Mohamed-Dhouioui/TWESD

The 10Th Tunisian Workshop on Embedded Systems Design (TWESD 2023) # TWESD Workshop - TensorFlow Lite Model Optimization for On-Device Machine Learning This repository contains two folders: 1. **CNN Model with TFLite Conversion** - This folder contains a Jupyter Notebook (`TensorFlow_Lite_Model_Optimization_for_On_Device_Machine_Learning.ipynb`) for creating a Convolutional Neural Network (CNN) model. - The notebook demonstrates how to convert the trained model using TFLite with various quantization techniques. - The trained model files with the `.tflite` extension are included in this folder. 2. **EfficientNet Model with Transfer Learning and TFLite Export** - This folder includes a Jupyter Notebook (`TensorFlow_Lite_Model_Optimization_with_transfer_learning.ipynb`) for utilizing the EfficientNet model with transfer learning. - The notebook showcases the process of creating a model using the TFLite Creator and exporting it to different quantization techniques with the `.tflite` extension. - The exported model files are included in this folder. Feel free to explore the folders and notebooks for detailed explanations, code samples, and instructions on using and converting deep learning models. ## Prerequisites Make sure you have the following dependencies installed: - Python 3.x - TensorFlow - TensorFlow Lite - EfficientNet ## Usage 1. Clone the repository to your local machine: ```shell git clone github.com ``` You can also download the repository as a zip file and extract it. As for the presentation slides, you can find them in the following link: my.visme.co