PyTorch Implementation of Mobilenet Variants including support for residual connections, group convolutions and squeeze-excite blocks
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Updated
Jul 4, 2018 - Python
PyTorch Implementation of Mobilenet Variants including support for residual connections, group convolutions and squeeze-excite blocks
A squeeze-and-excitation enabled ResNet for image classification
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.
Squeeze-and-Excitation Network - implementation in TensorFlow
Squeeze and Excitation network implementation.
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)
Cardiac_segmentation based on 3D Convolution Neural Network with SE blocks
PyTorch implementation of 'Squeeze and Excite' Guided Few Shot Segmentation of Volumetric Scans
Implementation of Squeeze and Excitation Networks (SENet) with MNIST dataset
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.
PyTorch Implementation of 2D and 3D 'squeeze and excitation' blocks for Fully Convolutional Neural Networks
PyTorch implementation of LS-CNN: Characterizing Local Patches at Multiple Scales for Face Recognition
Pytorch implementation of network design paradigm described in the paper "Designing Network Design Spaces"
Gluon implementation of channel-attention modules: SE, ECA, GCT
Implementation of various channel-wise attention modules
An experimental implementation to verify variation idea to Squeeze-and-Excitation Networks(SENet)
Implementation of SE-ResNet, SE-ResNeXt and SE-InceptionV3 from scratch and comparison of the results obtained for CIFAR-10, CIFAR-100 and Tiny ImageNet with the original paper.
Implementation of SE-ResNet models and other SE-Nets
A collection of deep learning models (PyTorch implemtation)
Application of a self-normalizing network for object segmentation.
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