This branch is an improved implementation of "Local Relation Networks for Image Recognition (LR-Net)". The original LR-Net utilizes sliding window based self-attention layer to replace the 3x3 convolution layers in a ResNet architecture. This improved implementation applies this layer into a stronger overall architecture based on Tranformers, dubbed as LR-Net V2. We provide cuda kernels for the local relation layers. Training scripts and pre-trained models will be provided in the future.

cd ops/local_relation
python setup.py build_ext --inplace

@inproceedings{hu2019local,
  title={Local relation networks for image recognition},
  author={Hu, Han and Zhang, Zheng and Xie, Zhenda and Lin, Stephen},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={3464--3473},
  year={2019}
}

@article{liu2021Swin,
  title={Swin Transformer: Hierarchical Vision Transformer using Shifted Windows},
  author={Liu, Ze and Lin, Yutong and Cao, Yue and Hu, Han and Wei, Yixuan and Zhang, Zheng and Lin, Stephen and Guo, Baining},
  journal={arXiv preprint arXiv:2103.14030},
  year={2021}
}

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