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Official PyTorch code for "BAM: Bottleneck Attention Module (BMVC2018)" and "CBAM: Convolutional Block Attention Module (ECCV2018)"
Implementation of a classification framework from the paper Aggregated Residual Transformations for Deep Neural Networks
3.8% and 18.3% on CIFAR-10 and CIFAR-100
The main features of szagoruyko/wide-residual-networks are: Computer Vision, Image Classification Architectures.
Projects with overlapping indexed features include: liuzhuang13/densenet — DenseNet is a computer vision model and convolutional neural network implementation designed for image recognition and… kaiminghe/resnet-1k-layers — Deep Residual Networks with 1K Layers. facebookresearch/resnext — Implementation of a classification framework from the paper Aggregated Residual Transformations for Deep Neural Networks. hujie-frank/senet — Squeeze-and-Excitation Networks. jongchan/attention-module — Official PyTorch code for "BAM: Bottleneck Attention Module (BMVC2018)" and "CBAM: Convolutional Block Attention… pytorch/vision — This project is a comprehensive computer vision library for the PyTorch ecosystem, providing a standardized collection…
