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