@@ -77,9 +77,9 @@ for each song. Finally, the overall score is obtained by averaging SDRsong over
# 🤖 Baselines
We use the [Open-Unmix](https://github.com/sigsep/open-unmix-pytorch) library for the baseline. Specifically, we provide trained checkpoints for the UMXL model. You can use the baseline by switching to the `openunmix-baseline`[branch](https://gitlab.aicrowd.com/aicrowd/challenges/music-demixing-challenge-2023/adx-2023-music-demixing-track-starter-kit/-/blob/openunmix-baseline/) on this repository. To test the models locally, you need to install `git-lfs`.
We use the [Open-Unmix](https://github.com/sigsep/open-unmix-pytorch) library for the baseline. Specifically, we provide trained checkpoints for the UMXL model. You can use the baseline by switching to the `openunmix-baseline`[branch](https://gitlab.aicrowd.com/aicrowd/challenges/sound-demixing-challenge-2023/sdx-2023-music-demixing-track-starter-kit/-/blob/openunmix-baseline/) on this repository. To test the models locally, you need to install `git-lfs`.
When submitting your own models, you need to submit the checkpoints using `git-lfs`. Check the instructions shared in the inference file [here](https://gitlab.aicrowd.com/aicrowd/challenges/music-demixing-challenge-2023/adx-2023-music-demixing-track-starter-kit/-/blob/openunmix-baseline/my_submission/openunmix_separation_model.py)
When submitting your own models, you need to submit the checkpoints using `git-lfs`. Check the instructions shared in the inference file [here](https://gitlab.aicrowd.com/aicrowd/challenges/sound-demixing-challenge-2023/sdx-2023-music-demixing-track-starter-kit/-/blob/openunmix-baseline/my_submission/openunmix_separation_model.py)
