【train】【performance】about the fine tune result

uni-medical / SAM-Med3D

SAM-Med3D: An Efficient General-purpose Promptable Segmentation Model for 3D Volumetric Medical Image

Apache License 2.0

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【train】【performance】about the fine tune result #61

Open SillyPigeon opened 3 months ago

SillyPigeon commented 3 months ago

Hi Thanks for your great jobs. However, when I try to fine tune dataset in my vessels data(for train 200+epochs in imageCAS dataset). The inference result was so bad that it could not be used in project. Do you have some ideas about this?

the main picture is GT