  Counting the number of birds in an open sky setting has been an challenging
problem due to the large number of bird flocks and the birds can overlap.
Another difficulty is the lack of accurate training samples since the cost of
labeling images of bird flocks can be extremely high and each sample picture
can contain thousands of birds in a high resolution image. Inspired by recent
work on training with synthetic data to perform crowd counting, we design a
mechanism to generate synthetic bird dataset with precise bird count and the
corresponding density maps. We then train a Unet model on the synthetic dataset
to perform density map estimation that produces the count for each input. Our
method is able to achieve MSE of approximately 12.4 on real dataset. In order
to build a scalable system for fast bird counting under storage and
computational constraints, we use model compression techniques and efficient
model structures to increase the inference speed and save storage cost. We are
able to reduce storage cost from 55MB to less than 5MB for the model with
minimum loss of accuracy. This paper describes the pipelines of building an
efficient bird counting system.
