Changes

The version 2A application was then ported to Python. This Python version is functionally equivalent to the 2A version, which is written in C++. The goal of version 2B is to make a comparison in terms of performance with respect to version 2A. Generally, Python has the advantage of being easier to work with, but at the cost of being slower to execute. However, in this case, '''regarding the inference computation''', the performance is '''pretty much the same between the two versions'''. This is because the Python API's act only as a wrapper to the core TensorFlow library written in C++ (and other "fast" languages). As detailed [[#Results comparison|in this section TBD]], the overall time is significantly different because it takes into account the pre/post-processing computations as well. These computations don't leverage the NPU accelerator and thus are more affected by the slower Python code. Nevertheless, in case the model used is much more complex as it usually occurs in real-world cases, this overhead could be still tolerable because it might be neglectable. In conclusion, the use of Python has not to be discarded a priori because of performance concerns. Depending on the specific use case, it can be a valid option to consider.