Hi @synbol 🤗
Niels here from 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/2402.02242.
The paper page lets people discuss about your paper and lets them find artifacts about it (your models, datasets or demo for instance), you can also claim
the paper as yours which will show up on your public profile at HF, add Github and project page URLs.
It's fantastic to see that you've already made the V-PEFT Bench processed datasets (FGVC, VTAB-1k) and many of the fine-tuned image classification and video action recognition model checkpoints available on the 🤗 Hub (e.g., at huggingface.co/XiN0919/v-pelt-benchmark)! This greatly enhances their visibility and discoverability for the community.
I noticed from your GitHub README that the dense prediction model checkpoints (for COCO, PASCAL VOC, and ADE20K tasks with Swin-B and Swin-L) are marked as "Coming Soon". It would be awesome to also have these models available on the 🤗 Hub once they are ready, to complete the benchmark's open-source release.
Additionally, for the existing fine-tuned models hosted in the v-pelt-benchmark repository, we could help you organize them into individual model repositories on the Hugging Face Hub (e.g., synbol/vit-b-adapter-fgvc) to enable easier from_pretrained loading and better discoverability through specific pipeline tags and metadata. This would further boost their usability!
Uploading models
See here for a guide: https://huggingface.co/docs/hub/models-uploading.
In this case, 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
See here for a guide: https://huggingface.co/docs/datasets/loading.
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 🤗
Hi @synbol 🤗
Niels here from 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/2402.02242.
The paper page lets people discuss about your paper and lets them find artifacts about it (your models, datasets or demo for instance), you can also claim
the paper as yours which will show up on your public profile at HF, add Github and project page URLs.
It's fantastic to see that you've already made the V-PEFT Bench processed datasets (FGVC, VTAB-1k) and many of the fine-tuned image classification and video action recognition model checkpoints available on the 🤗 Hub (e.g., at
huggingface.co/XiN0919/v-pelt-benchmark)! This greatly enhances their visibility and discoverability for the community.I noticed from your GitHub README that the dense prediction model checkpoints (for COCO, PASCAL VOC, and ADE20K tasks with Swin-B and Swin-L) are marked as "Coming Soon". It would be awesome to also have these models available on the 🤗 Hub once they are ready, to complete the benchmark's open-source release.
Additionally, for the existing fine-tuned models hosted in the
v-pelt-benchmarkrepository, we could help you organize them into individual model repositories on the Hugging Face Hub (e.g.,synbol/vit-b-adapter-fgvc) to enable easierfrom_pretrainedloading and better discoverability through specific pipeline tags and metadata. This would further boost their usability!Uploading models
See here for a guide: https://huggingface.co/docs/hub/models-uploading.
In this case, we could leverage the PyTorchModelHubMixin class which adds
from_pretrainedandpush_to_hubto any customnn.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
See here for a guide: https://huggingface.co/docs/datasets/loading.
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 🤗