Hi,
Niels here from the open-source team at Hugging Face. As your paper is accepted as ECCV Oral, I indexed the paper page: https://huggingface.co/papers/2407.07402 (feel free to claim the paper so that it appears under your HF account!). I work together with AK on improving the visibility of researchers' work on the hub.
Was wondering if you're up for improving the discoverability of your work by making the models available on the hub once the checkpoints are released, we can add tags so that people find them when filtering https://huggingface.co/models.
I'll add a guide below.
Uploading models
In terms of uploading models, we provide a guide here: https://huggingface.co/docs/hub/models-uploading. In case the model is a custom PyTorch model, we could probably leverage the PyTorchModelHubMixin class which adds from_pretrained and push_to_hub to the 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. We can then also link the models to the paper page, as explained here: https://huggingface.co/docs/hub/en/model-cards#linking-a-paper
Let me know if you need any help regarding this! Happy to send a PR.
Cheers,
Niels
ML Engineer @ HF 🤗
Hi,
Niels here from the open-source team at Hugging Face. As your paper is accepted as ECCV Oral, I indexed the paper page: https://huggingface.co/papers/2407.07402 (feel free to claim the paper so that it appears under your HF account!). I work together with AK on improving the visibility of researchers' work on the hub.
Was wondering if you're up for improving the discoverability of your work by making the models available on the hub once the checkpoints are released, we can add tags so that people find them when filtering https://huggingface.co/models.
I'll add a guide below.
Uploading models
In terms of uploading models, we provide a guide here: https://huggingface.co/docs/hub/models-uploading. In case the model is a custom PyTorch model, we could probably leverage the PyTorchModelHubMixin class which adds
from_pretrainedandpush_to_hubto the 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. We can then also link the models to the paper page, as explained here: https://huggingface.co/docs/hub/en/model-cards#linking-a-paper
Let me know if you need any help regarding this! Happy to send a PR.
Cheers,
Niels
ML Engineer @ HF 🤗