Alexander Krapivin

DeepMark++: CenterNet-based Clothing Detection

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The single-stage approach for fast clothing detection as a modification of a multi-target network, CenterNet, is proposed in this paper. We introduce several powerful post-processing techniques that may be applied to increase the quality of keypoint localization tasks. The semantic keypoint grouping approach and post-processing techniques make it possible to achieve a state-of-the-art accuracy of 0.737 mAP for the bounding box detection task and 0.591 mAP for the landmark detection task on the DeepFashion2 validation dataset. We have also achieved the second place in the DeepFashion2 Challenge 2020 with 0.582 mAP on the test dataset. The proposed approach can also be used on low-power devices with relatively high accuracy without requiring any post-processing techniques.

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