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Add Makefile and Markdown linter (#73)

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* Add Makefile

* Update .gitignore to exclude venv

* Add requirements.txt for venv

* Add pymarkdown lint Github action

* Update .md from lint tool

* Update README with make commands

* Add linter config
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Adriaan Van Niekerk
2025-10-31 01:06:36 +02:00
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docs/Learn-models-with-your-own-images.md
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# Learn a model with your own images
Once you have collected and selected your own images (see [Collect images to improve the models](collect-new-images.md)), you can train your very own model with them.
**This is an optional step and only suggested for advances users!**
@@ -8,6 +9,7 @@ For training the model you will need a python and Jupyter installation.
All current labeled images you can find under [ziffer_sortiert_raw](https://github.com/jomjol/neural-network-digital-counter-readout/tree/master/ziffer_sortiert_raw)
### dig-class11 models (digits)
Fork and checkout [neural-network-digital-counter-readout](https://github.com/jomjol/neural-network-digital-counter-readout).
Install all requirements for running the notebooks.
@@ -24,6 +26,7 @@ Put your labeled images into `/ziffer_sortiert_raw` folder and run
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-digital-counter-readout](https://github.com/jomjol/neural-network-digital-counter-readout).
All labeled images you can find under [Images](https://github.com/haverland/Tenth-of-step-of-a-meter-digit/tree/master/images)
@@ -39,6 +42,7 @@ Put your labeled images into `images/collected/<typeofdevice>/<your_short>/`
Run [dig-class100-s2.ipynb](https://github.com/haverland/Tenth-of-step-of-a-meter-digit/blob/master/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](https://github.com/jomjol/neural-network-analog-needle-readout).
All labeled images you can find under [data_raw_all](https://github.com/jomjol/neural-network-analog-needle-readout/tree/main/data_raw_all)
@@ -56,6 +60,7 @@ After every adding of images you need to run [Image_Preparation.ipynb](https://g
Run [Train_CNN_Analog-Readout_100-Small1_Dropout.ipynb](https://github.com/jomjol/neural-network-analog-needle-readout/blob/main/Train_CNN_Analog-Readout_100-Small1_Dropout.ipynb) and/or [Train_CNN_Analog-Readout_Version-Small2.ipynb](https://github.com/jomjol/neural-network-analog-needle-readout/blob/main/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!
See [Share your images](collect-new-images.md#share-your-images) for details.