Classification models trained on ImageNet. Keras.

PyTorch Implementation of 2D and 3D 'squeeze and excitation' blocks for Fully Convolutional Neural Networks

Pytorch implementation of network design paradigm described in the paper "Designing Network Design Spaces"

Official code for ResUNetplusplus for medical image segmentation (TensorFlow & Pytorch implementation)

PyTorch implementation of 'Squeeze and Excite' Guided Few Shot Segmentation of Volumetric Scans

This is a SE_DenseNet which contains a senet (Squeeze-and-Excitation Networks by Jie Hu, Li Shen, and Gang Sun) module, written in Pytorch, train, and eval codes have been released.

A convolution neural network with SE block and haar wavelet block for Chinese calligraphy styles classification by TensorFlow.(Paper: A novel CNN structure for fine-grained classification of Chinesecalligraphy styles)

Gluon implementation of channel-attention modules: SE, ECA, GCT

Official implementation of NanoNet: Real-time medical Image segmentation architecture (IEEE CBMS)

Official Pytorch implementation of the paper "Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification" (NeurIPS 2022)

A collection of deep learning models (PyTorch implemtation)

A squeeze-and-excitation enabled ResNet for image classification

Squeeze and Excitation network implementation.

Implementation of different attention mechanisms in TensorFlow and PyTorch.

A module for creating 3D ResNets with different depths and additional features.

I am aiming to write different Semantic Segmentation models from scratch with different pretrained backbones.

Different convolutional neural network implementations for predicting the lenght of the house numbers in the SVHN image dataset. First part of the Humanware project in ift6759-avanced projects in ML.

PyTorch implementation of LS-CNN: Characterizing Local Patches at Multiple Scales for Face Recognition

GAiA is a UCI chess engine built with C++ 17, ONNX and Pytorch. It performs an in-depth analysis and uses a complex squeeze-and-excitation residual network to evaluate each chess board.

Implementation of various channel-wise attention modules