Systems engineer Yacine recommends training models on a single GPU in under one minute to maximize research learning rates
Lucas Beyer clarified BiternionNet was developed at RWTH Aachen
@yacineMTB Btw this is my model/code i wrote while i was at RWTH Aachen, Long before my Zürich time :) (And I've never been student at ETH)
kache@yacineMTBif you're doing AI research at all; I recommend doing the "ETH zurich" route Train models that use a single GPU. Make sure that it takes less than a minute to train models. Pufferlib is a great example. The more models you train the more you learn
#55Lucas Beyer (bl16)@GIFFMANA@yacineMTB These two posts (Yacine and PINTO) motivated me to write up more info about this project, if anyone is curious:
Lucas Beyer (bl16)@giffmanaAlright, it's time for a paper thread about my own first ever vision paper, which is having a bit of a moment on twitter rn thanks to @PINTO03091 and @yacineMTB. BiternionNets: continuous head orientation from discrete labels. Demo video from ~11y ago:
#80Sander Dieleman@SEDIELEM💯AI research often isn't so much research, as it is "throwing stuff at the wall and seeing what sticks". If you can throw more stuff, you will learn more, and also more stuff will stick. @kaggle was a great place for me to internalise this, but it is also true in the real world!
kache@yacineMTBif you're doing AI research at all; I recommend doing the "ETH zurich" route Train models that use a single GPU. Make sure that it takes less than a minute to train models. Pufferlib is a great example. The more models you train the more you learn