Release artifacts (models, dataset) on Hugging Face

[NielsRogge]

Hello,

Niels here from the open-source team at Hugging Face. I discovered your work through Hugging Face's daily papers as yours got featured: https://huggingface.co/papers/2511.16618.
The paper page lets people discuss about your paper and lets them find artifacts about it (your models, datasets or demo 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 saw in your abstract and GitHub README (https://github.com/jinlab-imvr/SAM2S) that the source code for SAM2S and the SA-SV dataset are "coming soon" / "will be released". It'd be great to make the SAM2S model checkpoints and the SA-SV dataset available on the 🤗 hub once they are ready, to improve their discoverability/visibility.
We can add tags so that people find them when filtering https://huggingface.co/models and https://huggingface.co/datasets.

Uploading models

See here for a guide: https://huggingface.co/docs/hub/models-uploading.

In this case, we could leverage the PyTorchModelHubMixin class which adds from_pretrained and push_to_hub to any custom nn.Module. Alternatively, one can leverages the hf_hub_download one-liner to download a checkpoint from the hub.

We encourage researchers to push each model checkpoint to a separate model repository, so that things like download stats also work. We can then also link the checkpoints to the paper page. For SAM2S, a relevant pipeline tag would be "image-segmentation" for the interactive video object segmentation task.

Uploading dataset

Would be awesome to make the SA-SV dataset available on 🤗 , so that people can do:

from datasets import load_dataset

dataset = load_dataset("your-hf-org-or-username/your-dataset")

See here for a guide: https://huggingface.co/docs/datasets/loading. For SA-SV, a relevant task category would be "image-segmentation" given its instance-level spatio-temporal annotations (masklets).

Besides that, there's the dataset viewer which allows people to quickly explore the first few rows of the data in the browser.

Let me know if you're interested/need any help regarding this when you are ready to release!

Cheers,

Niels
ML Engineer @ HF 🤗