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
In this project, we proposed a straightforward strategy to engineer Residual networks with fewer than 5M parameters for CIFAR10 dataset
An experimental implementation to verify variation idea to Squeeze-and-Excitation Networks(SENet)
Deep Learning studies.
Application of a self-normalizing network for object segmentation.
Poly-Attention Intel Transfer Segmentation Network for skin lesion segmentation
ChurnNet: Deep Learning Enhanced Customer Churn Prediction in Telecommunication Industry
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.
This repository contains the original implementation of "iResSENet: An Accurate Convolutional Neural Network for Retinal Blood Vessel Segmentation".
KL severity grading using SE-ResNet and SE-DenseNet architectures trained with Cross Entropy loss and Focal Loss. The hyperparameters of focal loss have been fine-tuned as well. Further, Grad-CAM has been implemented for visualization purposes.
Squeeze-and-Excitation Network - implementation in TensorFlow
Cardiac_segmentation based on 3D Convolution Neural Network with SE blocks
Implementation of SE-ResNet models and other SE-Nets
PyTorch Implementation of ResUnet++
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.
The 'Advanced topics in Computer Science' big project by Duc Tran Van, Manh Hoang Duc, Hoang Pham Tuan Nguyen, Thang Pham Duc
Implementation of Squeeze and Excitation Networks (SENet) with MNIST dataset
Music genre classification project as part of the Numerical Analysis for Machine Learning course at Politecnico di Milano, A.Y 2022-2023.
Implementation of various channel-wise attention modules
PyTorch implementation of LS-CNN: Characterizing Local Patches at Multiple Scales for Face Recognition
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