Image Texture Segmentation
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Updated
Jan 25, 2024 - Jupyter Notebook
Image Texture Segmentation
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).
Code for paper: "Improved Residual Network Based on Norm-Preservation for Visual Recognition" https://doi.org/10.1016/j.neunet.2022.10.023
CIFAR10 PyTorch implementation of "MixMatch - A Holistic Approach to Semi-Supervised Learning"
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.
Training a Wide Residual Network on the CIFAR - 10 dataset with a limit of 5 million on the number of trainable parameters.
PyTorch implementation of deep CNNs
Image recognition on CIFAR 10, CIFAR 100, Caltech 101 and Caltech 256 datasets. With the implementation of WideResNet, InceptionV3 and DenseNet neural networks.
the CIFAR10 dataset
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
CIFAR10, CIFAR100 results with VGG16,Resnet50,WideResnet using pytorch-lightning
SE-Net Incorporates with ResNet and WideResnet on CIFAR-10/100 Dataset.
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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