You're making a critical mistake. Why make a network for a competition that won't produce great results without major modifications on real data? That is the m

You're making a critical mistake. Why make a network for a competition that won't produce great results without major modifications on real data? That is the main problem, and the lack of systems thinking is on your side. There is a strong pressure to cheat and overfit. Sharing in fact makes this even stronger. We've had some fun with that when trying to use ML for something as complex as music rhythm envelope extraction. (Which is easy in comparison to CT test.) Best results were approaching 90% accuracy on the big suite, but real results were closer to 40%. A slightly worse solution did reliably 70%. (And was not a neural network even, plus possible to improve.) General best approaches sometimes indeed work, but sometimes (often?) they are overfitted in architecture, not even dataset.