Image Feature Extraction
Transformers
Safetensors
feature-extraction
custom_code
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@@ -15,7 +15,7 @@ C-RADIOv2 models are available in multiple sizes:
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  * Base (90M parameters).
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  * Large (320M parameters).
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  * Huge (653M parameters).
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- * Gigantic (1.8B parameters).
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  C-RADIOv2 was trained for 1M steps (400k more steps than v1), using inverse frequency sampling for data balancing, and [PHI Standardization](https://arxiv.org/abs/2410.01680) for teacher distribution balancing.
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@@ -134,7 +134,7 @@ The resulting tensor will have shape `(B,D,H,W)`, as is typically seen with comp
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  * https://huggingface.co/nvidia/C-RADIOv2-B
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  * https://huggingface.co/nvidia/C-RADIOv2-L
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  * https://huggingface.co/nvidia/C-RADIOv2-H
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- * https://huggingface.co/nvidia/C-RADIOv2-G
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  # Training and Evaluation Datasets
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  * Base (90M parameters).
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  * Large (320M parameters).
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  * Huge (653M parameters).
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+ * Gigantic (1.1B parameters).
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  C-RADIOv2 was trained for 1M steps (400k more steps than v1), using inverse frequency sampling for data balancing, and [PHI Standardization](https://arxiv.org/abs/2410.01680) for teacher distribution balancing.
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  * https://huggingface.co/nvidia/C-RADIOv2-B
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  * https://huggingface.co/nvidia/C-RADIOv2-L
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  * https://huggingface.co/nvidia/C-RADIOv2-H
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+ * https://huggingface.co/nvidia/C-RADIOv2-g
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  # Training and Evaluation Datasets
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