Release Power Battery Detection artifacts (models, dataset) on Hugging Face

[NielsRogge]

Hi @Xiaoqi-Zhao-DLUT 🤗

Niels here from the open-source team at Hugging Face. I discovered your work on Arxiv and noticed it has been featured on Hugging Face's paper page: https://huggingface.co/papers/2508.07797.
The paper page lets people discuss your paper and find related artifacts (like your models, datasets, or demos). 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 pre-trained checkpoints (MDCNeXt, PBD5K_Crop, MDCNet) and the PBD5K datasets 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

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.

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.

Uploading dataset

Would be awesome to make the datasets 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.
We also support Webdataset, useful for image/video 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 🤗