Image Feature Extraction
timm
PyTorch
pathology
histology
medical imaging
self-supervised learning
vision transformer
foundation model
Instructions to use bioptimus/H-optimus-0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use bioptimus/H-optimus-0 with timm:
import timm model = timm.create_model("hf_hub:bioptimus/H-optimus-0", pretrained=True) - Notebooks
- Google Colab
- Kaggle
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- This model and associated code are released under the Apache-2.0 license.
- This model is provided “as-is” without warranties of any kind, express or implied. This model has not been reviewed, certified, or approved by any regulatory body, including but not limited to the FDA (U.S.), EMA (Europe), MHRA (UK), or other medical device authorities. Any application of this model in healthcare or biomedical settings must comply with relevant regulatory requirements and undergo independent validation. Users assume full responsibility for how they use this model and any resulting consequences. The authors, contributors, and distributors disclaim any liability for damages, direct or indirect, resulting from model use. Users are responsible for ensuring compliance with data protection regulations (e.g., GDPR, HIPAA) when using it in research that involves patient data.
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