Hi ptrblck,
First of all thanks for the constant help you provide to all the users here in the forums.
You can see a comparison between Titan and Tesla P100 here: https://www.videocardbenchmark.net/compare/TITAN-V-vs-Tesla-P100-PCIE-16GB/3859vs4039 8. As it seems, yours is much better thus computation time seems reasonable to me as well. I have some other things going on in the background that might take ~1-2 seconds extra, like calculating some statistics, keeping track of everything on tensorboard etc. Also, I noticed 2 more things:


I cannot achieve training or validation accuracy higher than 82%. I know that I can use some data augmentation techniques (like flipping, rotating etc., even though I didn’t), but I doubt this will get it over 90%. Is that normal? After examining a bunch of images in a batch on tensorboard, I noticed that all images after resize are losing some information (it’s like extra zoomed), and this might be the case that I cannot achieve better scores.


On some runs, validation scores are better than training ones. There might be a case that validation images might be easier to classify than the training ones. What I don’t get is why this happens. I splitted to training and validation by using torch.utils.data.random_split(dataset, [50000, 10000]).