Instructions to use nvidia/RADIO-B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/RADIO-B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="nvidia/RADIO-B", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nvidia/RADIO-B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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If you find this repository useful, please consider giving a star and citation:
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```
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If you find this repository useful, please consider giving a star and citation:
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@InProceedings{Ranzinger_2024_CVPR,
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author = {Ranzinger, Mike and Heinrich, Greg and Kautz, Jan and Molchanov, Pavlo},
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title = {AM-RADIO: Agglomerative Vision Foundation Model Reduce All Domains Into One},
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booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
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month = {June},
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year = {2024},
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pages = {12490-12500}
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}
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```
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