Coffee bean recognition

The use case presented is based on a coffee bean dataset to recognize the level of roasting. The solution proposes a model trained on a public dataset providing very good accuracy while running on an STM32.
This model was trained thanks to the Python scripts provided in the STM32 model zoo (read our tutorial on the training to learn more).
Here, after setting up our STM32 Model Zoo project, we downloaded the dataset and placed it in the appropriate structure.
Then we updated the yaml file, which was used to configure the training, with the following settings:
general:
project_name: coffee_bean
dataset:
name: coffee
class_names: [Green, Light, Medium, Dark]
training_path: datasets/train
validation_path:
test_path: datasets/test
Feel free to adjust those settings according to your use-case.
Finally, we simply ran the "train.py" script.

You will find everything you need in the STM32 model zoo to train and retrain the model with your own data. The model can also be easily deployed on a STM32H747 discovery kit with the Python scripts provided in the repository (read our tutorial on the deployment to learn more).
You can find details of all these steps in our Getting started video.