This repository provides PyTorch implementations of popular image classification models, including LeNet, VGG, and AlexNet. It also includes integration with TensorBoard for visualizing training and evaluation metrics. The models as as close to their paper equivilants.
The LeNet model is a classic convolutional neural network (CNN) architecture for image classification. It consists of a series of convolutional and pooling layers, followed by fully connected layers.
The VGG model is a deep CNN architecture that achieved top results in the ImageNet competition. It is known for its simplicity and uniform structure, with a series of convolutional layers and max-pooling layers.
AlexNet is a pioneering deep CNN architecture that won the ImageNet competition in 2012. It introduced the use of rectified linear units (ReLU) and dropout regularization, revolutionizing the field of deep learning.