DeepMark: One-Shot Clothing Detection
This work addresses efficient clothing detection for applications on low-power devices, representing an incremental improvement over existing methods.
The paper tackled fast clothing detection by proposing DeepMark, a one-shot approach based on CenterNet, achieving state-of-the-art accuracies of 0.723 mAP for bounding box detection and 0.532 mAP for landmark detection on the DeepFashion2 Challenge dataset.
The one-shot approach, DeepMark, for fast clothing detection as a modification of a multi-target network, CenterNet, is proposed in the paper. The state-of-the-art accuracy of 0.723 mAP for bounding box detection task and 0.532 mAP for landmark detection task on the DeepFashion2 Challenge dataset were achieved. The proposed architecture can be used effectively on the low-power devices.