Yangyangii/GAN-Tutorial：使用 PyTorch 简单实现多种 GAN 模型

项目介绍

Yangyangii/GAN-Tutorial 是一个托管在 GitHub 上的开源项目，主要使用 Jupyter Notebook 编写，目前获得 412 个 Star、107 次 Fork。

仓库全名: Yangyangii/GAN-Tutorial

README 摘要

GANs Tutorial Very simple implementation of GANs, DCGANs, CGANs, WGANs, and etc. with PyTorch for various dataset (MNIST, CARS, CelebA). You can run the code at Jupyter Notebook. And actually you can also run these codes by using Google Colab immediately (needed downloading some dataset)! Sometimes ipynb files do not work in Github, please clone and run it in your server. Requirements python 3.6 (Anaconda) pytorch…

完整内容见 GitHub 原始 README 或本页。

常见问题

GAN-Tutorial 用什么语言开发？

主要使用 Jupyter Notebook 编写，目前有 412 个 Star。

如何获取 GAN-Tutorial 的源码？

可以直接克隆仓库：git clone https://github.com/Yangyangii/GAN-Tutorial.git，默认分支为 master；安装与运行步骤见仓库 README。

GAN-Tutorial 最近还在维护吗？

仓库最近一次更新为 2023年2月22日，当前有 3 个未关闭 Issue；Star 数为 412，可作为社区活跃度的参考。

关于

GitHub 原始描述（英文）

Simple Implementation of many GAN models with PyTorch.

began began-pytorch cars-dataset celeba conditional-gan dcgan gan gan-implementations gan-pytorch gan-tutorial infogan infogan-pytorch mnist pytorch pytorch-gan regularization stanford-cars vanilla-gan wgan wgan-gp

412 Stars
107 Forks
3 Watching

描述取自 GitHub 官方 API，保持仓库作者书写的原文。

简要分析

热度分

629

价值分

279

活跃状态

活跃

GitHub

语言占比

Jupyter Notebook

贡献者

Yangyangii

GANs Tutorial

Very simple implementation of GANs, DCGANs, CGANs, WGANs, and etc. with PyTorch for various dataset (MNIST, CARS, CelebA).

You can run the code at Jupyter Notebook. And actually you can also run these codes by using Google Colab immediately (needed downloading some dataset)!

Sometimes ipynb files do not work in Github, please clone and run it in your server.

Requirements

python 3.6 (Anaconda)

pytorch 1.0.0 (updated from 0.4.0. If you want to use the previous version, then find previous commit.)

Implementation List

MNIST

Vanilla GAN

Conditional GANs

DCGAN

Wasserstain GAN

WGAN-gp

infoGAN

CARS (Stanford dataset)

you can download the dataset from https://ai.stanford.edu/~jkrause/cars/car_dataset.html

DCGAN

But I think the dataset is small and needs preprocessing a lot.