Make artifacts available on HF

[NielsRogge]

Hi @morrisalp,

Niels here from the open-source team at Hugging Face. I discovered your work through the paper page: https://huggingface.co/papers/2407.08521 (congrats on the ECCV Oral!). I work together with AK on improving the visibility of researchers' work on the hub.

It'd be great to make the CLIP models available on the 🤗 hub, we can add tags so that people find them when filtering https://huggingface.co/models. See for instance: https://huggingface.co/models?search=clip, showcasing nearly 3k CLIP models.

We can add tags like "zero-shot-image-classification" so that people will find your work.

Uploading models

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

In this case, the CLIP models could probably be converted to the Transformers API: https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/convert_clip_original_pytorch_to_hf.py.

If you prefer the original CLIP format, 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.

Uploading dataset

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

from datasets import load_dataset

dataset = load_dataset("your-hf-org/hierarcaps")

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 🤗