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docs/Choosing-the-Model.md
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!!! Warning
This page overlaps [Neural Network Types](../Neural-Network-Types). They should be merged to one page!
In the [Graphical Configuration Page](Graphical-configuration), you can choose different models depending on your needs.
In the [Graphical Configuration Page](../Graphical-configuration), you can choose different models depending on your needs.
This wiki page tries to help you on which model to select.
For more technical/deeper explanations have a look on [Neural-Network-Types](https://github.com/jomjol/AI-on-the-edge-device/wiki/Neural-Network-Types).
This page tries to help you on which model to select.
For more technical/deeper explanations have a look on [Neural-Network-Types](../Neural-Network-Types).
## Digit Models

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docs/FAQs.md
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* Wrong configuration, missing configuration files
* Unstable hardware - see [[Hardware Compatibility]]
There is a dedicated Wiki page about this: [Frequent Reboots](https://jomjol.github.io/AI-on-the-edge-device-docs/Frequent-Reboots/).
There is a dedicated page about this: [Frequent Reboots](../Frequent-Reboots/).
### How accurate are the detections?
@@ -21,14 +21,14 @@ It is hard to give a specific accuracy number. It depends on many factors, e.g.
* Are you trying to read digits, an analog dial, or both?
* etc.
Anecdotally, the authors of this wiki have great success with the meter. While the AI algorithm itself is not perfect and sometimes returns `NaN` or incorrect values, other post-processing / prevalue / sanity checks help ensure such invalid values are filtered out. With the correct settings, one author has been running this device for 1 month without any incorrect values reported.
Anecdotally, the authors of this page have great success with the meter. While the AI algorithm itself is not perfect and sometimes returns `NaN` or incorrect values, other post-processing / prevalue / sanity checks help ensure such invalid values are filtered out. With the correct settings, one author has been running this device for 1 month without any incorrect values reported.
See the FAQs below for more details and configuration hints.
## My numbers are not corrected detected. What can I do?
* There is a dedicated Wiki page about the correct setting [ROI Configuration](https://jomjol.github.io/AI-on-the-edge-device-docs/ROI-Configuration/).
* There is a dedicated page about the correct setting [ROI Configuration](../ROI-Configuration/).
* This page also includes the instructions for gathering new images for the training.
## How can I ensure invalid numbers are never reported?

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docs/Installation.md
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Grab the firmware from the
- [Releases page](https://github.com/jomjol/AI-on-the-edge-device/releases) (Stable, tested versions), or the
- [Automatically build development branch](https://github.com/jomjol/AI-on-the-edge-device/actions?query=branch%3Arolling) (experimental, untested versions). Please have a look on https://github.com/jomjol/AI-on-the-edge-device/wiki/Install-a-rolling-%28unstable%29-release first!
- [Automatically build development branch](https://github.com/jomjol/AI-on-the-edge-device/actions?query=branch%3Arolling) (experimental, untested versions). Please have a look on [Living on the Edge](../rolling-installation)] first!
You need:

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docs/Integration-Home-Assistant.md
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## Using REST
When using REST, Home Assistant has to periodically call an URL on the ESP32 which in return provides the requested data.
See [REST API](https://github.com/jomjol/AI-on-the-edge-device/wiki/REST-API) for a list of available URLs.
See [REST API](../REST-API) for a list of available URLs.
The most practical one is the `json` entrypoint which provides the most relevant data JSON formated:
`http://<IP>/json`

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docs/ROI-Configuration.md
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@@ -8,7 +8,7 @@ The most critical settings for accurate detection are:
1. Correct setting of the **R**egions **O**f **I**nterest (ROIs) for detection of the image.
> This must be done manually for each meter!
2. Number type is part of the training set.
> Have a look on the [Digital Counters](https://jomjol.github.io/neural-network-digital-counter-readout/) resp. [Analog Needles](https://jomjol.github.io/neural-network-analog-needle-readout) to check if your types are contained. If your number types are **not** contained, you should take the effort to record them so we can add them to the training data. See: [Learn models with your own images](https://github.com/jomjol/AI-on-the-edge-device/wiki/Learn-models-with-your-own-images) on how to create new input.
> Have a look on the [Digital Counters](https://jomjol.github.io/neural-network-digital-counter-readout/) resp. [Analog Needles](https://jomjol.github.io/neural-network-analog-needle-readout) to check if your types are contained. If your number types are **not** contained, you should take the effort to record them so we can add them to the training data. See: [Learn models with your own images](../Learn-models-with-your-own-images) on how to create new input.
_____
@@ -47,7 +47,7 @@ dig-class11 - Models recognize the **complete digit only**. Here it is not relev
For this model, there should be a border of 20% of the image size around the number itself. This border is shown in the ROI setup image by the inner thinner rectangle. This rectangle should fit perfectly around the number when the number has not started to rotate to the next position:
<img width="300px" src=https://github.com/jomjol/AI-on-the-edge-device/wiki/images/ROI_drawing.jpg>
<img width="300px" src=../img/ROI_drawing.jpg>
| | Example 1 | Example 2 |
| ------------ | --------------------------------- | --------------------------------- |
@@ -57,7 +57,7 @@ For this model, there should be a border of 20% of the image size around the num
If you have perfect alignment you and are not getting satisfying results, most probably your numbers are not part of the training data yet. Read on [Learn models with your own images](https://github.com/jomjol/AI-on-the-edge-device/wiki/Learn-models-with-your-own-images) how to add your meter's type of numbers to the training set.
If you have perfect alignment you and are not getting satisfying results, most probably your numbers are not part of the training data yet. Read on [Learn models with your own images](../Learn-models-with-your-own-images) how to add your meter's type of numbers to the training set.
#### dig-class100 / dig-cont Configuration