-
Notifications
You must be signed in to change notification settings - Fork 14
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
The evaluation results using the provided pretrained checkpoint are not consistent with that in the paper. #14
Comments
|
Sorry we just did some modification to the training and testing code, but the checkpoint is not updated yet. I will update the checkpoint within this week. |
So, is the training and test code provided on GitHub the version before or after your update? |
The code is after the updating. But the checkpoint is before the updating (where the architecture is actually one of the ablation). Sorry very much for the inconvenience. |
thanks for your reply |
Dear author Have you updated the latest checkpoint so that I can reproduce your results? |
Hi, I just uploaded checkpoint for MSD-pancreas, lits and MSD-Colon. The training of Kits takes longer and will be uploaded soon. Best |
Dear author, thanks for your contribution! Which doesnt happen with the MSD-pancreas weights and I have the recommended versions and packages , do you have any advice on this ? |
Hi, |
Thank you! It works now! |
Dear author, I also meet the similar problem when using the pretrained checkpoint for the MSD-colon, it says "RuntimeError: PytorchStreamReader failed reading zip archive: failed finding central directory" Can you please make sure the uploaded checkpoint for the MSD-colon is correct? Thank you so much |
Hi, we have uploaded a new one. Can you try again? |
2023-10-14 19:53:25,603 - - Test metrics Dice: 0.58578235 Hi, thanks a lot for your work, I tested colon with --num_prompts 1 , but in the test.py, can we use rand_crop_size in test data? which means that a volume has more than one point, and should the testing phase be better at the original size? (no rand_crop) . Or comparative experiments with other models, for example, does nnU-Net also use random crop size when testing? |
Hi, for the test, we also use rand_crop_size, which is set to be the same as during the training. nnU-Net using a sliding window inference mechanism where the prediction is also predicted on a patch-wise basis but all the patches in the image are involved for inference. Our method requires a point as a prompt, as a result, we cannot predict for those patches far away from the prompt. Only patches with prompts inside can be used for prediction. Actually, this is also one advantage of interactive segmentation, we have knowledge about where the foreground is by prompts. |
Thanks for your reply, and I also want to know that if we need same crop size for different models in both train and test step, does the comparative experiment need to be set like this, because it seems that crop size has a great impact on the result during testing. |
No description provided.
The text was updated successfully, but these errors were encountered: