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README.md

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+# AM-RADIO: Reduce All Domains Into One
+
+Mike Ranzinger, Greg Heinrich, Jan Kautz, Pavlo Molchanov
+
+[NVIDIA Research](https://www.nvidia.com/en-us/research/)
+
+\[[Paper](https://arxiv.org/abs/2312.06709)\]\[[BibTex](#citing-radio)\]
+
+## Pretrained Models
+
+
+### HuggingFace Hub
+
+Pull the E-RADIO model from a Python script:
+
+```Python
+from transformers import AutoModel
+model = AutoModel.from_pretrained("nvidia/E-RADIO", trust_remote_code=True)
+```
+
+### Usage
+
+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.
+
+## Training
+
+_Coming Soon_
+
+## License
+
+RADIO code and weights are released under the [NSCLv1 License](LICENSE).
+
+## Citing RADIO
+
+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}
+}
+```