# 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}
}
```

{}
transformers pytorch feature-extraction custom_code arxiv:2312.06709 region:us