  Clothing recognition is the most fundamental AI application challenge within
the fashion domain. While existing solutions offer decent recognition accuracy,
they are generally slow and require significant computational resources. In
this paper we propose a single-stage approach to overcome this obstacle and
deliver rapid clothing detection and keypoint estimation. Our solution is based
on a multi-target network CenterNet, and we introduce several powerful
post-processing techniques to enhance performance. Our most accurate model
achieves results comparable to state-of-the-art solutions on the DeepFashion2
dataset, and our light and fast model runs at 17 FPS on the Huawei P40 Pro
smartphone. In addition, we achieved second place in the DeepFashion2 Landmark
Estimation Challenge 2020 with 0.582 mAP on the test dataset.
