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FightingCV 代码库， 包含 Attention,Backbone, MLP, Re-parameter, Convolution

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For 小白（Like Me）： 最近在读论文的时候会发现一个问题，有时候论文核心思想非常简单，核心代码可能也就十几行。但是打开作者release的源码时，却发现提出的模块嵌入到分类、检测、分割等任务框架中，导致代码比较冗余，对于特定任务框架不熟悉的我，很难找到核心代码，导致在论文和网络思想的理解上会有一定困难。

For 进阶者（Like You）： 如果把Conv、FC、RNN这些基本单元看做小的Lego积木，把Transformer、ResNet这些结构看成已经搭好的Lego城堡。那么本项目提供的模块就是一个个具有完整语义信息的Lego组件。让科研工作者们避免反复造轮子，只需思考如何利用这些“Lego组件”，搭建出更多绚烂多彩的作品。

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新增

• 支持通过 pip 方式使用该代码库

使用

安装

直接通过 pip 安装

[object Object],[object Object],[object Object]

或克隆该仓库

[object Object],[object Object],[object Object],[object Object]

演示

使用 pip 方式

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

• pip包 内置模块使用参考: fightingcv-attention 说明文档

使用 git 方式

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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目录

• Attention Series

  • 1. External Attention Usage

  • 2. Self Attention Usage

  • 3. Simplified Self Attention Usage

  • 4. Squeeze-and-Excitation Attention Usage

  • 5. SK Attention Usage

  • 6. CBAM Attention Usage

  • 7. BAM Attention Usage

  • 8. ECA Attention Usage

  • 9. DANet Attention Usage

  • 10. Pyramid Split Attention (PSA) Usage

  • 11. Efficient Multi-Head Self-Attention(EMSA) Usage

  • 12. Shuffle Attention Usage

  • 13. MUSE Attention Usage

  • 14. SGE Attention Usage

  • 15. A2 Attention Usage

  • 16. AFT Attention Usage

  • 17. Outlook Attention Usage

  • 18. ViP Attention Usage

  • 19. CoAtNet Attention Usage

  • 20. HaloNet Attention Usage

  • 21. Polarized Self-Attention Usage

  • 22. CoTAttention Usage

  • 23. Residual Attention Usage

  • 24. S2 Attention Usage

  • 25. GFNet Attention Usage

  • 26. Triplet Attention Usage

  • 27. Coordinate Attention Usage

  • 28. MobileViT Attention Usage

  • 29. ParNet Attention Usage

  • 30. UFO Attention Usage

  • 31. ACmix Attention Usage

  • 32. MobileViTv2 Attention Usage

  • 33. DAT Attention Usage

  • 34. CrossFormer Attention Usage

  • 35. MOATransformer Attention Usage

  • 36. CrissCrossAttention Attention Usage

  • 37. Axial_attention Attention Usage

• Backbone Series

  • 1. ResNet Usage

  • 2. ResNeXt Usage

  • 3. MobileViT Usage

  • 4. ConvMixer Usage

  • 5. ShuffleTransformer Usage

  • 6. ConTNet Usage

  • 7. HATNet Usage

  • 8. CoaT Usage

  • 9. PVT Usage

  • 10. CPVT Usage

  • 11. PIT Usage

  • 12. CrossViT Usage

  • 13. TnT Usage

  • 14. DViT Usage

  • 15. CeiT Usage

  • 16. ConViT Usage

  • 17. CaiT Usage

  • 18. PatchConvnet Usage

  • 19. DeiT Usage

  • 20. LeViT Usage

  • 21. VOLO Usage

  • 22. Container Usage

  • 23. CMT Usage

  • 24. EfficientFormer Usage

  • 25. ConvNeXtV2 Usage

• MLP Series

  • 1. RepMLP Usage

  • 2. MLP-Mixer Usage

  • 3. ResMLP Usage

  • 4. gMLP Usage

  • 5. sMLP Usage

  • 6. vip-mlp Usage

• Re-Parameter(ReP) Series

  • 1. RepVGG Usage

  • 2. ACNet Usage

  • 3. Diverse Branch Block(DDB) Usage

• Convolution Series

  • 1. Depthwise Separable Convolution Usage

  • 2. MBConv Usage

  • 3. Involution Usage

  • 4. DynamicConv Usage

  • 5. CondConv Usage

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Attention Series

• Pytorch implementation of "Beyond Self-attention: External Attention using Two Linear Layers for Visual Tasks---arXiv 2021.05.05"

• Pytorch implementation of "Attention Is All You Need---NIPS2017"

• Pytorch implementation of "Squeeze-and-Excitation Networks---CVPR2018"

• Pytorch implementation of "Selective Kernel Networks---CVPR2019"

• Pytorch implementation of "CBAM: Convolutional Block Attention Module---ECCV2018"

• Pytorch implementation of "BAM: Bottleneck Attention Module---BMCV2018"

• Pytorch implementation of "ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks---CVPR2020"

• Pytorch implementation of "Dual Attention Network for Scene Segmentation---CVPR2019"

• Pytorch implementation of "EPSANet: An Efficient Pyramid Split Attention Block on Convolutional Neural Network---arXiv 2021.05.30"

• Pytorch implementation of "ResT: An Efficient Transformer for Visual Recognition---arXiv 2021.05.28"

• Pytorch implementation of "SA-NET: SHUFFLE ATTENTION FOR DEEP CONVOLUTIONAL NEURAL NETWORKS---ICASSP 2021"

• Pytorch implementation of "MUSE: Parallel Multi-Scale Attention for Sequence to Sequence Learning---arXiv 2019.11.17"

• Pytorch implementation of "Spatial Group-wise Enhance: Improving Semantic Feature Learning in Convolutional Networks---arXiv 2019.05.23"

• Pytorch implementation of "A2-Nets: Double Attention Networks---NIPS2018"

• Pytorch implementation of "An Attention Free Transformer---ICLR2021 (Apple New Work)"

• Pytorch implementation of VOLO: Vision Outlooker for Visual Recognition---arXiv 2021.06.24" 【论文解析】

• Pytorch implementation of Vision Permutator: A Permutable MLP-Like Architecture for Visual Recognition---arXiv 2021.06.23 【论文解析】

• Pytorch implementation of CoAtNet: Marrying Convolution and Attention for All Data Sizes---arXiv 2021.06.09 【论文解析】

• Pytorch implementation of Scaling Local Self-Attention for Parameter Efficient Visual Backbones---CVPR2021 Oral 【论文解析】

• Pytorch implementation of Polarized Self-Attention: Towards High-quality Pixel-wise Regression---arXiv 2021.07.02 【论文解析】

• Pytorch implementation of [Contextual