Image recognition on CIFAR 10, CIFAR 100, Caltech 101 and Caltech 256 datasets. With the implementation of WideResNet, InceptionV3 and DenseNet neural networks.
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Updated
Jun 17, 2021 - Python
Image recognition on CIFAR 10, CIFAR 100, Caltech 101 and Caltech 256 datasets. With the implementation of WideResNet, InceptionV3 and DenseNet neural networks.
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"
Code for paper: "Improved Residual Network Based on Norm-Preservation for Visual Recognition" https://doi.org/10.1016/j.neunet.2022.10.023
WideResNet implementation on MNIST dataset. FGSM and PGD adversarial attacks on standard training, PGD adversarial training, and Feature Scattering adversarial training.
vanilla training and adversarial training in PyTorch
SE-Net Incorporates with ResNet and WideResnet on CIFAR-10/100 Dataset.
CIFAR10, CIFAR100 results with VGG16,Resnet50,WideResnet using pytorch-lightning
PyTorch implementation for 3D CNN models for medical image data (1 channel gray scale images).
Wide Residual Networks (WideResNets) in PyTorch
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)
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