Hi @m-saeid 🤗
Niels here from the open-source team at Hugging Face. I discovered your work on Arxiv and found your paper "Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet" to be very interesting! It's currently featured on Hugging Face's daily papers: https://huggingface.co/papers/2509.05198.
The paper page lets people discuss your work and find related artifacts (such as your models and datasets). You can also claim the paper as yours, which will show up on your public profile at HF, and add GitHub and project page URLs.
It'd be great to make the ModelNet-R dataset and pre-trained Point-SkipNet checkpoints available on the 🤗 Hub, to improve their discoverability and visibility. We can add tags so that people find them when filtering https://huggingface.co/models and https://huggingface.co/datasets.
Uploading models
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.
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 leverage the hf_hub_download one-liner to download a checkpoint from the Hub.
Uploading dataset
Would be awesome to make the ModelNet-R dataset available on 🤗 , so that people can do:
from datasets import load_dataset
dataset = load_dataset("your-hf-org-or-username/ModelNet-R")See here for a guide: https://huggingface.co/docs/datasets/loading.
We also support Webdataset, useful for image/video/3D datasets: https://huggingface.co/docs/datasets/en/loading#webdataset.
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!
Cheers,
Niels
ML Engineer @ HF 🤗
Hi @m-saeid 🤗
Niels here from the open-source team at Hugging Face. I discovered your work on Arxiv and found your paper "Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet" to be very interesting! It's currently featured on Hugging Face's daily papers: https://huggingface.co/papers/2509.05198.
The paper page lets people discuss your work and find related artifacts (such as your models and datasets). You can also claim the paper as yours, which will show up on your public profile at HF, and add GitHub and project page URLs.
It'd be great to make the ModelNet-R dataset and pre-trained Point-SkipNet checkpoints available on the 🤗 Hub, to improve their discoverability and visibility. We can add tags so that people find them when filtering https://huggingface.co/models and https://huggingface.co/datasets.
Uploading models
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.
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 leverage the hf_hub_download one-liner to download a checkpoint from the Hub.Uploading dataset
Would be awesome to make the ModelNet-R dataset available on 🤗 , so that people can do:
See here for a guide: https://huggingface.co/docs/datasets/loading.
We also support Webdataset, useful for image/video/3D datasets: https://huggingface.co/docs/datasets/en/loading#webdataset.
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!
Cheers,
Niels
ML Engineer @ HF 🤗