Sharing Large Pre-Trained Models

[Lucas Erlacher]

Dear Challenge Organizers,

I have two questions:

- we are using a pre-trained model (that we are fine-tuning) which is about 300MB in size. given that GitHub limits each individual file to not exceed 100MB how would you recommend us to share these pre-trained weights s.t. you can run our training on your end?

- I have just seen that in your test dataset you will use different sources than "code_15", "ptbxl" and "samitrop". since we are currently using this source information in our training code we need to understand what exactly will change: will the format of the header itself be different, or will the format remain the same with only the source string differing?

[PhysioNet Challenge]

Dear Lucas,

There are multiple options for uploading larger model weights, including using Git LFS, using GitLab, or downloading model weights while building your Docker image. Some teams may also decide not to save all of the model weights, or to save them with fewer bits.

(Again, we encourage teams not upload models to avoid submitting working training code or the provide training resource constraints. We will verify that the submitted training code actually runs and learns from the training data for rankings and prize eligibility.)

The data in the validation and test sets are from different sources than the data in the training set. The validation and test data are formatted exactly the same as the training data, except for the labels or sources; we do not include the labels or data sources in the validation and test sets so that teams can infer the labels and to better assess how teams generalize to new data sources.
https://moody-challenge.physionet.org/2025/#data

Best,
Matt
(On behalf of the Challenge team.)

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