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Pytorch implementation of pix2pix for various datasets.

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pytorch-pix2pix

Pytorch implementation of pix2pix [1] for various datasets.

dataset

  • cityscapes
    • 2,975 training images, 200 train epochs, 1 batch size, inverse order: True
  • facades
    • 400 training images, 200 train epochs, 1 batch size, inverse order: True
  • maps
    • 1,096 training images, 200 train epochs, 1 batch size, inverse order: True
  • edges2shoes
    • 50k training images, 15 train epochs, 4 batch size, inverse order: False
  • edges2handbags
    • 137k training images, 15 train epochs, 4 batch size, inverse order: False

Resutls

cityscapes

  • cityscapes after 200 epochs
    • First column: input, second column: output, third column: ground truth

city_result

  • Generate animation for fixed inputs

cityscapes_gif

  • Learning Time
    • cityscapes pix2pix - Avg. per epoch: 332.08 sec; Total 200 epochs: 66,846.58 sec

facades

  • facades after 200 epochs
    • First column: input, second column: output, third column: ground truth

facades_result

  • Generate animation for fixed inputs

facades_gif

  • Learning Time
    • facades pix2pix - Avg. per epoch: 44.94 sec; Total 200 epochs: 9,282.64 sec

edges2handbags

  • edges2handbags after 15 epochs
    • First column: input, second column: output, third column: ground truth

edges2handbags_result

  • Generate animation for fixed inputs

edges2handbags_gif

  • Learning Time
    • edges2handbags pix2pix - Avg. per epoch: 10,228.08 sec; Total 15 epochs: 153,443.62 sec

Development Environment

  • Ubuntu 14.04 LTS
  • NVIDIA GTX 1080 ti
  • cuda 8.0
  • Python 2.7.6
  • pytorch 0.1.12
  • matplotlib 1.3.1
  • imageio 2.2.0
  • scipy 0.19.1

Reference

[1] Isola, Phillip, et al. "Image-to-image translation with conditional adversarial networks." arXiv preprint arXiv:1611.07004 (2016).

(Full paper: https://arxiv.org/pdf/1611.07004.pdf)