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https://github.com/vietanhdev/tflite-micro-esp-examples


https://github.com/vietanhdev/tflite-micro-esp-examples

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# TensorFlow Lite Micro Examples for Espressif Chipsets

- This repository has the examples needed to use Tensorflow Lite Micro on Espressif Chipsets (e.g., ESP32) using ESP-IDF platform.
- This repo at it's core uses sources from tflite-micro. This repo can be found [here.](https://github.com/tensorflow/tflite-micro)

## Build Status

| Build Type | Status |
| ----------- | --------- |
| Build (Linux) | ![CI](https://github.com/espressif/tflite-micro-esp-examples/actions/workflows/build.yml/badge.svg)

## How to Install

### Install the ESP IDF

Follow the instructions of the
[ESP-IDF get started guide](https://docs.espressif.com/projects/esp-idf/en/latest/get-started/index.html)
to setup the toolchain and the ESP-IDF itself.

The next steps assume that the
[IDF environment variables are set](https://docs.espressif.com/projects/esp-idf/en/latest/get-started/index.html#step-4-set-up-the-environment-variables) :
* The `IDF_PATH` environment variable is set. * `idf.py` and Xtensa-esp32 tools
(e.g., `xtensa-esp32-elf-gcc`) are in `$PATH`.

## Build the example

Go to example directory (`examples/`) and build the example.

To build this, run:

```
idf.py build
```

### Load and run the example

To flash (replace `/dev/ttyUSB0` with the device serial port):
```
idf.py --port /dev/ttyUSB0 flash
```

Monitor the serial output:
```
idf.py --port /dev/ttyUSB0 monitor
```

Use `Ctrl+]` to exit.

The previous two commands can be combined:
```
idf.py --port /dev/ttyUSB0 flash monitor
```

## Sync to latest sources

- `components/tflite-lib` directory contains the tflite micro sources.
- If you need the latest head, just run `sync_from_tflite_micro.sh`.

## Contributing
- If you find an issue in these examples, or wish to submit an enhancement request, please use the Issues section on Github.
- For ESP-IDF related issues please use [esp-idf](https://github.com/espressif/esp-idf) repo.
- For TensorFlow related information use [tflite-micro](https://github.com/tensorflow/tflite-micro) repo.

## License

These examples are covered under Apache2 License.

TensorFlow library code and third_party code contains their own license specified under respective [repos](https://github.com/tensorflow/tflite-micro).