| | --- |
| | {} |
| | --- |
| | # 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 |
| |
|
| | Refer to `model_results.csv` for model versions and their metrics. |
| |
|
| | ### HuggingFace Hub |
| |
|
| | In order to pull the model from HuggingFace, you need to be logged in: |
| |
|
| | ```Bash |
| | huggingface-cli login |
| | ``` |
| |
|
| | Then you can pull the model from a Python script: |
| |
|
| | ```Python |
| | from transformers import AutoModel |
| | model = AutoModel.from_pretrained("nvidia/RADIO", trust_remote_code=True) |
| | ``` |
| |
|
| | Alternatively, you can specify an access token: |
| |
|
| | ```Python |
| | access_token = "<YOUR ACCESS TOKEN" |
| | model = AutoModel.from_pretrained("nvidia/RADIO", trust_remote_code=True, token=access_token) |
| | ``` |
| |
|
| | ### Usage |
| |
|
| | 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. It has shape $(B,T,D)$ with $T$ being the flattened spatial tokens, and $D$ being the channels for spatial features. Note that $C \neq D$ in general. |
| |
|
| | Converting to a spatial tensor format can be done using the downsampling size of the model, combined with the input tensor shape. For 'radio_v1', the patch size is 14. |
| | ```Python |
| | from einops import rearrange |
| | spatial_features = rearrange(spatial_features, 'b (h w) d -> b d h w', h=x.shape[-2] // patch_size, w=x.shape[-1] // patch_size) |
| | ``` |
| | |
| | The resulting tensor will have shape $(B,D,H,W)$, as is typically seen with computer vision models. |
| | |
| | ### RADIOv1 Notes |
| | |
| | We have trained this model to be flexible in input dimension. It supports inputs with both width and height in the range $[14, 1008]$ as long as both axes are divisible by 14. We have found that summarization tokens work best at $H=W=378$ (although the range $[192, 448]$ works well). For spatial tasks, we used $H=W=518$ to perform linear probing for semantic segmentation, and may perform better for more high-resolution tasks. Going up to $1008$, the model may need additional fine tuning at that resolution for best results. |
| | |
| | It is not required that $H=W$ although we have not specifically trained or testing the model in this setting. |
| | |
| | |
| | ## 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} |
| | } |
| | ``` |
