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Human pose estimation (HPE) is a classical task in computer vision thatfocuses on representing the orientation of a person by identifying thepositions of their joints. We design a lighterversion of the stacked hourglassnetwork with minimal loss in performance of the model. The lightweight2-stacked hourglass has a reduced number of channels with depthwise separableconvolutions, residual connections with concatenation, and residual connectionsbetween the necks of the hourglasses. The final model has a marginal drop inperformance with 79% reduction in the number of parameters and a similar dropin MAdds

To Perceive or Not to Perceive: Lightweight Stacked Hourglass Network