---
license: mit
---

# R2O PyTorch

PyTorch implementation of R2O from ["Refine and Represent: Region-to-Object Representation Learning"](https://arxiv.org/abs/2208.11821) (Gokul et al., 2022).

## Pretrained Weights

We provide R2O ResNet-50 weights pretrained on ImageNet-1K for 300 epochs:

| Format      | Download                                                                                           | Use Case        |
|-------------|----------------------------------------------------------------------------------------------------|-----------------|
| Original    | r2o_resnet50_imagenet300.pth | Direct loading  |
| Torchvision | r2o_resnet50_imagenet300_torchvision.pth | MMSegmentation  |
| Detectron2  | r2o_resnet50_imagenet300_d2.pkl | Detectron2      |

## Usage

See [GitHub](https://github.com/KKallidromitis/r2o) repo for how to use weights.

## Citing this work

```
@misc{gokul2022refine,
  title = {Refine and Represent: Region-to-Object Representation Learning},
  author = {Gokul, Akash and Kallidromitis, Konstantinos and Li, Shufan and Kato, Yusuke and Kozuka, Kazuki and Darrell, Trevor and Reed, Colorado J},
  journal={arXiv preprint arXiv:2208.11821},
  year = {2022}
}
```