Instructions to use nvidia/C-RADIOv2-B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/C-RADIOv2-B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="nvidia/C-RADIOv2-B", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nvidia/C-RADIOv2-B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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## References
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* [Paper](https://arxiv.org/abs/2412.07679)
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## Model Architecture
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## References
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* \[CVPR 2025\] [**RADIOv2.5: Improved Baselines for Agglomerative Vision Foundation Models**](https://arxiv.org/abs/2412.07679)
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* \[CVPR 2024\] [**AM-RADIO: Agglomerative Vision Foundation Model - Reduce All Domains Into One**](https://arxiv.org/abs/2312.06709)
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## Model Architecture
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