Skip to content

csinva/gan-vae-pretrained-pytorch

master
Switch branches/tags

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 

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. Trained for 100 epochs. Weights here.

data samples dcgan samples vae samples
real_images fake_images-300 fake_images-300

For comparison with a less complicated architecture, I've also included a pre-trained non-convolutional GAN in the mnist_gan_mlp folder, based on code from this repo (trained for 300 epochs).

I've also included a pre-trained LeNet classifier which achieves 99% test accuracy in the mnist_classifier folder, based on this repo.

cifar10

The cifar10 gan is from the pytorch examples repo and implements the DCGAN paper. It required only minor alterations to generate images the size of the cifar10 dataset (32x32x3). Trained for 200 epochs. Weights here.

data samples generated samples
real_images fake_images-300

I've also linked to a pre-trained cifar10 classifier in the cifar10_classifier folder from this repo.

cifar100

Similiar to the above gans, the cifar100 gan here generates 32x32x1 images for generating grayscale images. Trained for 200 epochs. Weights here. There are also weights/code for generating images which are 34x45x1.

data samples generated samples
real_images fake_images-300

reference