Abstract
Face detectors are becoming a crucial component of many applications, including surveillance, that often have to run on edge devices with limited processing power and memory. Therefore, there's a pressing demand for compact face detection models that can function efficiently across resource-constrained devices. Over recent years, network pruning techniques have attracted a lot of attention from researchers. These methods haven't been well examined in the context of face detectors, despite their expanding popularity. In this paper, we implement filter pruning on two already small and compact face detectors, named EXTD (Extremely Tiny Face Detector) and EResFD (Efficient ResNet Face Detector). The main pruning algorithm that we utilize is Filter Pruning via Geometric Median (FPGM), combined with the Soft Filter Pruning (SFP) iterative procedure. We also apply L1 Norm pruning, as a baseline to compare with the proposed approach. The experimental evaluation on the WIDER FACE dataset indicates that the proposed approach has the potential to further reduce the model size of already lightweight face detectors, with limited accuracy loss, or even with small accuracy gain for low pruning rates.
Abstract (translated)
面部识别器已成为许多应用程序的关键组件，包括监控，而这些应用程序通常需要在具有有限处理能力和内存的边缘设备上运行。因此，对于在资源受限设备上运行的紧凑型面部识别器模型，迫切需要开发出高效运作的模型。在最近几年里，网络剪枝技术已经引起了研究人员的高度关注。尽管这些方法在面部识别器方面越来越受欢迎，但在面部识别器背景下，这些方法并没有得到很好的研究。在本文中，我们在两个已经小型且紧凑的面部识别器EXTD（非常小巧的面部识别器）和EResFD（高效的ResNet面部识别器）上实现了基于几何中值滤波（FPGM）的滤波器剪枝。我们所采用的主要剪枝算法是结合Filter Pruning through Geometric Median（FPGM）和Soft Filter Pruning（SFP）迭代过程。我们还应用了L1范数剪枝作为基准，与所提出的剪枝方法进行比较。在WIDER FACE数据集上的实验评估结果表明，与所提出的剪枝方法相比，具有有限的准确度损失，甚至在高剪枝率下具有小的准确度增加，原剪枝方法具有巨大潜力。
URL
https://arxiv.org/abs/2311.16613
