Baseline comparison for 2D dataset
wshi8 opened this issue · 6 comments
Hi Dr. Lee, do you have the code that you used to compare with various baselines in section 5.2 (list of other models) for 2D dataset? Is there a similar comparison for the 3D dataset?
Yes all the baseline code is included in this repo. You just need to provide different --net parameters. For example, 'unet-scratch' for vanilla u-net, 'unet' for using pretrained encoders (--bb eff-b4 to use efficientnet-b4 as the encoder).
For 3D images, only Unet-3D and V-Net were included in the code. There are two more baselines in the paper, whose results were extracted from their papers.
Thanks a lot, I tried to run test using vnet
python3 test3d.py --task [mydataset] --split all --bs 1 --ds 2020valid --net vnet --attractors 1024 --translayers 2
but got the following errors:
Error Given groups=1, weight of size [16, 1, 3, 3, 3], expected input[1, 2, 112, 112, 96] to have 1 channels, but got 2 channels instead
I see. In test3d.py, the input channels of V-Net / U-Net 3d are hardcoded as 1. You could try to change it to 2 and see what you can get. Anyway at the later stage of my development, I no longer used these models, so their API calls may contain bugs.
Thanks! Is there or Do I need to upload any checkpoint for the U-net/V-net 3D t for testing?
Do you mean pretrained checkpoints? I don't have them at hand. You have to train first, then do evaluation.