What is this?
A PyTorch collection of Attention, Backbone, MLP, Re-parameter, and Convolution implementations from research papers, meant to help you understand papers by seeing their mechanisms coded up.
A PyTorch collection of Attention, Backbone, MLP, Re-parameter, and Convolution implementations from research papers, meant to help you understand papers by seeing their mechanisms coded up.Is this a library I import, or just reference code?Both: it is pip-installable as a package (fightingcv-attention) and also works as a repo of standalone modules you copy into your own model code.
Both: it is pip-installable as a package (fightingcv-attention) and also works as a repo of standalone modules you copy into your own model code.How do I start using a module from it?Install via pip or clone the repo, then import the specific attention/backbone/MLP class you need (e.g. External Attention, CBAM, SE) and call it like any nn.Module.
Install via pip or clone the repo, then import the specific attention/backbone/MLP class you need (e.g. External Attention, CBAM, SE) and call it like any nn.Module.Do I need PyTorch to use this?Yes, every module is implemented as a PyTorch nn.Module, so you need PyTorch installed in your environment.
Yes, every module is implemented as a PyTorch nn.Module, so you need PyTorch installed in your environment.Is every module equally production-ready?Not stated explicitly, but the repo is organized purely as paper reimplementations for learning/reference, not a trained model zoo or benchmark suite — there are no pretrained weights or training scripts mentioned.
Not stated explicitly, but the repo is organized purely as paper reimplementations for learning/reference, not a trained model zoo or benchmark suite — there are no pretrained weights or training scripts mentioned.How many mechanisms are included?Dozens across four series: 37 Attention modules (e.g. External, Self, CBAM, SE, ECA, Shuffle), 25 Backbones (ResNet, PVT, DeiT, VOLO...), 6 MLP variants, 3 Re-parameter methods, and 5 Convolution variants.
Dozens across four series: 37 Attention modules (e.g. External, Self, CBAM, SE, ECA, Shuffle), 25 Backbones (ResNet, PVT, DeiT, VOLO...), 6 MLP variants, 3 Re-parameter methods, and 5 Convolution variants.What is CBAM in this repo?CBAM (Convolutional Block Attention Module) is one of the included attention implementations, reproducing the ECCV2018 paper that combines channel and spatial attention.
CBAM (Convolutional Block Attention Module) is one of the included attention implementations, reproducing the ECCV2018 paper that combines channel and spatial attention.Can I use a module directly in my own model?Yes, each module is a self-contained PyTorch class with usage code shown in the README, so you can drop it straight into your own network architecture.
Yes, each module is a self-contained PyTorch class with usage code shown in the README, so you can drop it straight into your own network architecture.Is there a Chinese version of the docs?Yes, the README is available in both Simplified Chinese and English.
Yes, the README is available in both Simplified Chinese and English.What are similar projects?deep-learning-for-image-processing, nnUNet_att_position_correction, Attention-Residuals, DeepLearning, vit-pytorch, llama3-from-scratch, Deep-Learning-Papers-Reading-Roadmap, VisionHOPE
deep-learning-for-image-processing, nnUNet_att_position_correction, Attention-Residuals, DeepLearning, vit-pytorch, llama3-from-scratch, Deep-Learning-Papers-Reading-Roadmap, VisionHOPEWhich topics is it in?convolutional-neural-networks, attention, image-classification, attention-mechanism, paper, academic, neural-network, transformer, computer-vision, pytorch, deep-learning, research
convolutional-neural-networks, attention, image-classification, attention-mechanism, paper, academic, neural-network, transformer, computer-vision, pytorch, deep-learning, research