Make artifacts (model, dataset) available on Hugging Face #2

Hi @ningyu1991,

Niels here from the open-source team at Hugging Face. I found your work through ECCV (congrats!), and indexed your paper here: https://huggingface.co/papers/2212.09877, congrats on getting it accepted to ECCV (feel free to claim authorship with your HF account). I work together with AK on improving the visibility of researchers' work on the hub.

I was wondering you'd be up for collaborating on releasing the checkpoints on the 🤗 hub, rather than Google Drive, to improve their discoverability. We can add tags so that people find them when filtering https://huggingface.co/models.

Uploading models

See here for a guide: https://huggingface.co/docs/hub/models-uploading. In case the models are custom PyTorch model, we could probably leverage the PyTorchModelHubMixin class which adds from_pretrained and push_to_hub to each model. 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. Moreover, we can then link the checkpoints to the paper page, improving their visibility.

Uploading dataset

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

from datasets import load_dataset

dataset = load_dataset("your-hf-org/ad-banner")

See here for a guide: https://huggingface.co/docs/datasets/image_dataset

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 🤗

Start by locating the model checkpoints and training dataset currently referenced as being on Google Drive; the issue names no repository files or tests. Review the Hugging Face model-uploading and image-dataset guides, along with PyTorchModelHubMixin, hf_hub_download, and load_dataset. Done means separate model repositories and a discoverable dataset are available on Hugging Face and linked to the paper.