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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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\[[AM-RADIO Paper](https://arxiv.org/abs/2312.06709)\] |
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\[[PHI-S Paper](https://arxiv.org/abs/2410.01680)\] |
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\[[BibTex](#citing-radio)\]\[[GitHub examples](https://github.com/NVlabs/RADIO)\] |
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\[[Tech report on v2.5](https://github.com/NVlabs/RADIO/blob/main/RADIOv2.5_tech_report.md)\] |
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### HuggingFace Hub |
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You can pull the model from a Python script: |
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```Python |
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import torch |
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from PIL import Image |
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from transformers import AutoModel, CLIPImageProcessor |
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hf_repo = "nvidia/RADIO-B" |
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image_processor = CLIPImageProcessor.from_pretrained(hf_repo) |
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model = AutoModel.from_pretrained(hf_repo, trust_remote_code=True) |
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model.eval().cuda() |
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image = Image.open('./assets/radio.png').convert('RGB') |
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pixel_values = image_processor(images=image, return_tensors='pt', do_resize=True).pixel_values |
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pixel_values = pixel_values.cuda() |
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summary, features = model(pixel_values) |
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``` |
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### Usage |
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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. |
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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. |
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```Python |
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from einops import rearrange |
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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) |
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``` |
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The resulting tensor will have shape $(B,D,H,W)$, as is typically seen with computer vision models. |
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### RADIOv2.5 Notes |
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See the [RADIOv2.5 technical report](https://github.com/NVlabs/RADIO/blob/main/RADIOv2.5_tech_report.md). |
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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: |
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``` |
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@InProceedings{Ranzinger_2024_CVPR, |
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author = {Ranzinger, Mike and Heinrich, Greg and Kautz, Jan and Molchanov, Pavlo}, |
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title = {AM-RADIO: Agglomerative Vision Foundation Model Reduce All Domains Into One}, |
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booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, |
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month = {June}, |
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year = {2024}, |
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pages = {12490-12500} |
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} |
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``` |
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``` |
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@misc{ranzinger2024phisdistributionbalancinglabelfree, |
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title={PHI-S: Distribution Balancing for Label-Free Multi-Teacher Distillation}, |
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author={Mike Ranzinger and Jon Barker and Greg Heinrich and Pavlo Molchanov and Bryan Catanzaro and Andrew Tao}, |
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year={2024}, |
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eprint={2410.01680}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.LG}, |
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url={https://arxiv.org/abs/2410.01680}, |
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} |
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``` |
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