TF Lite Framework

Explore how TensorFlow Lite compresses and converts large deep learning models into a compact FlatBuffers format suited for resource-limited mobile devices. Understand model serialization, deployment methods, and hardware acceleration techniques to run efficient on-device inference without cloud dependency.

We'll cover the following...

Model serialization formats
TF Lite model development
TF Lite model deployment
Benefits of TF Lite
Conclusion

DL models trained on a huge amount of data can have millions of trained weights and a size of hundreds of megabytes. Testing even a single data point or an image can take a few seconds. Therefore, we can’t deploy large DL models to resource-constrained mobile devices. TF provides us with a lightweight framework, TF Lite, that can compress, optimize, and deploy DL models to mobile devices.

Let’s explore various model serialization formats and understand model development and deployment using TF Lite.

Model serialization formats

Model serialization is the process of converting an ML model into a format that we can store in a file or transmit over a network. Model serialization permits us to save and share trained DL models. Protocol buffers, GraphDef, and FlatBuffers are all serialization formats to represent data in a compact, efficient, and platform-independent way.

1.Getting Started with Python

2.Machine Learning (ML) and Deep Learning (DL)

Project

3.TensorFlow (TF)

Project

4.Dataset Processing Using TensorFlow

5.Keras: High-Level TF API

Project

6.Quick Start with Android Apps

7.TensorFlow (TF) Lite

8.Image Classification Apps Using TF Lite

9.Object Detection Apps Using TF Lite

10.Appendix

Mini Project

TF Lite Framework

Model serialization formats

Protocol buffers