In this blog post, we will learn to create a TensorFlow Lite model using the TF Lite Model Maker Library. We will fine-tune a pre-trained image classification model on the custom dataset and further explore different types of model optimization techniques currently supported by the library and export them to the TF Lite model. Detailed performance comparison of the created TF Lite models and the converted one is done, followed by deploying the model on the web app in the end. The TensorFlow Lite Model Maker Library enables us to train a pre-trained or a custom TensorFlow Lite model on a custom dataset. Similar to the previous blog, we will be using Microsoft's Cats and Dogs Dataset.

The recent trend in the development of larger and larger Deep Learning models for a slight increase in accuracy raises the concern about their computational efficiency and wide scaled usability. We can not use such huge models on resource-constrained devices like mobiles and embedded devices. Does it mean that such devices have to sacrifice accuracy at the cost of a smaller model? Is it possible at all to deploy these models on devices such as smartphones or a Raspberry Pi or even on Microcontrollers? Optimizing the models using TensorFlow Lite is the answer to these questions.

Edge computing devices are becoming the logical destination to run deep learning models. While the public cloud is the preferred environment for training, it is the edge that runs the models for inferencing. Since most of the edge devices have constraints in the form of available CPU and GPU resources, there are purpose-built AI chips designed to accelerate the inferencing. These AI accelerators complement the CPU by speeding up the calculations involved in inferencing. They are designed to optimize the forward propagation of neural networks deployed on the edge.