DeepMark++: Real-time Clothing Detection at the Edge
Alexey Sidnev, Alexander Krapivin, Alexey Trushkov, Ekaterina, Krasikova, Maxim Kazakov, Mikhail Viryasov

TL;DR
DeepMark++ presents a fast, efficient clothing detection model suitable for real-time edge devices, achieving high accuracy and speed on mobile hardware, advancing fashion AI applications.
Contribution
The paper introduces a single-stage multi-target network with novel post-processing for real-time clothing detection and keypoint estimation on edge devices.
Findings
Achieves comparable accuracy to state-of-the-art on DeepFashion2
Runs at 17 FPS on Huawei P40 Pro
Secures second place in DeepFashion2 Landmark Estimation Challenge 2020
Abstract
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.
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Taxonomy
MethodsDeep Layer Aggregation · Batch Normalization · Convolution · Cascade Corner Pooling · Center Pooling · CenterNet
