If your device has new, different digits and the existing models don't recognize them well, you can collect your own images and train the model.

But before you do this, please check if your type really is not contained yet in the training data, see digits resp. pointers for an overview of images used for the training

The neural network is trained on base of a set of images, that have been collected over time. If your digits are included or at least very similar to included images, the chance is very high that the neural network is working fine for you as well.

The neural network configuration is stored in the TensorFlow Lite format as `filename.tfl` or `filename.tflite` in the `/config` directory. It can be updated by uploading the new file and activating it on the configuration page or in the config file `/config/config.ini`.

In order to incorporate new digits a training set of images is required. The training images needs to be collected in the final setup with the help of the `Digits` or `Analog` log settings (not to be confused with the `Data` or `Debug` log). Enable the logging of the images on the configuration page or in the config file `/config/config.ini`:

Now wait, until you have an image of each digit of every type on the SD card. Ideally remove the SD card from the camera and search for two to three images of each digit (not more! :-)). The format can be jpg.

## Collecting images for dig-class100/dig-cont/ana-class100

Collectmeterdigits and collectmeteranalog helps you to collect the images easily. Read the project readme for detailed instructions.

## Train the model

For training the model you will need a python and Jupyter installation.

All current labeled images you can find under ziffer_sortiert_raw

### dig-class11 models (digits)

Fork and checkout neural-network-digital-counter-readout.

Install all requirements for running the notebooks.

    pip install -r requirements.txt

Put your labeled images into `/ziffer_sortiert_raw` folder and run

1. Image_Preparation.ipynb
2. Train_CNN_Digital-Readout-Small-v2.ipynb

It creates a dig-class11_xxxx_s2.tflite model, you can upload to the `config` folder on your device and test it.

### dig-class100 / dig-cont models (digits)

Fork and checkout neural-network-analog-needle-readout.

All labeled images you can find under Images

Install all requirements for running the notebooks.

    pip install -r requirements.txt

Put your labeled images into `images/collected/<typeofdevice>/<your_short>/`

Run dig-class100-s2.ipynb. The model to upload to your device you can find under '/output'.

### ana-class100/ana-cont models (analog pointers)

Fork and checkout neural-network-analog-needle-readout.

All labeled images you can find under data_raw_all

Install all requirements for running the notebooks.

    pip install -r requirements.txt

Put your labeled images into `images/collected/<typeofdevice>/<your_short>/`

After every adding of images you need to run Image_Preparation.ipynb before you train the models.

Run Train_CNN_Analog-Readout_100-Small1_Dropout.ipynb and/or Train_CNN_Analog-Readout_Version-Small2.ipynb. The model to upload to your device you can find in the project folder.

## Share your images

If the results are good you can share the images as pull-request. Please images only!

If you not able to create a pull request or don't know what it is, open an issue and put the zipped images in it.

### Images can be rejected if

- As same as dig-class11 collected, more than 1000 images of your device are really to much.
- images are not good configured (ROIs) will be rejected. It reduces the accuracy of the networks.
- Images with too little focus will be rejected.
- Images with too much blur are rejected.

Our models are to small to recognize everything in any quality. So we use only images of medium or good quality.

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