Scared_Employer6992 OP t1_ja7xpjt wrote
Reply to comment by QuadmasterXLII in [D] Training a UNet-like architecture for semantic segmentation with 200 outcome classes. by Scared_Employer6992
I haven't tried with bs=1, but I also don't want to use bs=1 as I usually get bad results with it and my net has a lot of BN layers.
badabummbadabing t1_ja7yxbg wrote
Don't use batch normalization. Lots of U-Nets use e.g. instance normalisation. A batch size of 1 should be completely fine (but you will need to play with the learning rate upon changing this). Check the 'no new U-Net' (aka NN-Unet) paper by Fabian Isensee for the definitive resource on what matters in U-Nets.
LetterRip t1_ja88v3i wrote
Which particular paper?
badabummbadabing t1_ja8bzhc wrote
https://cardiacmr.hms.harvard.edu/files/cardiacmr/files/isensee_etal_nature2021_nnunet.pdf Check Figure 4. Architecture barely matters on average.
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