PyTorch implementation of Differentiable Patch Selection

Implementation of DPS Differentiable patch selection for image recognition applied to traffic sign recognition.

Usage

Run main.py to train a model. All configs can be set in this file as well.

Further notes

This implementation was used as baseline in Iterative Patch Selection for High-Resolution Image Recognition (Repo: https://github.com/benbergner/ips) and differs slightly from the original paper. In particular, a simplified pre-trained ResNet is used as scorer network, and a pre-trained ResNet-18 is employed as feature network. For patch aggregation, a cross-attention based transformer module is used.

More details about the experimental setup and hyperparameter settings can be found in the paper and appendix.

References

@inproceedings{cordonnier2021differentiable,
  title={Differentiable patch selection for image recognition},
  author={Cordonnier, Jean-Baptiste and Mahendran, Aravindh and Dosovitskiy, Alexey and Weissenborn, Dirk and Uszkoreit, Jakob and Unterthiner, Thomas},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={2351--2360},
  year={2021}
}

@article{bergner2022iterative,
  title={Iterative Patch Selection for High-Resolution Image Recognition},
  author={Bergner, Benjamin and Lippert, Christoph and Mahendran, Aravindh},
  journal={arXiv preprint arXiv:2210.13007},
  year={2022}
}