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ge-xing / Diff-UNet

Diff-UNet: A Diffusion Embedded Network for Volumetric Segmentation. (using diffusion for 3D medical image segmentation)
Apache License 2.0
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Training results using 2 GPUs #18

Open jhonson592 opened 1 year ago

jhonson592 commented 1 year ago

Thanks for releasing the paper and the source code!

I experimented with GPU=2 since I only have 2 GPUs. I experimented with batch_size=4 and found that the mean_dice during training was abnormally low compared to other people's results.

Results of the experiment with batch_size=4 training mean_dice = 0.5956 wt = 0.9142 tc = 0.6453 et = 0.6193

mean_dice train_loss et

Are there any parameters or codes that should be changed by reducing the number of GPUs?

920232796 commented 1 year ago

The result is strange. In my experiment, the result is not related to the number of gpus.

jhonson592 commented 1 year ago

Thank you for reply!

Maybe there is a difference between the different versions. If you don't mind, could you please tell me the versions of python, torch, numpy, monai and SimpleITK?

Liour commented 7 months ago

Thank you for reply!

Maybe there is a difference between the different versions. If you don't mind, could you please tell me the versions of python, torch, numpy, monai and SimpleITK?

may I ask about your Python, Torch, and monai versions, I encountering many warnings when training start, and I cannot achieve the performance you reproduced.