Feiyu Bi

the code is from this script : segmentation_models_3D/models/unet.py # model head (define number of output classes) x = layers.Conv3D( filters=classes, kernel_size=(3, 3, 3), padding='same', use_bias=True, kernel_initializer='glorot_uniform', name='final_conv', )(x)

Thanks, as I have seen, most of the prediction head in Unet have implemented Conv2D with kernel size 1. So I am not sure if the kernel size 3 or...

Thanks,I’m just wondering if directly upscale the out7 and out8 feature map to original resolution using up- sampling layer will have any influence on performance of the model?