  Human pose estimation (HPE) is a classical task in computer vision that
focuses on representing the orientation of a person by identifying the
positions of their joints. We design a lighterversion of the stacked hourglass
network with minimal loss in performance of the model. The lightweight
2-stacked hourglass has a reduced number of channels with depthwise separable
convolutions, residual connections with concatenation, and residual connections
between the necks of the hourglasses. The final model has a marginal drop in
performance with 79% reduction in the number of parameters and a similar drop
in MAdds
