tensorflow-pix2pix

Tensorflow implementation of pix2pix [1] for various datasets.

• you can download datasets: https://people.eecs.berkeley.edu/~tinghuiz/projects/pix2pix/datasets/
• you can see more information for network architecture and training details in https://arxiv.org/pdf/1611.07004.pdf

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

maps

• facades after 200 epochs

Input	Output	Ground truth

• Generate animation for fixed inputs
  • First column: input, second column: output, third column: ground truth

• Learning time
  • Avg. per epoch: 54.19 sec; Total 200 epochs: 11,339.61 sec

maps

• maps after 200 epochs

Input	Output	Ground truth

• Generate animation for fixed inputs
  • First column: input, second column: output, third column: ground truth

• Learning time
  • Avg. per epoch: 205.08 sec; Total 200 epochs: 41,622.29 sec

Development Environment

• Windows 7
• GTX1080 ti
• cuda 8.0
• Python 3.5.3
• tensorflow-gpu 1.2.1
• numpy 1.13.1
• matplotlib 2.0.2
• imageio 2.2.0

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)