Instructions to use SixAILab/nepa-base-patch14-224-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SixAILab/nepa-base-patch14-224-sft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="SixAILab/nepa-base-patch14-224-sft") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import ViTNepaForImageClassification model = ViTNepaForImageClassification.from_pretrained("SixAILab/nepa-base-patch14-224-sft", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Improve model card: Add image classification pipeline tag, GitHub link, and update project page URL
#1
by nielsr HF Staff - opened
This PR enhances the model card by making the following improvements:
- Added
pipeline_tag: image-classification: This metadata ensures the model is correctly categorized on the Hugging Face Hub, improving its discoverability for users looking for image classification models. - Added GitHub repository link: A direct link to the official GitHub repository (
https://github.com/SihanXU/nepa) has been included as a badge for easy access to the code. - Updated Project Page URL: The existing "Project Page" badge has been updated to point to the correct project website:
https://sihanxu.me/nepa, resolving a factual discrepancy mentioned in the paper info.
The rest of the model card content, including the valuable "Quick Start" usage example and the arXiv paper link, has been preserved as it provides complete and accurate information.
sihanxu changed pull request status to merged