Hi @danjacobellis 馃
I'm Niels and work as part of the open-source team at Hugging Face. I discovered your work through Hugging Face's daily papers as yours got featured: https://huggingface.co/papers/2510.13714.
The paper page lets people discuss about your paper and find artifacts about it.
I noticed that you've already shared the datasets (bdd100k_train and bdd500_pl_f14) on the Hub, which is awesome for discoverability and reproducibility!
Would you like to also host the model checkpoints you've pre-trained (like dedelayed_v1_efficientvitl1_mstransformer2d) on https://huggingface.co/models?
Hosting on Hugging Face will give your models more visibility and enable better discoverability. We can add metadata tags in the model cards so that people find the models easier when filtering by task (e.g., image-segmentation) and link them directly to the paper page.
If you're down, leaving a guide here. If it's a custom PyTorch model, you can use the PyTorchModelHubMixin class which adds from_pretrained and push_to_hub to the model, which lets you to upload the model and allows people to download and use them right away. Alternatively, people can also use hf_hub_download.
After they are uploaded, we can link the models to the paper page so people can discover your work more easily.
Let me know if you're interested/need any guidance :)
Kind regards,
Niels
ML Engineer @ HF 馃
Hi @danjacobellis 馃
I'm Niels and work as part of the open-source team at Hugging Face. I discovered your work through Hugging Face's daily papers as yours got featured: https://huggingface.co/papers/2510.13714.
The paper page lets people discuss about your paper and find artifacts about it.
I noticed that you've already shared the datasets (
bdd100k_trainandbdd500_pl_f14) on the Hub, which is awesome for discoverability and reproducibility!Would you like to also host the model checkpoints you've pre-trained (like
dedelayed_v1_efficientvitl1_mstransformer2d) on https://huggingface.co/models?Hosting on Hugging Face will give your models more visibility and enable better discoverability. We can add metadata tags in the model cards so that people find the models easier when filtering by task (e.g.,
image-segmentation) and link them directly to the paper page.If you're down, leaving a guide here. If it's a custom PyTorch model, you can use the PyTorchModelHubMixin class which adds
from_pretrainedandpush_to_hubto the model, which lets you to upload the model and allows people to download and use them right away. Alternatively, people can also use hf_hub_download.After they are uploaded, we can link the models to the paper page so people can discover your work more easily.
Let me know if you're interested/need any guidance :)
Kind regards,
Niels
ML Engineer @ HF 馃