HyperGAN
  • About
  • Getting started
  • CLI guide
  • Configurations
    • Configurable Parameters
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  • Examples
    • 2D
    • Text
    • Classification
    • Colorizer
    • Next Frame (video)
  • Tutorials
    • Training a GAN
    • Pygame inference
    • Creating an image dataset
    • Searching for hyperparameters
  • Components
    • GAN
      • Aligned GAN
      • Aligned Interpolated GAN
      • Standard GAN
    • Generator
      • Configurable Generator
      • DCGAN Generator
      • Resizable Generator
    • Discriminator
      • DCGAN Discriminator
      • Configurable Discriminator
    • Layers
      • add
      • cat
      • channel_attention
      • ez_norm
      • layer
      • mul
      • multi_head_attention
      • operation
      • pixel_shuffle
      • residual
      • resizable_stack
      • segment_softmax
      • upsample
    • Loss
      • ALI Loss
      • F Divergence Loss
      • Least Squares Loss
      • Logistic Loss
      • QP Loss
      • RAGAN Loss
      • Realness Loss
      • Softmax Loss
      • Standard Loss
      • Wasserstein Loss
    • Latent
      • Uniform Distribution
    • Trainer
      • Alternating Trainer
      • Simultaneous Trainer
      • Balanced Trainer
      • Accumulate Gradient Trainer
    • Optimizer
    • Train Hook
      • Adversarial Norm
      • Weight Constraint
      • Stabilizing Training
      • JARE
      • Learning Rate Dropout
      • Gradient Penalty
      • Rolling Memory
    • Other GAN implementations
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  • Creating a Dataset
  • Downloadable datasets
  • Cleaning up data

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  1. Tutorials

Creating an image dataset

PreviousPygame inferenceNextSearching for hyperparameters

Last updated 4 years ago

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To build a new network you need a dataset. Your data should be structured like:

  [folder]/[directory]/*.png

Creating a Dataset

Datasets in HyperGAN are meant to be simple to create. Just use a folder of images.

 [folder]/*.png

For jpg(pass -f jpg)

Downloadable datasets

  • Loose images of any kind can be used

  • CelebA aligned faces

  • MS Coco

  • ImageNet

  • youtube-dl (see )

Cleaning up data

To convert and resize your data for processing, you can use imagemagick

for i in *.jpg; do convert $i  -resize "300x256" -gravity north   -extent 256x256 -format png -crop 256x256+0+0 +repage $i-256x256.png;done
http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html
http://mscoco.org/
http://image-net.org/
examples/Readme.md