Adaptation of MobileNetV2 for Face Detection on Ultra-Low Power Platform
This work addresses the problem of efficient face detection for ultra-low power embedded systems, but it is incremental as it builds on existing MobileNetV2 architecture.
The paper tackled the challenge of deploying deep neural networks on edge hardware by adapting MobileNetV2's topology and applying post-training quantization for face detection, resulting in a model deployed on an embedded platform with specific performance metrics discussed.
Designing Deep Neural Networks (DNNs) running on edge hardware remains a challenge. Standard designs have been adopted by the community to facilitate the deployment of Neural Network models. However, not much emphasis is put on adapting the network topology to fit hardware constraints. In this paper, we adapt one of the most widely used architectures for mobile hardware platforms, MobileNetV2, and study the impact of changing its topology and applying post-training quantization. We discuss the impact of the adaptations and the deployment of the model on an embedded hardware platform for face detection.