The MedVidU Challenge runs in two phases. Phase 1 is a public leaderboard on the MedVidBench test set. The top 5 teams from Phase 1 advance to Phase 2, where their models are evaluated on a completely held-out test set via a provided Docker image. Follow the steps below.
Phase 1 — Public Leaderboard
Download the training data and train your model
- Download the MedVidU ECCV 2026 train/val split from UII-AI/MedVidU_ECCV2026_TrainVal on Hugging Face.
- Sign in to Hugging Face, click Request access, fill out the short form, and agree to the terms of use. Requests are reviewed manually and typically approved within a few business days.
- Once approved, you may use this split for training and local validation, alongside any other open-source or internal data of your choice.
Submit predictions to the MedVidBench Leaderboard
Run inference on the MedVidBench test set and submit your predictions to the MedVidBench Leaderboard.
Include the following with your submission:
- Model Name
- Team Name
- Authors
- Institution
- Email ID — mandatory for MedVidU ECCV 2026 Challenge participants
- Remark:
ECCV challenge— required to flag your entry as part of the challenge
Submit a report on OpenReview Required
All challenge participants are required to submit a report describing their method on OpenReview for the MedVidU Challenge. The submission link will be announced soon.
Phase 2 — Held-Out Evaluation (Top 5 Teams) (starts on July 13)
Download the evaluation Docker image Coming soon
The top 5 teams on the Phase 1 leaderboard will be invited to Phase 2. We will release a base Docker image with the evaluation harness and required entry-point interface. Build your submission on top of this image so that it is compatible with our internal evaluation pipeline.
Submit your Docker image
Package your trained model inside the provided Docker image and submit it to the organizers by email. We will run your container on the completely held-out test set to determine the final challenge ranking.
Confidentiality. We encourage teams to open-source their models to benefit the community, but this is not required. Teams that wish to keep their models private can do so without concern: submitted Docker images (and the model weights they contain) are treated as confidential. They will be accessed only by the organizing team for the sole purpose of running the held-out evaluation, will not be shared with any third party, will not be redistributed or open-sourced by us, and will be permanently deleted from our infrastructure once final rankings are determined.