This repository provides Keras 2.0+ implementations of Squeeze-and-Excitation (SE) blocks, a channel attention mechanism for convolutional neural networks introduced in the 2017 SE-Net paper. It includes prebuilt SE-enhanced model architectures including SEResNet, SE-InceptionV3, SE-Inception-ResNet-v2, SE-ResNeXt, SE-MobileNets, and SE-DenseNet variants. The SE block applies global average pooling, dense layers, and sigmoid activation to learn channel-wise attention weights that recalibrate feature map responses.