Image Feature Extraction
Transformers
Safetensors
rudolf
pathology
histopathology
computational-pathology
digital-pathology
vision-transformer
foundation-model
self-supervised
feature-extraction
biology
medical
custom_code
Instructions to use Aignostics/RudolfV-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aignostics/RudolfV-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="Aignostics/RudolfV-2", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Aignostics/RudolfV-2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model added to Hugging Science and featured on the banner
#1
by EmmaScharfmann - opened
Hi @Aignostics π€
I'm Emma, from the science team at Hugging Face. Congrats and thanks for open-sourcing Aignostics/RudolfV-2 on the Hub! We were excited about this work, so I added it as part of the models on Hugging Science (huggingscience.co).
Your model will also be featured in the rolling banner on the first page. Feel free to communicate about it , we're always happy to amplify on Twitter or LinkedIn.
Also feel free to let us know when you release new models, datasets, or blogs. We'd be happy to add some to Hugging Science.
(If you have any questions or just want to chat more about this, you can find me on Twitter, LinkedIn, or emma.scharfmann @ huggingface.co)
Cheers,
Emma