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| # AM-RADIO: Reduce All Domains Into One |
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| Mike Ranzinger, Greg Heinrich, Jan Kautz, Pavlo Molchanov |
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| [NVIDIA Research](https://www.nvidia.com/en-us/research/) |
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| \[[Paper](https://arxiv.org/abs/2312.06709)\]\[[BibTex](#citing-radio)\] |
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| ## Pretrained Models |
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| ### HuggingFace Hub |
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| Pull the E-RADIO model from a Python script: |
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| ```Python |
| from transformers import AutoModel |
| model = AutoModel.from_pretrained("nvidia/E-RADIO", trust_remote_code=True) |
| ``` |
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| ### Usage |
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| E-RADIO will return a tuple with two tensors. |
| The `summary` is similar to the `cls_token` in ViT and is meant to represent the general concept of the entire image. |
| It has shape $(B,C)$ with $B$ being the batch dimension, and $C$ being some number of channels. |
| The `spatial_features` represent more localized content which should be suitable for dense tasks such as semantic segmentation, or for integration into an LLM. |
| Spatial features have shape $(B,H,W,D)$ with $H$ being the height, and $W$ being the width of the spatial features. |
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| ## Training |
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| _Coming Soon_ |
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| ## License |
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| RADIO code and weights are released under the [NSCLv1 License](LICENSE). |
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| ## Citing RADIO |
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| If you find this repository useful, please consider giving a star and citation: |
| ``` |
| @misc{ranzinger2023amradio, |
| title={AM-RADIO: Agglomerative Model -- Reduce All Domains Into One}, |
| author={Mike Ranzinger and Greg Heinrich and Jan Kautz and Pavlo Molchanov}, |
| year={2023}, |
| eprint={2312.06709}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CV} |
| } |
| ``` |
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