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* The OpenCV library.
As stated in the introduction, the classifier is based on a model that was generated with Azure Custom Vision. In particular, the model was retrieved from [https://dev.to/azure/creating-an-image-recognition-solution-with-azure-iot-edge-and-azure-cognitive-services-4n5i this project] by [https://dev.to/gloveboxes Dave Glover]. Glover's project is extremely useful to understand how Custom Vision—and, in general, Azure Cognitive Services—work. Glover followed the approach suggested by Azure, which makes use of containers. For the sake of simplicity, this Technical Note It is based on a simpler strategy, which is closer worth remembering that no particular Machine Learning-related skills are required to the usual approach used in the embedded world. As create such, it doesn't make use of any containera model.
Glover's project follows the approach suggested by Azure, which makes use of containers. For the sake of simplicity, this Technical Note is based on a simpler strategy, which is closer to the usual approach used in the embedded world. As such, it doesn't make use of any container.
Once the TensorFlow model is deployed on the SBCX, the classifier can work without any Internet connection. In other words, the SBCX can perform the classification task autonomously.
==Performances==
