Machine learning optimization with python

Your task is to prepare data reader for the training code and evaluate several training configurations, which are:

1-UNet vs LinkNet vs MobileUNet comparison

2-Increased filter size (from 3x3 to e.g. 7x7)

3-Location augmentation as described here: [login to view URL]

4-SegCaps - 4th architecture to test (as an addition to 3 architectures from #1) which should be taken from here: [login to view URL]

_This means you need to perform at least 6 training (3 for #1 and 1 for each of #2, #3 and #4). Make sure you optimize hyperparams before the actual training.

-Hopt module should help in this, but you can obviously perform the optimizations your own way. If you decide to stick to hopt, you can find some explanations here: [login to view URL]

after you finish, you have to run the code on my laptop and show the comparison between each model which one the best and why .. by create reports and put all the results for each model with graphs. please put comments inside the code to make everything clear

Taidot: C-ohjelmointi, C++ -ohjelmointi, Machine Learning (ML), Python, tietojärjestelmäarkkitehtuuri

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Tietoa työnantajasta:
( 4 arvostelua ) Gdańsk, Poland

Projektin tunnus: #20051787

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