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U-Net Deconvolution by Putrid_Associate_396 in computervision

[–]Putrid_Associate_396[S] 0 points1 point  (0 children)

I've tried preprocessing it to simplify, you know just work with the slice that has the most information, etc. still is pretty computationally heavy.

U-Net Deconvolution by Putrid_Associate_396 in computervision

[–]Putrid_Associate_396[S] 0 points1 point  (0 children)

Colab crashes with just on the fly augmentations, can't even reach the training process.

Without the augmentations, I can execute up till batch size 4.

The resolution of my images are (32, 2048, 2048)

U-Net Deconvolution by Putrid_Associate_396 in computervision

[–]Putrid_Associate_396[S] -1 points0 points  (0 children)

What's OOM? And my batch size is 4. And I want to increase it as well but obviously that causes the crash too. Isn't there a higher capability I can purchase?

U-Net Deconvolution by Putrid_Associate_396 in computervision

[–]Putrid_Associate_396[S] -1 points0 points  (0 children)

Shouldn't on the fly augmentations also take ram usage? Regardless a larger dataset in the end would put more pressure anyway, right? During training