Comments (5)
The Swin UNETR model has 62M parameters. You can find this information in Table S.3. in this link. The official release also reports the number of parameters here.
Best,
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You can get the sum of trainable weights from a model with sum(p.numel() for p in model.parameters() if p.requires_grad)
or using the torchinfo package to get a summary with that information.
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Thank you, I want to know how many parameters in the original paper, to compare the results with another model.
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@ahatamiz would you have the parameter count for your paper implementation? Thanks!
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Hi @ahatamiz, besides the parameters, I see the FLOPs of Swin UNETR reported in the paper is 394.84G, would you release the code to calculate this? Thank you.
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Related Issues (20)
- One problem came when I was using SwinUNETR to train my own data HOT 5
- Invalid organ segmentation nii file produced from prostate_mri_lesion_seg_app
- SwinUNETR-V2
- About Test HOT 4
- about evaluation HOT 1
- About SwimUNETR Model Structure
- Unnecessary conv3 Layer Weights in DAE with MONAI v0.8.0 Leading to Compatibility Issues with v0.9.0+
- FileNotFoundError in swinunetr
- Swin-UNETR pretraining validation criterion
- AttributeError: 'NoneType' object has no attribute 'items' in Auto3dSeg
- Not reproducible results
- AttributeError: 'NoneType' object has no attribute 'items' in swinunetr algo
- Brats2020 HOT 3
- Evaluation
- difference brats2021 train test
- Unexpected key(s) in state_ dict: "epoch", "best_acc", "state_dict". HOT 3
- N foreground 0, N background 52003401,unable to generate class balanced samples. NaN or Inf found in input tensor.
- Understanding the contrastive loss implementation HOT 1
- Poor reconstruction usinf DAE pre-trained weights
- Swin-UNETR pretrain roi size
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