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# Welcome to the AI-on-the-edge-device
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<img src="images/icon/watermeter.svg" width="100px">
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Artificial intelligence based systems have become established in our everyday lives. Just think of speech or image recognition. Most of the systems rely on either powerful processors or a direct connection to the cloud for doing the calculations there. With the increasing power of modern processors, the AI systems are coming closer to the end user – which is usually called **edge computing**.
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Artificial intelligence-based systems have become established in our everyday lives. Just think of speech or image recognition. Most of the systems rely on either powerful processors or a direct connection to the cloud for doing the calculations there. With the increasing power of modern processors, the AI systems are coming closer to the end user, a concept known as **edge computing**.
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In this project, edge computing is demonstrated through a practical example, where an AI network is implemented on an ESP32 device, hence: **AI on the edge**.
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This project allows you to digitize your **analog** water, gas, power and other meters using cheap and easily available hardware.
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This project allows you to digitize your **analog** water, gas, power and other meters using cheap and readily available hardware.
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All you need is an [ESP32 board with a supported camera](https://jomjol.github.io/AI-on-the-edge-device-docs/Hardware-Compatibility/) and some practical skills.
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<img src="images/esp32-cam.png" width="200px">
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## Key features
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- Tensorflow Lite (TFlite) integration – including easy-to-use wrapper
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- Tensorflow Lite (TFLite) integration – including easy-to-use wrapper
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- Inline image processing (feature detection, alignment, ROI extraction)
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- **Small** and **cheap** device (3 x 4.5 x 2 cm³, < 10 EUR)
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- Integrated camera and illumination
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@@ -45,7 +45,7 @@ There is growing [documentation](https://jomjol.github.io/AI-on-the-edge-device-
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There are also articles in the German Heise magazine "make:" about the setup and technical background (behind a paywall): [DIY - Setup](https://www.heise.de/select/make/2021/2/2103513300897420296)
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A lot of people created useful Youtube videos which might help you getting started.
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A lot of people created useful YouTube videos which might help you getting started.
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Here a small selection:
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- [youtube.com/watch?v=HKBofb1cnNc](https://www.youtube.com/watch?v=HKBofb1cnNc)
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@@ -62,7 +62,7 @@ For further background information, head to [Neural Networks](https://www.heise.
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The latest available version can be found on the [Releases page](https://github.com/jomjol/AI-on-the-edge-device/releases).
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### Flashing the ESP32
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Initially you will have to flash the ESP32 via a USB connection. Later updates are possible directly over the air (OTA using WIFI).
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Initially you will have to flash the ESP32 via a USB connection. Later updates are possible directly over the air (OTA using Wi-Fi).
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There are different ways to flash your ESP32:
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- The preferred way is the [Web Installer and Console](https://jomjol.github.io/AI-on-the-edge-device/index.html) which is a browser-based tool to flash the ESP32 and extract the log over USB:
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@@ -89,7 +89,7 @@ If you would like to support the developer with a cup of coffee, you can do that
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<a href="https://www.paypal.com/donate?hosted_button_id=8TRSVYNYKDSWL"><img border="0" src="images/paypal.png" width="200px" target="_blank"></a>
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## Support
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If you have any technical problems please search the [discussions](https://github.com/jomjol/AI-on-the-edge-device/discussions). In case you found a bug or have a feature request, please open an [issue](https://github.com/jomjol/AI-on-the-edge-device/issues).
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If you have any technical problems please search the [discussions](https://github.com/jomjol/AI-on-the-edge-device/discussions). In case you find a bug or have a feature request, please open an [issue](https://github.com/jomjol/AI-on-the-edge-device/issues).
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In other cases you can contact the developer via email: <img src="https://raw.githubusercontent.com/jomjol/AI-on-the-edge-device/master/images/mail.jpg" height="25">
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