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https://github.com/arrhythmia-detection/esp32tfmicro

Abstract 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.
https://github.com/arrhythmia-detection/esp32tfmicro

easy-esp32-tflm esp32 platformio-library tensorflow-lite tensorflow-lite-micro tflm-arduino

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Abstract 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.

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README

        

ESP32TFLMWrapper




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Abstracts away all the low level complexity of TensorFlow
Lite Micro and makes it really easy to deploy
TFLM (*T*ensor*F*low *L*ite *M*icro) models on supported ESP32 boards.
On top of that it adds the ability to print
per [ops](https://www.tensorflow.org/api_docs/cc/namespace/tensorflow/ops) wise
execution time (*latency*).

### Acknowledgement

This library internally utilizes
[EloquentTinyML](https://github.com/eloquentarduino/EloquentTinyML)
and [tflm_esp32](https://github.com/eloquentarduino/tflm_esp32) developed
and maintained by [eloquentarduino](https://github.com/eloquentarduino).

> [!NOTE]
> For examples on how to use this library, please check out [examples](examples) folder.

 

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