[ECCV 2024] This repository represents the official implementation of PARE-Net: Position-Aware Rotation-Equivariant Networks for Robust Point Cloud Registration
I'm Merve and I work on computer vision at Hugging Face. I see your paper is accepted as ECCV Posters congratulations! I indexed it here https://huggingface.co/papers/2407.10142 at paper pages. The paper page lets people discuss about your paper and lets them find artifacts about it (your model for instance) you can also claim the paper as yours which will show up on your public profile at HF.
Would you like to host the model you've pre-trained with this technique on https://huggingface.co/models? I see you're using GDrive for it. Hosting on Hugging Face will give your work more visibility.
If you're down, leaving a guide here. If it's a PyTorch model, you can use PyTorchModelHubMixin class which adds from_pretrained and push_to_hub to the model which lets you to upload the model and people to download and use models right away.
If you do not want this and directly want to upload model through UI or however you want, people can also use hf_hub_download.
After uploaded, we can also link the models to the paper page (read here) so people can discover your model.
You can also build a demo to your model on Spaces we can provide you an A100 grant.
Hello @yaorz97 🤗
I'm Merve and I work on computer vision at Hugging Face. I see your paper is accepted as ECCV Posters congratulations! I indexed it here https://huggingface.co/papers/2407.10142 at paper pages. The paper page lets people discuss about your paper and lets them find artifacts about it (your model for instance) you can also claim the paper as yours which will show up on your public profile at HF.
Would you like to host the model you've pre-trained with this technique on https://huggingface.co/models? I see you're using GDrive for it. Hosting on Hugging Face will give your work more visibility.
If you're down, leaving a guide here. If it's a PyTorch model, you can use PyTorchModelHubMixin class which adds from_pretrained and push_to_hub to the model which lets you to upload the model and people to download and use models right away. If you do not want this and directly want to upload model through UI or however you want, people can also use hf_hub_download.
After uploaded, we can also link the models to the paper page (read here) so people can discover your model. You can also build a demo to your model on Spaces we can provide you an A100 grant.
What do you think?