Skip to content
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

Evaluation on BCSS dataset #1

Open
o0t1ng0o opened this issue Jun 4, 2023 · 2 comments
Open

Evaluation on BCSS dataset #1

o0t1ng0o opened this issue Jun 4, 2023 · 2 comments

Comments

@o0t1ng0o
Copy link

o0t1ng0o commented Jun 4, 2023

Hi @ynulonger,

Thank you for sharing your code.
Can you share the code for testing on BCSS dataset?

Best,

@ynulonger
Copy link
Owner

Sorry for the later response. The following command can be used to run testing:
python -u predict_ada_hist.py -w checkpoints/BCSS/ds_True_Ada_scse/0_0.001_Ada_scse -o scse -l 1234_pred -v soft -d BCSS

  • -w path to the checkpoints, where you save the trained models.
  • -o feature re-calibration in skip connection, option: [scse, None].
  • -l specify the output from the ADS_UNet. option:

    1234_pred, Collecting softmax prediction from each base $UNet^d$
    1, Only the output of the UNet$^1$ is returned
    ...
    4, Only the output of the UNet$^4$ is returned

  • -v voting strategy, option:

    soft, $\alpha$ voting
    mean, average voting

  • -d dataset, option: [BCSS, CRAG, Kumar]
    Still working on cleaning the code and will update the README later, apologize for bring you confusion.

Best,

@guascy666
Copy link

Hi,thanks for your code,i want to konw that how to generate the whole image from the list of the cropped patch image

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

3 participants