Due to the high dimensionality of object states, a garment flattening pipeline requires recognising the configurations of garments for a robot to produce/select manipulation plans to flatten garments. In this paper, we propose a data-centric approach to identify known configurations of garments based on a known configuration network (KCNet) trained on depth images that capture the known configurations of garments and prior knowledge of garment shapes. In this paper, we propose a data-centric approach to identify the known configurations of garments based on a known configuration network (KCNet) trained on the depth images that capture the known configurations of garments and prior knowledge of garment shapes. The known configurations of garments are the configurations of garments when a robot hangs garments in the middle of the air. We found that it is possible to achieve 92\% accuracy if we let the robot recognise the common hanging configurations (the known configurations) of garments. We also demonstrate an effective robot garment flattening pipeline with our proposed approach on a dual-arm Baxter robot. The robot achieved an average operating time of 221.6 seconds and successfully manipulated garments of five different shapes.