This GitHub Organization contains the codebase that I/my team (team 27) developed for CSE713 semester final (Fall2024) project under the supervision of Mr. Annajiat Alim Rasel.
Abstracts away all the low-level complexity of TensorFlow Lite Micro and makes it really easy to deploy TFLM models on supported ESP32 boards. By default, this library prints per ops wise execution…
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This repository contains ML codebase developed during CSE713 group project
Jupyter Notebook
Forked from eloquentarduino/tflm_esp32
My fork of eloquentarduino/tflm_esp32
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Deploys a vanilla Decision Tree for Arrhythmia classification using Chapman ECG dataset on Arduino UNO board
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Deploys a vanilla non-optimized Decision Tree for Arrhythmia classification using Chapman ECG dataset on ESP32-S3 dev kit
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Deploys a simple MLP to ESP32-S3 chip to do arrhythmia classification using Chapman ECG dataset
Abstracts away all the low-level complexity of TensorFlow Lite Micro and makes it really easy to deploy TFLM models on supported ESP32 boards. By default, this library prints per ops wise execution time via Serial. This makes it really easy to collect metrics for logging.
Deploys a vanilla Decision Tree for Arrhythmia classification using Chapman ECG dataset on Arduino UNO board
Deploys an optimized Decision Tree for Arrhythmia classification using Chapman ECG dataset on Arduino UNO board
Deploys an optimized Decision Tree for Arrhythmia classification using Chapman ECG dataset on ESP32-S3 dev kit
Deploys a vanilla non-optimized Decision Tree for Arrhythmia classification using Chapman ECG dataset on ESP32-S3 dev kit
This repository contains ML codebase developed during CSE713 group project
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