Release DGMR checkpoints as individual model repositories on Hugging Face

[NielsRogge]

Hello @visresearch 🤗

Niels here from the open-source team at Hugging Face. I discovered your work on Arxiv and was wondering whether you would like to submit it to hf.co/papers to improve its discoverability. If you are one of the authors, you can submit it at https://huggingface.co/papers/submit.

The paper page lets people discuss about your paper and lets them find artifacts about it (your models for instance), you can also claim the paper as yours which will show up on your public profile at HF, add Github and project page URLs.

I noticed that you have links to the trained weights in the Github README, but they are currently located under subfolders of the main repository https://huggingface.co/visresearch/DGMR/tree/main, instead of being separate model repositories. It would be great to have each checkpoint as a separate model repository on the Hugging Face Hub. This improves discoverability and enables features like download statistics for each model.

We can add tags in the model cards so that people find the models easier, and link them to the paper page.

If you're down, leaving a guide here. If it's a custom PyTorch model, you can use the PyTorchModelHubMixin
class which adds from_pretrained and push_to_hub to the model which lets you to upload the model and people to download and use models right away.
If you do not want this and directly want to upload model through UI or however you want, people can also use hf_hub_download.

After uploaded, we can also link the models to the paper page (read here) so people can discover your model.

Let me know if you're interested/need any guidance :)

Kind regards,

Niels
ML Engineer @ HF 🤗