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80 lines
3.2 KiB
Markdown
80 lines
3.2 KiB
Markdown
# TensorFlow Lite Micro Library for Arduino-Espressif32
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This repository has the code (including examples) needed to use Tensorflow Lite Micro on an Arduino.
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## Table of contents
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<!--ts-->
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* [Table of contents](#table-of-contents)
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* [Build Status](#build-status)
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* [How to Install](#how-to-install)
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* [GitHub](#github)
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* [Checking your Installation](#checking-your-installation)
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* [Compatibility](#compatibility)
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* [License](#license)
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* [Contributing](#contributing)
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<!--te-->
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## Build Status
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Build Type | Status |
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--------------- | ------------- |
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Arduino CLI on Linux | [](https://github.com/tensorflow/tflite-micro-arduino-examples/actions/workflows/ci.yml)
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Sync from tflite-micro | [](https://github.com/tensorflow/tflite-micro-arduino-examples/actions/workflows/sync.yml)
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## How to Install
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### GitHub
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The officially supported TensorFlow Lite Micro library for Arduino resides
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in the [tflite-micro-arduino-examples](https://github.com/tensorflow/tflite-micro-arduino-examples)
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GitHub repository.
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To install the in-development version of this library, you can use the
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latest version directly from the GitHub repository. This requires you clone the
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repo into the folder that holds libraries for the Arduino IDE. The location for
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this folder varies by operating system, but typically it's in
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`~/Arduino/libraries` on Linux, `~/Documents/Arduino/libraries/` on MacOS, and
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`My Documents\Arduino\Libraries` on Windows.
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Once you're in that folder in the terminal, you can then grab the code using the
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git command line tool:
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```
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git clone https://github.com/tensorflow/tflite-micro-arduino-examples Arduino_TensorFlowLite
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```
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To update your clone of the repository to the latest code, use the following terminal commands:
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```
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cd Arduino_TensorFlowLite
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git pull
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```
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### Checking your Installation
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Once the library has been installed, you should then start the Arduino IDE.
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You will now see an `Arduino_TensorFlowLite`
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entry in the `File -> Examples` menu of the Arduino IDE. This submenu contains a list
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of sample projects you can try out.
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## Compatibility
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This library is designed for the `Arduino Nano 33 BLE Sense` board. The framework
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code for running machine learning models should be compatible with most Arm Cortex
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M-based boards, such as the `Raspberry Pi Pico`, but the code to access peripherals
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like microphones, cameras, and accelerometers is specific to the `Nano 33 BLE Sense`.
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## License
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This code is made available under the Apache 2 license.
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## Contributing
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Forks of this library are welcome and encouraged. If you have bug reports or
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fixes to contribute, the source of this code is at [https:://github.com/tensorflow/tflite-micro](github.com/tensorflow/tflite-micro)
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and all issues and pull requests should be directed there.
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The code here is created through an automatic project generation process
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and may differ from
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that source of truth, since it's cross-platform and needs to be modified to
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work within the Arduino IDE. |