So far, we have learned how to develop deep learning models by preprocessing data, training models, and generating inferences using a Python PC environment.
In this chapter, we will learn how to take the generated model and deploy it on edge devices and production systems. This will result in a complete end-to-end TensorFlow object detection model implementation. A number of edge devices and their nominal performance and acceleration techniques will be discussed in this chapter.
In particular, TensorFlow models have been developed, converted, and optimized using the TensorFlow Lite and Intel Open Visual Inference and Neural Network Optimization (OpenVINO) architectures and deployed to Raspberry Pi, Android, and iPhone. Although this chapter focuses mainly on object detection on Raspberry Pi, Android, and iPhone, the approach...
