An implementation of GoogLeNet Inception V3 by Pytorch. The implementation can be adapted to any size of picture input.

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pytorch implementations of some DL architectures. Includes googlenet, resnet, inceptionV3, densenet, mobilenetV1, mobilenetV2, senet, efficientnetV1, transformer etc.

PyTorch implementations of classic CNN architectures (LeNet, AlexNet, VGG, GoogLeNet, ResNet)

An implement of LeNet, AlexNet, VGG16, GoogLeNet and ResNet with Pytorch

A PyTorch implementation of a GoogLeNet (Inception) architecture from scratch for image classification on the CIFAR-100 dataset.

PyTorch implementation of GoogLeNet Inception v1

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An unofficial implementation of GoogLeNet and Resnet34 in pytorch.

PyTorch implementation of CNN architectures including LeNet, AlexNet, VGG, NiN, GoogLeNet, etc.

PyTorch implementation of classic computer vision papers namely AlexNet, VGG, GoogLeNet/InceptionNet and ZFNet.

PyTorch implementations of AlexNet, GoogLeNet (Inception), and ResNet trained on FashionMNIST with a unified pipeline and accuracy comparison.

Implementations of classic CNN models (AlexNet, GoogLeNet, LeNet, ResNet-18, VGG-16) with PyTorch for image tasks like classification.

This project provides a PyTorch implementation of GoogleNet, also known as Inception v1, based on the original research paper "Going Deeper with Convolutions" by Szegedy et al.

This package is a PyTorch port of the original Spiking Neural Networks with GoogLeNet-Like Inception Module(SpikeGoogle) framework for improved backpropagation based spiking neural networks (SNNs) learning with Inception modules. The original implementation is in C++ with CUDA and CUDNN.