https://github.com/csinva/gan-vae-pretrained-pytorch

Pretrained GANs + VAEs + classifiers for MNIST/CIFAR in pytorch.
https://github.com/csinva/gan-vae-pretrained-pytorch

ai cifar cnn convolutional-neural-networks dcgan deep-learning gan gans generative-adversarial-network generative-adversarial-networks machine-learning ml mnist neural-network pretrained pretrained-models python pytorch pytorch-implementation statistics

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Pretrained GANs + VAEs + classifiers for MNIST/CIFAR in pytorch.

• Host: GitHub
• URL: https://github.com/csinva/gan-vae-pretrained-pytorch
• Owner: csinva
• Created: 2019-02-19T23:44:09.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2025-02-02T23:51:06.000Z (3 months ago)
• Last Synced: 2025-03-28T11:11:06.469Z (26 days ago)
• Topics: ai, cifar, cnn, convolutional-neural-networks, dcgan, deep-learning, gan, gans, generative-adversarial-network, generative-adversarial-networks, machine-learning, ml, mnist, neural-network, pretrained, pretrained-models, python, pytorch, pytorch-implementation, statistics
• Language: Jupyter Notebook
• Homepage:
• Size: 98.4 MB
• Stars: 191
• Watchers: 3
• Forks: 49
• Open Issues: 2
• Metadata Files:
  • Readme: readme.md

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README

        
 Pre-trained GANs, VAEs + classifiers for MNIST / CIFAR10 

 A simple starting point for modeling with GANs/VAEs in pytorch.





  

  

  

  


- includes model class definitions + training scripts

- includes notebooks showing how to load pretrained nets / use them

- tested with pytorch 1.0+

- generates images the same size as the dataset images

### mnist

Generates images the size of the MNIST dataset (28x28), using an architecture based on the [DCGAN paper](http://arxiv.org/abs/1511.06434). Trained for 100 epochs. Weights [here](https://github.com/csinva/pytorch_gan_pretrained/tree/master/mnist_dcgan/weights).

| data samples                                                |  dcgan samples                                       |    vae samples                                      |

| ------------------------------------------------------------ | ------------------------------------------------------------ | ---------------------------------------------------- |

| ![real_images](mnist_dcgan/samples/real_samples.png)  | ![fake_images-300](mnist_dcgan/samples/fake_samples_epoch_099.png) | ![fake_images-300](mnist_vae/samples/sample_25.png) | 

For comparison with a less complicated architecture, I've also included a pre-trained non-convolutional GAN  in the [mnist_gan_mlp](mnist_mlp) folder, based on code from [this repo](https://github.com/BeierZhu/GAN-MNIST-Pytorch/blob/master/main.py) (trained for 300 epochs).

I've also included a pre-trained LeNet classifier which achieves 99% test accuracy in the [mnist_classifier](mnist_classifier) folder, based on [this repo](https://github.com/activatedgeek/LeNet-5).

### cifar10

The cifar10 gan is from the [pytorch examples repo](https://github.com/pytorch/examples/tree/master/dcgan) and implements the [DCGAN paper](http://arxiv.org/abs/1511.06434). It required only minor alterations to generate images the size of the cifar10 dataset (32x32x3). Trained for 200 epochs. Weights [here](https://github.com/csinva/pytorch_gan_pretrained/tree/master/cifar10_dcgan/weights).

| data samples                                            |    generated samples                                        |

| ------------------------------------------------------------ | ------------------------------------------------------ |

| ![real_images](cifar10_dcgan/samples/real_samples.png) | ![fake_images-300](cifar10_dcgan/samples/fake_samples_epoch_199.png)  |

I've also linked to a pre-trained cifar10 classifier in the [cifar10_classifier](cifar10_classifier) folder from [this repo](https://github.com/aaron-xichen/pytorch-playground/tree/master/cifar).

### cifar100

Similiar to the above gans, the cifar100 gan here generates 32x32x1 images for generating grayscale images. Trained for 200 epochs. Weights [here](https://github.com/csinva/pytorch_gan_pretrained/tree/master/cifar100_dcgan_grayscale/weights). There are also weights/code for generating images which are 34x45x1.

| data samples                                             |   generated samples                                        |

| ------------------------------------------------------------ | ------------------------------------------------------ |

| ![real_images](cifar100_dcgan_grayscale/samples/real_samples.png) | ![fake_images-300](cifar100_dcgan_grayscale/samples/fake_samples_epoch_299.png) |

### reference

- based on the official [pytorch examples repo](https://github.com/pytorch/examples/tree/master/dcgan) with modifications to generate the appropriate size

- feel free to use/share this code openly

- for similar projects, see related repos: (e.g. [imodels](https://github.com/csinva/imodels), [neural-network-interpretations](https://github.com/csinva/acd)) or my website ([csinva.io](https://csinva.github.io/))

- tweets [@csinva_](https://twitter.com/csinva_)