  Judging by popular and generic computer vision challenges, such as the
ImageNet or PASCAL VOC, neural networks have proven to be exceptionally
accurate in recognition tasks. However, state-of-the-art accuracy often comes
at a high computational price, requiring equally state-of-the-art and high-end
hardware acceleration to achieve anything near real-time performance. At the
same time, use cases such as smart cities or autonomous vehicles require an
automated analysis of images from fixed cameras in real-time. Due to the huge
and constant amount of network bandwidth these streams would generate, we
cannot rely on offloading compute to the omnipresent and omnipotent cloud.
Therefore, a distributed Edge Cloud must be in charge to process images
locally. However, the Edge Cloud is, by nature, resource-constrained, which
puts a limit on the computational complexity of the models executed in the
edge. Nonetheless, there is a need for a meeting point between the Edge Cloud
and accurate real-time video analytics. In this paper, we propose a method for
improving accuracy of edge models without any extra compute cost by means of
automatic model specialization. First, we show how the sole assumption of
static cameras allows us to make a series of considerations that greatly
simplify the scope of the problem. Then, we present Edge AutoTuner, a framework
that implements and brings these considerations together to automate the
end-to-end fine-tuning of models. Finally, we show that complex neural networks
- able to generalize better - can be effectively used as teachers to annotate
datasets for the fine-tuning of lightweight neural networks and tailor them to
the specific edge context, which boosts accuracy at constant computational
cost, and do so without any human interaction. Results show that our method can
automatically improve accuracy of pre-trained models by an average of 21%.
