Abstract
Face detection is a computer vision application that increasingly demands lightweight models to facilitate deployment on devices with limited computational resources. Neural network pruning is a promising technique that can effectively reduce network size without significantly affecting performance. In this work, we propose a novel face detection pruning pipeline that leverages Filter Pruning via Geometric Median (FPGM) pruning, Soft Filter Pruning (SFP) and Bayesian optimization in order to achieve a superior trade-off between size and performance compared to existing approaches. FPGM pruning is a structured pruning technique that allows pruning the least significant filters in each layer, while SFP iteratively prunes the filters and allows them to be updated in any subsequent training step. Bayesian optimization is employed in order to optimize the pruning rates of each layer, rather than relying on engineering expertise to determine the optimal pruning rates for each layer. In our experiments across all three subsets of the WIDER FACE dataset, our proposed approach B-FPGM consistently outperforms existing ones in balancing model size and performance. All our experiments were applied to EResFD, the currently smallest (in number of parameters) well-performing face detector of the literature; a small ablation study with a second small face detector, EXTD, is also reported. The source code and trained pruned face detection models can be found at: this https URL.
Abstract (translated)
面部检测是一种计算机视觉应用，越来越需要轻量级模型以在计算资源有限的设备上进行部署。神经网络剪枝是一种有前景的技术，可以通过有效减少网络大小而不显著影响性能来实现这一目标。在这项工作中，我们提出了一种新颖的面部检测剪枝流水线，该流水线利用几何中位数滤波器剪枝（FPGM）和软滤波器剪枝（SFP），并结合贝叶斯优化，以实现相对于现有方法更优的大小与性能之间的权衡。FPGM剪枝是一种结构化剪枝技术，允许在每层中修剪最不重要的过滤器，而SFP则迭代地修剪这些过滤器，并使它们可以在后续训练步骤中被更新。贝叶斯优化用于优化每一层的最佳剪枝率，而不是依赖工程专业知识来确定最佳的层级剪枝率。 在WIDER FACE数据集的三个子集中进行的所有实验表明，在平衡模型大小和性能方面，我们的方法B-FPGM始终优于现有的方法。所有实验均应用于EResFD，这是目前文献中参数最少且表现良好的面部检测器；此外还报告了一个小型消融研究结果，使用了另一款较小的面部检测器EXTD。源代码及训练好的剪枝后面部检测模型可在以下链接找到：[此URL]（请将此处的“this https URL”替换为实际提供的网址）。
URL
https://arxiv.org/abs/2501.16917
