SE-Net Incorporates with ResNet and WideResnet on CIFAR-10/100 Dataset.
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Updated
Sep 11, 2017 - Python
SE-Net Incorporates with ResNet and WideResnet on CIFAR-10/100 Dataset.
WideResNet implementation on MNIST dataset. FGSM and PGD adversarial attacks on standard training, PGD adversarial training, and Feature Scattering adversarial training.
CIFAR10, CIFAR100 results with VGG16,Resnet50,WideResnet using pytorch-lightning
Wide Residual Networks (WideResNets) in PyTorch
Image recognition on CIFAR 10, CIFAR 100, Caltech 101 and Caltech 256 datasets. With the implementation of WideResNet, InceptionV3 and DenseNet neural networks.
vanilla training and adversarial training in PyTorch
Training a Wide Residual Network on the CIFAR - 10 dataset with a limit of 5 million on the number of trainable parameters.
PyTorch implementation for 3D CNN models for medical image data (1 channel gray scale images).
Code for paper: "Improved Residual Network Based on Norm-Preservation for Visual Recognition" https://doi.org/10.1016/j.neunet.2022.10.023
PyTorch implementation of deep CNNs
An implementation of WideResNets with Fixup initialization in Jax/Flax. This can be useful for use cases where Batch Normalization should be avoided (for example when using the Laplace approximation to the Bayesian posterior).
CIFAR10 PyTorch implementation of "MixMatch - A Holistic Approach to Semi-Supervised Learning"
Image Texture Segmentation
the CIFAR10 dataset
Practice on cifar100(ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet, NasNet, Residual Attention Network, SENet, WideResNet)
This project uses PyTorch to classify bone fractures. As well as fine-tuning some famous CNN architectures (like VGG 19, MobileNetV3, RegNet,...), we designed our own architecture. Additionally, we used Transformer architectures (such as Vision Transformer and Swin Transformer). This dataset is Bone Fracture Multi-Region X-ray, available on Kaggle.
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