B-FPGM: Lightweight Face Detection via Bayesian-Optimized Soft FPGM Pruning

TL;DR

This paper introduces B-FPGM, a novel lightweight face detection model that uses Bayesian-optimized soft filter pruning to effectively reduce model size while maintaining high detection performance.

Contribution

The paper proposes a new pruning pipeline combining FPGM, SFP, and Bayesian optimization to automatically determine optimal pruning rates for face detection models.

Findings

Outperforms existing methods on WIDER FACE dataset

Achieves better size-performance trade-off in face detection models

Demonstrates effectiveness on the smallest well-performing face detector

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: https://github.com/IDTITI/B-FPGM.