52 lines
2.7 KiB
Markdown
52 lines
2.7 KiB
Markdown
# Local Relation Networks V2 (LR-Net V2)
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This branch is an improved implementation of ["Local Relation Networks for Image Recognition (LR-Net)"](https://arxiv.org/pdf/1904.11491.pdf). 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.
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## Install
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```bash
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cd ops/local_relation
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python setup.py build_ext --inplace
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```
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## Citing Local Relation Networks
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```
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@inproceedings{hu2019local,
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title={Local relation networks for image recognition},
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author={Hu, Han and Zhang, Zheng and Xie, Zhenda and Lin, Stephen},
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booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
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pages={3464--3473},
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year={2019}
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}
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```
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```
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@article{liu2021Swin,
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title={Swin Transformer: Hierarchical Vision Transformer using Shifted Windows},
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author={Liu, Ze and Lin, Yutong and Cao, Yue and Hu, Han and Wei, Yixuan and Zhang, Zheng and Lin, Stephen and Guo, Baining},
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journal={arXiv preprint arXiv:2103.14030},
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year={2021}
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}
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```
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## Contributing
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This project welcomes contributions and suggestions. Most contributions require you to agree to a
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Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
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the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
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When you submit a pull request, a CLA bot will automatically determine whether you need to provide
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a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
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provided by the bot. You will only need to do this once across all repos using our CLA.
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This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
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For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
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contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
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## Trademarks
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This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft
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trademarks or logos is subject to and must follow
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[Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general).
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Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
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Any use of third-party trademarks or logos are subject to those third-party's policies.
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