of-the-art feature extraction backbones is simply amazing (see Fig 1. from the paper - https://raw.githubusercontent.com/tensorflow/tpu/master/models/official/efficientnet/g3doc/params.png https://raw.githubusercontent.com/tensorflow/tpu/master/mode...). AFAICT, this is a huge leap in improvement, and what's fascinating is how fundamentally simple the entire premise of this paper and the type of experiments performed was. Pretty much everyone will want to switch their feature extractors to some flavor of a pre-trained EfficientNet for any image classification / object detection type application going forward. I'm also excited to see the improvements in speed and accuracy that this can enable for mobile/embedded systems.