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⭐⭐⭐ Pytorch implementation of Attentiom, Backbone, ViT, MLP, Re-parameter, Convolution, very flexible module combination.

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Flexible-Conv-Module

Python OnnxRuntime Markdown Visual Studio Code Linux Windows

The Flexible-Conv-Module library is a flexible toolbox of a series of CV algorithms based on PyTorch. Used to combine different modules to build different networks.

➤ Contents

  1. Requirements

  2. Main Components

➤ Requirements🚀

  • Install :

    The requirements.txt file should list all Python libraries that your notebooks depend on, and they will be installed using:

    pip install -r requirements.txt
    

➤ Main Components🚀

Conv Module Components
Basic Block ReParameter ReceptionField Backbone
  • CondConv
  • DoConv
  • DynamicConv
  • PConv
  • PsConv
  • PyConv
  • BsConv
  • DwsConv
  • GhostConv
  • GnConv
  • MixConv
  • ScConv
  • AC Block
  • DiverseBranch Block
  • Mobileone Block
  • RepLK Block
  • RepMLP Block
  • RepVGG Block
  • SPP Module
  • SPPF Module
  • ASPP Module
  • SPPCSPC Module
  • SPPFCSPC Module
  • RFB Module
  • BottleNeck Block
  • Resnet Block
  • Resnext Block
  • VarGroup Block
  • VoV Block
  • HGnet Block
  • Pelee Block
  • Mobilenext Block
  • EPSA Block
  • CGNet Block
Transformer Module Componens
Attention ViT
  • CBAModule
  • coordAttModule
  • EAModule
  • ECAModule
  • GAModule
  • LKAModule
  • SEModule
  • shuffleAttModule
  • simAModule
  • SKModule
  • ULSAModule
  • PSAModule
  • ConvNext Block
  • EdgeViT Block
  • Hor Block
  • MobileVit Block
  • PVTv1 Block
  • PVTv2 Block
  • TnT Block
  • WaveMLP Block
  • STViT Block
👉 Conv Basic Series
👉 Conv Block Series
👉 Conv ReceptionField Series
👉 Conv Backbone Series
👉 ReParameter Series
👉 Vision Transformer Series
👉 Attention Series

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