论文标题
DeepMark ++：边缘的实时服装检测
DeepMark++: Real-time Clothing Detection at the Edge
论文作者
论文摘要
服装识别是时尚域中最基本的AI应用程序挑战。尽管现有解决方案提供了体面的识别精度，但它们通常很慢，需要大量的计算资源。在本文中，我们提出了一种克服这一障碍并提供快速服装检测和关键点估计的单阶段方法。我们的解决方案是基于多目标网络Centernet的，我们介绍了几种强大的后处理技术来增强性能。我们最准确的模型可实现的结果与DeepFashion2数据集上的最新解决方案相当，而我们的轻型和快速模型在Huawei P40 Pro智能手机上以17 fps的速度运行。此外，我们在2020年的DeepFashion2里程碑估计挑战中获得了第二名，测试数据集上的地图为0.582。
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.
