This workshop is designed to teach you how to use TensorFlow Lite to perform multi-label classification on embedded devices. TensorFlow Lite is a lightweight version of the popular TensorFlow library, specifically designed for deployment on resource-constrained devices such as smartphones, Raspberry Pi, and other embedded systems.
In this workshop, you will learn how to train a multi-label classification model in TensorFlow, convert the model to TensorFlow Lite, and run the model on an embedded device to make predictions.
We'll also do a proof-of-concept study, where we'll compare different compression strategies in TFlite and how it influences the overall performance of a neural network.
Before starting this workshop, you should have a basic understanding of machine learning concepts, basic knowledge of microcontrollers (Arduino, Raspberry Pi, ESPs, NodeMCU, ... etc) and basic experience with TensorFlow.
- Introduction to multi-label classification and Tensorflow.
- Introduction to basic edge computing concepts and microcontrollers.
- Preparing the dataset for multi-label classification. (MNIST Fashion)
- Training a multi-label classification model in TensorFlow.
- Converting the TensorFlow model to TensorFlow Lite.
- Comparing TF and TFlite models.
- Model deployment optimization (case of weights size compression)
This Workshop is made for the service of WTM's IOTMakers series of workshops and talks.