Instructions to use microsoft/rad-dino with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/rad-dino with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="microsoft/rad-dino")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("microsoft/rad-dino") model = AutoModel.from_pretrained("microsoft/rad-dino") - Notebooks
- Google Colab
- Kaggle
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Browse files
README.md
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@@ -136,6 +136,7 @@ We used images from five public, deidentified chest X-ray datasets to train this
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| [NIH-CXR](https://openaccess.thecvf.com/content_cvpr_2017/html/Wang_ChestX-ray8_Hospital-Scale_Chest_CVPR_2017_paper.html) | 112 120 |
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| [PadChest](https://www.sciencedirect.com/science/article/abs/pii/S1361841520301614) | 136 787 |
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| [BRAX](https://www.nature.com/articles/s41597-022-01608-8) | 41 260 |
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Images in the validation and test sets used to train [MAIRA](https://arxiv.org/abs/2311.13668) were excluded from the training set of RAD-DINO.
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The list of image files used for training is available at [`./training_images.csv`](./training_images.csv).
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| [NIH-CXR](https://openaccess.thecvf.com/content_cvpr_2017/html/Wang_ChestX-ray8_Hospital-Scale_Chest_CVPR_2017_paper.html) | 112 120 |
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| 137 |
| [PadChest](https://www.sciencedirect.com/science/article/abs/pii/S1361841520301614) | 136 787 |
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| [BRAX](https://www.nature.com/articles/s41597-022-01608-8) | 41 260 |
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| **TOTAL** | 882 775 |
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Images in the validation and test sets used to train [MAIRA](https://arxiv.org/abs/2311.13668) were excluded from the training set of RAD-DINO.
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The list of image files used for training is available at [`./training_images.csv`](./training_images.csv).
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