Hi @apple1986 🤗
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, 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.
Your abstract mentions that "Code, datasets, and models will be publicly available at https://github.com/apple1986/DD-SAM2". It'd be great to make the DD-SAM2 model checkpoints and any newly introduced datasets (like those related to TrackRad2025 and EchoNet-Dynamic) available on the 🤗 hub once they are released, 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 DD-SAM2, a relevant pipeline tag would be image-segmentation or image-to-image for medical object segmentation.
Uploading dataset
Would be awesome to make the datasets (e.g., your adapted versions of TrackRad2025 and EchoNet-Dynamic) 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 these medical video datasets, relevant task categories would be image-segmentation, object-detection, or video-classification.
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 once the artifacts are released!
Cheers,
Niels
ML Engineer @ HF 🤗
Hi @apple1986 🤗
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, 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.
Your abstract mentions that "Code, datasets, and models will be publicly available at https://github.com/apple1986/DD-SAM2". It'd be great to make the DD-SAM2 model checkpoints and any newly introduced datasets (like those related to TrackRad2025 and EchoNet-Dynamic) available on the 🤗 hub once they are released, 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_pretrainedandpush_to_hubto any customnn.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 DD-SAM2, a relevant pipeline tag would be
image-segmentationorimage-to-imagefor medical object segmentation.Uploading dataset
Would be awesome to make the datasets (e.g., your adapted versions of TrackRad2025 and EchoNet-Dynamic) available on 🤗 , so that people can do:
See here for a guide: https://huggingface.co/docs/datasets/loading. For these medical video datasets, relevant task categories would be
image-segmentation,object-detection, orvideo-classification.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 once the artifacts are released!
Cheers,
Niels
ML Engineer @ HF 🤗