In recent years, model quantization for face recognition has gained
prominence. Traditionally, compressing models involved vast datasets like the
5.8 million-image MS1M dataset as well as extensive training times, raising the
question of whether such data enormity is essential. This paper addresses this
by introducing an efficiency-driven approach, fine-tuning the model with just
up to 14,000 images, 440 times smaller than MS1M. We demonstrate that effective
quantization is achievable with a smaller dataset, presenting a new paradigm.
Moreover, we incorporate an evaluation-based metric loss and achieve an
outstanding 96.15% accuracy on the IJB-C dataset, establishing a new
state-of-the-art compressed model training for face recognition. The subsequent
analysis delves into potential applications, emphasizing the transformative
power of this approach. This paper advances model quantization by highlighting
the efficiency and optimal results with small data and training time.

该研究论文介绍了一种以高效为驱动的模型量化方法，使用比传统方法小 440 倍的数据集进行微调，通过引入评估基于指标的损失，该方法在 IJB-C 数据集上实现了 96.15% 的准确率，建立了面部识别领域新的压缩模型训练的最新技术。